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librarian-bots

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Librarian Bots 57

atmatechai/commonvoice_male_3_nld

atmatechai

--- dataset_info: features: - name: client_id dtype: string - name: text dtype: string - name: gender dtype: string - name: accent dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: string - name: phonemes dtype: string - name: stoi dtype: float64 - name: si-sdr dtype: float64 - name: pesq dtype: float64 - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string - name: sdr_noise dtype: string - name: pesq_speech_quality dtype: string - name: text_description dtype: string splits: - name: train num_bytes: 508005 num_examples: 756 download_size: 169712 dataset_size: 508005 configs: - config_name: default data_files: - split: train path: data/train-* ---

Mitake/cluster5_news

Mitake

--- license: mit ---

aiswariya24/DSA_interview_datasets

aiswariya24

--- dataset_info: features: - name: Question dtype: string - name: Answer dtype: string - name: Correctness dtype: string - name: FollowUp Question struct: - name: Part1 dtype: string - name: Part2 dtype: string splits: - name: train num_bytes: 1272747.038841808 num_examples: 2973 - name: test num_bytes: 545829.9611581921 num_examples: 1275 download_size: 853468 dataset_size: 1818577.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

LeviWarren/vangogh

LeviWarren

--- license: llama2 task_categories: - question-answering - text-generation language: - en - nl - de tags: - art pretty_name: mylittledataset size_categories: - 1K<n<10K ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_390711e7-4c98-4125-b927-d13c67f9c9f4

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_c9884a70-fbaf-4d64-8b9f-6dd15a93e00b

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_578c909c-53cb-45f8-805d-e3284bedfcf1

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_38048450-e255-4735-b7cc-a6a163161a4b

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_9492b135-c68e-4767-92de-ea06cf8829cc

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

vanshp1232/Rakesh5553

vanshp1232

--- size_categories: n<1K tags: - rlfh - argilla - human-feedback --- # Dataset Card for Rakesh5553 This dataset has been created with [Argilla](https://github.com/argilla-io/argilla). As shown in the sections below, this dataset can be loaded into your Argilla server as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets). ## Using this dataset with Argilla To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code: ```python import argilla as rg ds = rg.Dataset.from_hub("vanshp1232/Rakesh5553") ``` This will load the settings and records from the dataset repository and push them to you Argilla server for exploration and annotation. ## Using this dataset with `datasets` To load the records of this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset("vanshp1232/Rakesh5553") ``` This will only load the records of the dataset, but not the Argilla settings. ## Dataset Structure This dataset repo contains: * Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `rg.Dataset.from_hub` and can be loaded independently using the `datasets` library via `load_dataset`. * The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla. * A dataset configuration folder conforming to the Argilla dataset format in `.argilla`. The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**. ### Fields The **fields** are the features or text of a dataset's records. For example, the 'text' column of a text classification dataset of the 'prompt' column of an instruction following dataset. | Field Name | Title | Type | Required | Markdown | | ---------- | ----- | ---- | -------- | -------- | | instruction | User instruction | text | True | True | ### Questions The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking. | Question Name | Title | Type | Required | Description | Values/Labels | | ------------- | ----- | ---- | -------- | ----------- | ------------- | | relevance_score | How Relevant is the conversation based upon expert. Is the conversation highly curated for you or not. Please don't judge accuracy. | rating | True | N/A | [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] | | accuracy_score | How accurate is the conversation based upon persona | rating | True | if | [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] | | clarity_score | How clear is the conversation based upon persona | rating | True | Is the LLM getting confused | [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] | | actionable_score | How actionable is the conversation based upon persona | rating | True | Is the LLM response to actionable for example, it shows comparison card on the right question. | [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] | | engagement_score | How engaging is the conversation based upon persona | rating | True | Are there a lot of question that are being shown if yes, high score else low score | [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] | | completeness_score | is the conversation complete based upon persona | rating | True | is the conversation complete based upon persona, not leaving any key aspect out | [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] | | feedback | feedback | text | True | What do you think can be improved in the given conversation. How good was the conversation as per you? | N/A | <!-- check length of metadata properties --> ### Metadata The **metadata** is a dictionary that can be used to provide additional information about the dataset record. | Metadata Name | Title | Type | Values | Visible for Annotators | | ------------- | ----- | ---- | ------ | ---------------------- | | conv_id | Conversation ID | | - | True | | turn | Conversation Turn | | 0 - 100 | True | ### Data Instances An example of a dataset instance in Argilla looks as follows: ```json { "_server_id": "1568a4cc-1731-4efa-b35b-e1d492269b1f", "fields": { "instruction": "**user**: Hi\n**assistant**: Hello Rakesh! How can I assist you today? If you have any questions about software or need recommendations, feel free to ask.\n**user**: Best tools in graphic designing\n**assistant**: Hey Rakesh! Graphic design tools are essential for creating stunning visuals. Here\u0027s a quick comparison of some top tools:\n\n| **Tool** | **Features** | **Best For** |\n|----------------|----------------------------------------|-------------------------------|\n| **Adobe Photoshop** | Advanced editing, photo manipulation | Professional designers |\n| **Canva** | User-friendly, templates | Beginners, social media posts |\n| **Sketch** | UI/UX design, vector editing | Web and app designers |\n| **CorelDRAW** | Vector illustration, layout | Print and advertising |\n| **Figma** | Collaborative design, prototyping | Team projects, UI/UX |\n\n*Which one are you most interested in exploring further?*\n**user**: Which is tool is better for graphic designing \n**assistant**: Hey Rakesh! Choosing the best graphic design tool really depends on your specific needs and skill level. Here\u2019s a quick overview to help you decide:\n\n| **Tool** | **Strengths** | **Ideal For** |\n|----------------|-----------------------------------------|--------------------------------|\n| **Adobe Photoshop** | Comprehensive photo editing and manipulation | Professionals and photographers |\n| **Canva** | Easy to use with a vast library of templates | Beginners and quick projects |\n| **Sketch** | Excellent for UI/UX design and prototyping | Web and mobile app designers |\n| **CorelDRAW** | Strong vector graphics capabilities | Print design and illustrations |\n| **Figma** | Real-time collaboration and prototyping | Teams working on UI/UX projects |\n\n*What type of graphic design work are you looking to do? This will help narrow down the best tool for you!*\n**user**: give me more\n**assistant**: Sure, Rakesh! Here\u2019s a deeper dive into each of the graphic design tools I mentioned earlier, including their pricing and some unique features:\n\n| **Tool** | **Description** | **Pricing** | **Unique Features** |\n|----------------|-------------------------------------------|-----------------------------------|---------------------------------------------|\n| **Adobe Photoshop** | Industry-standard for photo editing and graphic design. | Starting at \u20b9638.38/month (incl. GST) | Advanced retouching, extensive plugins, and AI features for image enhancement. |\n| **Canva** | A versatile design tool with a drag-and-drop interface. | Free version available; Pro at $12.99/month | Thousands of templates, easy social media integration, and collaborative features. |\n| **Sketch** | Focused on UI/UX design with vector editing capabilities. | Starting at $99/year | Symbols for reusable design elements, plugins for extended functionality. |\n| **CorelDRAW** | Powerful vector graphics software for illustrations and layouts. | Starting at $249/year | Non-destructive editing, advanced typography tools, and color management. |\n| **Figma** | A web-based design tool for collaborative projects. | Free for individual use; Pro at $12/month | Real-time collaboration, design systems, and prototyping capabilities. |\n\n*Do any of these tools resonate with your needs? Let me know if you want to explore a specific one further or if you have any other questions!*\n**feedback**: radar Chart didnot create. \n**user**: give me more tools\n**assistant**: Sure, Rakesh! Here are some additional graphic design tools that you might find useful:\n\n| **Tool** | **Description** | **Pricing** | **Unique Features** |\n|----------------------|-------------------------------------------------|---------------------------------|---------------------------------------------|\n| **Affinity Photo** | A powerful photo editing software for professionals. | One-time purchase of $54.99 | Advanced retouching, HDR merging, and extensive editing tools. |\n| **Inkscape** | Open-source vector graphics editor. | Free | Supports SVG format, great for illustrations and logos. |\n| **Gravit Designer** | A versatile design tool for web and print. | Free version available; Pro at $49/year | Cloud-based, cross-platform, and vector editing capabilities. |\n| **Pencil Project** | Open-source GUI prototyping tool. | Free | Built-in shapes collection for UI design and mockups. |\n| **Adobe Illustrator**| Industry-standard for vector graphics design. | Starting at \u20b9638.38/month (incl. GST) | Advanced vector editing, typography, and integration with other Adobe products. |\n\n*Do any of these tools catch your eye? Let me know if you want more details on a specific one or if you have other questions!*\n**feedback**: Chart has not been created\n**user**: compare then in user ratings\n**assistant**: Here\u0027s a concise analysis of the user ratings for the top graphic design tools based on the number of users who rated them:\n\n1. **Canva** stands out with the highest number of ratings (11,852), showcasing its popularity and user engagement. It has a strong ease of use and features rating, making it ideal for beginners and those looking for quick design solutions.\n\n2. **Figma** follows closely with 733 ratings, indicating a solid user base. It excels in features and ease of use, making it a favorite among UI/UX designers for collaborative projects.\n\n3. **Affinity Photo** has a respectable rating from 87 users, highlighting its value for money and support. It\u0027s a great choice for professionals looking for advanced photo editing capabilities.\n\n4. **CorelDRAW Graphics Suite** has a good rating from 67 users, but lacks detailed metrics in several areas. It is known for its vector graphics capabilities, making it suitable for print design.\n\n5. **Pencil Project** has the least number of ratings (21), but still offers a decent ease of use and value for money. It is more niche, focusing on prototyping and wireframing.\n\nNow, let\u0027s visualize this data in a radar chart:\n\nnull{\"graph_type\": \"radar\", \"x_label\": \"Tools\", \"y_label\": \"Ratings\", \"json_payload\": [{\"category\": \"Ease of Use\", \"data\": [{\"tool_name\": \"Canva\", \"rating\": 4.67}, {\"tool_name\": \"Figma\", \"rating\": 4.46}, {\"tool_name\": \"Affinity Photo\", \"rating\": 4.44}, {\"tool_name\": \"CorelDRAW Graphics Suite\", \"rating\": 0}, {\"tool_name\": \"Pencil Project\", \"rating\": 4.33}]}, {\"category\": \"Feature Rating\", \"data\": [{\"tool_name\": \"Canva\", \"rating\": 4.53}, {\"tool_name\": \"Figma\", \"rating\": 4.63}, {\"tool_name\": \"Affinity Photo\", \"rating\": 4.53}, {\"tool_name\": \"CorelDRAW Graphics Suite\", \"rating\": 0}, {\"tool_name\": \"Pencil Project\", \"rating\": 4.1}]}, {\"category\": \"Rating\", \"data\": [{\"tool_name\": \"Canva\", \"rating\": 4.72}, {\"tool_name\": \"Figma\", \"rating\": 4.73}, {\"tool_name\": \"Affinity Photo\", \"rating\": 4.77}, {\"tool_name\": \"CorelDRAW Graphics Suite\", \"rating\": 4.8}, {\"tool_name\": \"Pencil Project\", \"rating\": 4.33}]}, {\"category\": \"Support Rating\", \"data\": [{\"tool_name\": \"Canva\", \"rating\": 4.39}, {\"tool_name\": \"Figma\", \"rating\": 4.31}, {\"tool_name\": \"Affinity Photo\", \"rating\": 4.58}, {\"tool_name\": \"CorelDRAW Graphics Suite\", \"rating\": 0}, {\"tool_name\": \"Pencil Project\", \"rating\": 3.67}]}, {\"category\": \"Value for Money\", \"data\": [{\"tool_name\": \"Canva\", \"rating\": 4.62}, {\"tool_name\": \"Figma\", \"rating\": 4.62}, {\"tool_name\": \"Affinity Photo\", \"rating\": 4.83}, {\"tool_name\": \"CorelDRAW Graphics Suite\", \"rating\": 0}, {\"tool_name\": \"Pencil Project\", \"rating\": 4.64}]}, {\"category\": \"Likelihood to Recommend\", \"data\": [{\"tool_name\": \"Canva\", \"rating\": 4.58}, {\"tool_name\": \"Figma\", \"rating\": 4.54}, {\"tool_name\": \"Affinity Photo\", \"rating\": 4.58}, {\"tool_name\": \"CorelDRAW Graphics Suite\", \"rating\": 0}, {\"tool_name\": \"Pencil Project\", \"rating\": 4.17}]}]}\n\n### Business Implications:\n- **Canva** and **Figma** are excellent choices for users seeking user-friendly interfaces and strong collaborative features, making them suitable for teams and individuals alike.\n- **Affinity Photo** is ideal for those who require advanced photo editing tools, while **CorelDRAW** may appeal to users focused on vector graphics.\n- **Pencil Project** serves a niche market, appealing to developers and designers working on prototypes.\n\nIf you\u0027re interested in exploring these tools further, consider booking a demo to see how they can fit your specific design needs!\n**user**: create it again\n**assistant**: Absolutely! Let\u0027s dive into the analysis of the top graphic design tools based on user ratings, focusing on their strengths and weaknesses.\n\n### Data-Driven Insights:\nThe analysis reveals that **Canva** and **Figma** are leading the pack in user ratings, showcasing their popularity and user engagement. Canva is particularly noted for its ease of use and extensive features, making it ideal for beginners and quick design tasks. Figma excels in collaborative features, making it a favorite among UI/UX designers. \n\n**Affinity Photo** stands out for its value for money and advanced photo editing capabilities, while **CorelDRAW** is recognized for its vector graphics expertise, although it has a smaller user base. **Pencil Project**, while less popular, serves a niche market focused on prototyping and wireframing.\n\n### Radar Chart Visualization:\nHere\u2019s the radar chart visualizing the ratings for these tools:\n\nnull{\"graph_type\": \"radar\", \"x_label\": \"Tools\", \"y_label\": \"Ratings\", \"json_payload\": [{\"category\": \"Ease of Use\", \"data\": [{\"tool_name\": \"Canva\", \"rating\": 4.67}, {\"tool_name\": \"Figma\", \"rating\": 4.46}, {\"tool_name\": \"Affinity Photo\", \"rating\": 4.44}, {\"tool_name\": \"CorelDRAW\", \"rating\": 4.17}, {\"tool_name\": \"Pencil Project\", \"rating\": 4.33}]}, {\"category\": \"Feature Rating\", \"data\": [{\"tool_name\": \"Canva\", \"rating\": 4.53}, {\"tool_name\": \"Figma\", \"rating\": 4.63}, {\"tool_name\": \"Affinity Photo\", \"rating\": 4.53}, {\"tool_name\": \"CorelDRAW\", \"rating\": 4.45}, {\"tool_name\": \"Pencil Project\", \"rating\": 4.1}]}, {\"category\": \"Rating\", \"data\": [{\"tool_name\": \"Canva\", \"rating\": 4.72}, {\"tool_name\": \"Figma\", \"rating\": 4.73}, {\"tool_name\": \"Affinity Photo\", \"rating\": 4.77}, {\"tool_name\": \"CorelDRAW\", \"rating\": 4.52}, {\"tool_name\": \"Pencil Project\", \"rating\": 4.33}]}, {\"category\": \"Support Rating\", \"data\": [{\"tool_name\": \"Canva\", \"rating\": 4.39}, {\"tool_name\": \"Figma\", \"rating\": 4.31}, {\"tool_name\": \"Affinity Photo\", \"rating\": 4.58}, {\"tool_name\": \"CorelDRAW\", \"rating\": 4.12}, {\"tool_name\": \"Pencil Project\", \"rating\": 3.67}]}, {\"category\": \"Value for Money\", \"data\": [{\"tool_name\": \"Canva\", \"rating\": 4.62}, {\"tool_name\": \"Figma\", \"rating\": 4.62}, {\"tool_name\": \"Affinity Photo\", \"rating\": 4.83}, {\"tool_name\": \"CorelDRAW\", \"rating\": 4.23}, {\"tool_name\": \"Pencil Project\", \"rating\": 4.64}]}, {\"category\": \"Likelihood to Recommend\", \"data\": [{\"tool_name\": \"Canva\", \"rating\": 4.58}, {\"tool_name\": \"Figma\", \"rating\": 4.54}, {\"tool_name\": \"Affinity Photo\", \"rating\": 4.58}, {\"tool_name\": \"CorelDRAW\", \"rating\": 4.21}, {\"tool_name\": \"Pencil Project\", \"rating\": 4.17}]}]}\n\n### Business Implications:\n- **Canva** and **Figma** are excellent choices for users seeking user-friendly interfaces and strong collaborative features, making them suitable for teams and individuals alike.\n- **Affinity Photo** is ideal for those who require advanced photo editing tools, while **CorelDRAW** may appeal to users focused on vector graphics.\n- **Pencil Project** serves a niche market, appealing to developers and designers working on prototypes.\n\nIf you\u0027re interested in exploring these tools further, consider booking a demo to see how they can fit your specific design needs! Let me know if you have any other questions or need more information!\n**user**: Give me their pricing card\n**assistant**: Absolutely, Rakesh! Here\u2019s a comparison card showcasing the pricing and features of the graphic design tools we discussed:\n\nnull{\"graph_type\": \"comparison card\", \"x_label\": \"Tools\", \"y_label\": \"Pricing\", \"json_payload\": [{\"icon\": \"https://gdm-catalog-fmapi-prod.imgix.net/ProductLogo/974e79f0-c83c-4b9f-a75b-e3d3f66500f0.jpeg\", \"tool_name\": \"Canva\", \"tags\": {\"price\": \"12.99\", \"currency\": \"$\", \"wyzard_discount\": 5, \"wyzard_pricing\": \"12.34\", \"subscription_frequency\": \"MONTHLY\", \"plan_type\": \"Pro\"}, \"features\": [{\"description\": \"User-friendly interface\", \"availability\": \"true\"}, {\"description\": \"Extensive template library\", \"availability\": \"true\"}, {\"description\": \"Collaboration features\", \"availability\": \"true\"}], \"more_info\": \"https://www.canva.com/en_in/\"}, {\"icon\": \"https://gdm-catalog-fmapi-prod.imgix.net/ProductLogo/18df3d15-0497-4661-b2e1-bfeba3a21cd2.jpeg\", \"tool_name\": \"Figma\", \"tags\": {\"price\": \"12.00\", \"currency\": \"$\", \"wyzard_discount\": 5, \"wyzard_pricing\": \"11.40\", \"subscription_frequency\": \"MONTHLY\", \"plan_type\": \"Professional\"}, \"features\": [{\"description\": \"Real-time collaboration\", \"availability\": \"true\"}, {\"description\": \"Prototyping capabilities\", \"availability\": \"true\"}, {\"description\": \"Version control\", \"availability\": \"true\"}], \"more_info\": \"https://www.figma.com/\"}, {\"icon\": \"https://gdm-catalog-fmapi-prod.imgix.net/ProductLogo/b4a11aaf-afdc-4d1c-8cab-fe6594b96a6a.png\", \"tool_name\": \"Affinity Photo\", \"tags\": {\"price\": \"54.99\", \"currency\": \"$\", \"wyzard_discount\": 5, \"wyzard_pricing\": \"52.24\", \"subscription_frequency\": \"YEARLY\", \"plan_type\": \"One Time License\"}, \"features\": [{\"description\": \"Advanced photo editing tools\", \"availability\": \"true\"}, {\"description\": \"Non-destructive editing\", \"availability\": \"true\"}, {\"description\": \"Supports various file formats\", \"availability\": \"true\"}], \"more_info\": \"https://affinity.serif.com/en-us/photo/\"}, {\"icon\": \"none\", \"tool_name\": \"CorelDRAW Graphics Suite\", \"tags\": {\"price\": \"249.00\", \"currency\": \"$\", \"wyzard_discount\": 5, \"wyzard_pricing\": \"236.55\", \"subscription_frequency\": \"YEARLY\", \"plan_type\": \"One Time Fee\"}, \"features\": [{\"description\": \"Vector illustration tools\", \"availability\": \"true\"}, {\"description\": \"Page layout capabilities\", \"availability\": \"true\"}, {\"description\": \"Photo editing integration\", \"availability\": \"true\"}], \"more_info\": \"https://www.coreldraw.com/en/\"}, {\"icon\": \"https://gdm-catalog-fmapi-prod.imgix.net/ProductLogo/1738a478-5c20-42e2-a5cc-a37a166f2ea8.png\", \"tool_name\": \"Pencil Project\", \"tags\": {\"price\": \"0.00\", \"currency\": \"$\", \"wyzard_discount\": 5, \"wyzard_pricing\": \"0.00\", \"subscription_frequency\": \"FREE\", \"plan_type\": \"Free\"}, \"features\": [{\"description\": \"Open-source prototyping tool\", \"availability\": \"true\"}, {\"description\": \"Built-in shape collections\", \"availability\": \"true\"}, {\"description\": \"Document export options\", \"availability\": \"true\"}], \"more_info\": \"https://pencil.evolus.vn/\"}]}.\n\n### Business Implications:\n- **Canva** and **Figma** are excellent for users looking for collaborative features and ease of use, making them suitable for teams and quick projects.\n- **Affinity Photo** offers advanced editing capabilities at a one-time cost, ideal for professionals focused on photo editing.\n- **CorelDRAW Graphics Suite** provides robust vector graphics tools, suitable for print design and illustrations.\n- **Pencil Project** is a free option for those focusing on prototyping and wireframing.\n\nIf you\u0027re interested in any of these tools, consider booking a demo to see how they can fit your specific design needs! Let me know if you have any other questions or need more information!\n**user**: Give me their Ratings\n**assistant**: ### Data-Driven Insights:\nThe ratings for the graphic design tools indicate that **Affinity Photo** holds the highest rating among the tools analyzed, showcasing its strong value for money and user satisfaction. **Figma** follows closely, particularly excelling in features and ease of use, making it a favorite for collaborative projects. **Canva** also has a high rating, with a significant user base, indicating its popularity and effectiveness for beginners and quick design tasks. **CorelDRAW** has a decent rating but with a smaller user base, while **Pencil Project** has the lowest rating, reflecting its niche appeal.\n\n### Bubble Chart Visualization:\nnull{\"graph_type\": \"bubble\", \"x_label\": \"Tools\", \"y_label\": \"Ratings\", \"json_payload\": [{\"id\": \"Canva\", \"data\": [{\"y\": 4.72, \"x\": \"Canva\", \"icon\": \"https://gdm-catalog-fmapi-prod.imgix.net/ProductLogo/974e79f0-c83c-4b9f-a75b-e3d3f66500f0.jpeg\", \"size\": 11852}]}, {\"id\": \"Figma\", \"data\": [{\"y\": 4.73, \"x\": \"Figma\", \"icon\": \"https://gdm-catalog-fmapi-prod.imgix.net/ProductLogo/18df3d15-0497-4661-b2e1-bfeba3a21cd2.jpeg\", \"size\": 733}]}, {\"id\": \"Affinity Photo\", \"data\": [{\"y\": 4.77, \"x\": \"Affinity Photo\", \"icon\": \"https://gdm-catalog-fmapi-prod.imgix.net/ProductLogo/b4a11aaf-afdc-4d1c-8cab-fe6594b96a6a.png\", \"size\": 87}]}, {\"id\": \"CorelDRAW\", \"data\": [{\"y\": 4.52, \"x\": \"CorelDRAW\", \"icon\": \"https://gdm-catalog-fmapi-prod.imgix.net/ProductLogo/c777fa4c-f314-481f-a60a-fa59bfb43c42.jpeg\", \"size\": 1494}]}, {\"id\": \"Pencil Project\", \"data\": [{\"y\": 4.33, \"x\": \"Pencil Project\", \"icon\": \"https://gdm-catalog-fmapi-prod.imgix.net/ProductLogo/1738a478-5c20-42e2-a5cc-a37a166f2ea8.png\", \"size\": 21}]}]}\n\n### Business Implications:\n- **Affinity Photo** is ideal for professionals seeking advanced photo editing capabilities, while **Figma** and **Canva** cater well to collaborative and beginner-friendly environments, respectively.\n- **CorelDRAW** is suitable for users focused on vector graphics, although it has a smaller user base.\n- **Pencil Project** serves a niche market, appealing to those focused on prototyping.\n\nIf you\u0027re interested in exploring these tools further, consider booking a demo to see how they can fit your specific design needs! Let me know if you have any other questions or need more information!\n**user**: give me features of each tool\n**assistant**: Sure, Rakesh! Here are the features of each graphic design tool we discussed:\n\n### Features of Graphic Design Tools\n\n| **Tool** | **Key Features** |\n|----------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| **Canva** | - User-friendly drag-and-drop interface\u003cbr\u003e- Extensive library of templates for various projects\u003cbr\u003e- Collaboration features for team projects\u003cbr\u003e- Ability to create social media graphics, presentations, and more\u003cbr\u003e- Free and paid plans available |\n| **Figma** | - Real-time collaboration for teams\u003cbr\u003e- Prototyping capabilities with interactive elements\u003cbr\u003e- Version control to track changes\u003cbr\u003e- Plugin support for extended functionality\u003cbr\u003e- Cloud-based for easy access across devices |\n| **Affinity Photo** | - Advanced photo editing tools\u003cbr\u003e- Non-destructive editing capabilities\u003cbr\u003e- Supports various file formats including PSD\u003cbr\u003e- Extensive retouching and color correction tools\u003cbr\u003e- One-time purchase with no subscription fees |\n| **CorelDRAW** | - Powerful vector illustration tools\u003cbr\u003e- Page layout capabilities for brochures and multi-page documents\u003cbr\u003e- Advanced typography tools\u003cbr\u003e- Color management for print accuracy\u003cbr\u003e- Supports various file formats for import/export |\n| **Pencil Project** | - Open-source GUI prototyping tool\u003cbr\u003e- Built-in shape collections for UI design\u003cbr\u003e- Document export options in multiple formats (PNG, PDF, SVG)\u003cbr\u003e- Inter-page linking for creating interactive prototypes\u003cbr\u003e- Free to use |\n\nIf you have any specific needs or projects in mind, let me know, and I can help you choose the best tool for your requirements!" }, "id": "a5b83ba2-985c-4c70-8c1a-15cfd20707d6", "metadata": { "conv_id": "c67b65f2-fbe1-4027-b1e5-c6e95856a341", "turn": 0 }, "responses": {}, "status": "pending", "suggestions": {}, "vectors": {} } ``` While the same record in HuggingFace `datasets` looks as follows: ```json { "_server_id": "1568a4cc-1731-4efa-b35b-e1d492269b1f", "conv_id": "c67b65f2-fbe1-4027-b1e5-c6e95856a341", "id": "a5b83ba2-985c-4c70-8c1a-15cfd20707d6", "instruction": "**user**: Hi\n**assistant**: Hello Rakesh! How can I assist you today? If you have any questions about software or need recommendations, feel free to ask.\n**user**: Best tools in graphic designing\n**assistant**: Hey Rakesh! Graphic design tools are essential for creating stunning visuals. Here\u0027s a quick comparison of some top tools:\n\n| **Tool** | **Features** | **Best For** |\n|----------------|----------------------------------------|-------------------------------|\n| **Adobe Photoshop** | Advanced editing, photo manipulation | Professional designers |\n| **Canva** | User-friendly, templates | Beginners, social media posts |\n| **Sketch** | UI/UX design, vector editing | Web and app designers |\n| **CorelDRAW** | Vector illustration, layout | Print and advertising |\n| **Figma** | Collaborative design, prototyping | Team projects, UI/UX |\n\n*Which one are you most interested in exploring further?*\n**user**: Which is tool is better for graphic designing \n**assistant**: Hey Rakesh! Choosing the best graphic design tool really depends on your specific needs and skill level. Here\u2019s a quick overview to help you decide:\n\n| **Tool** | **Strengths** | **Ideal For** |\n|----------------|-----------------------------------------|--------------------------------|\n| **Adobe Photoshop** | Comprehensive photo editing and manipulation | Professionals and photographers |\n| **Canva** | Easy to use with a vast library of templates | Beginners and quick projects |\n| **Sketch** | Excellent for UI/UX design and prototyping | Web and mobile app designers |\n| **CorelDRAW** | Strong vector graphics capabilities | Print design and illustrations |\n| **Figma** | Real-time collaboration and prototyping | Teams working on UI/UX projects |\n\n*What type of graphic design work are you looking to do? This will help narrow down the best tool for you!*\n**user**: give me more\n**assistant**: Sure, Rakesh! Here\u2019s a deeper dive into each of the graphic design tools I mentioned earlier, including their pricing and some unique features:\n\n| **Tool** | **Description** | **Pricing** | **Unique Features** |\n|----------------|-------------------------------------------|-----------------------------------|---------------------------------------------|\n| **Adobe Photoshop** | Industry-standard for photo editing and graphic design. | Starting at \u20b9638.38/month (incl. GST) | Advanced retouching, extensive plugins, and AI features for image enhancement. |\n| **Canva** | A versatile design tool with a drag-and-drop interface. | Free version available; Pro at $12.99/month | Thousands of templates, easy social media integration, and collaborative features. |\n| **Sketch** | Focused on UI/UX design with vector editing capabilities. | Starting at $99/year | Symbols for reusable design elements, plugins for extended functionality. |\n| **CorelDRAW** | Powerful vector graphics software for illustrations and layouts. | Starting at $249/year | Non-destructive editing, advanced typography tools, and color management. |\n| **Figma** | A web-based design tool for collaborative projects. | Free for individual use; Pro at $12/month | Real-time collaboration, design systems, and prototyping capabilities. |\n\n*Do any of these tools resonate with your needs? Let me know if you want to explore a specific one further or if you have any other questions!*\n**feedback**: radar Chart didnot create. \n**user**: give me more tools\n**assistant**: Sure, Rakesh! Here are some additional graphic design tools that you might find useful:\n\n| **Tool** | **Description** | **Pricing** | **Unique Features** |\n|----------------------|-------------------------------------------------|---------------------------------|---------------------------------------------|\n| **Affinity Photo** | A powerful photo editing software for professionals. | One-time purchase of $54.99 | Advanced retouching, HDR merging, and extensive editing tools. |\n| **Inkscape** | Open-source vector graphics editor. | Free | Supports SVG format, great for illustrations and logos. |\n| **Gravit Designer** | A versatile design tool for web and print. | Free version available; Pro at $49/year | Cloud-based, cross-platform, and vector editing capabilities. |\n| **Pencil Project** | Open-source GUI prototyping tool. | Free | Built-in shapes collection for UI design and mockups. |\n| **Adobe Illustrator**| Industry-standard for vector graphics design. | Starting at \u20b9638.38/month (incl. GST) | Advanced vector editing, typography, and integration with other Adobe products. |\n\n*Do any of these tools catch your eye? Let me know if you want more details on a specific one or if you have other questions!*\n**feedback**: Chart has not been created\n**user**: compare then in user ratings\n**assistant**: Here\u0027s a concise analysis of the user ratings for the top graphic design tools based on the number of users who rated them:\n\n1. **Canva** stands out with the highest number of ratings (11,852), showcasing its popularity and user engagement. It has a strong ease of use and features rating, making it ideal for beginners and those looking for quick design solutions.\n\n2. **Figma** follows closely with 733 ratings, indicating a solid user base. It excels in features and ease of use, making it a favorite among UI/UX designers for collaborative projects.\n\n3. **Affinity Photo** has a respectable rating from 87 users, highlighting its value for money and support. It\u0027s a great choice for professionals looking for advanced photo editing capabilities.\n\n4. **CorelDRAW Graphics Suite** has a good rating from 67 users, but lacks detailed metrics in several areas. It is known for its vector graphics capabilities, making it suitable for print design.\n\n5. **Pencil Project** has the least number of ratings (21), but still offers a decent ease of use and value for money. It is more niche, focusing on prototyping and wireframing.\n\nNow, let\u0027s visualize this data in a radar chart:\n\nnull{\"graph_type\": \"radar\", \"x_label\": \"Tools\", \"y_label\": \"Ratings\", \"json_payload\": [{\"category\": \"Ease of Use\", \"data\": [{\"tool_name\": \"Canva\", \"rating\": 4.67}, {\"tool_name\": \"Figma\", \"rating\": 4.46}, {\"tool_name\": \"Affinity Photo\", \"rating\": 4.44}, {\"tool_name\": \"CorelDRAW Graphics Suite\", \"rating\": 0}, {\"tool_name\": \"Pencil Project\", \"rating\": 4.33}]}, {\"category\": \"Feature Rating\", \"data\": [{\"tool_name\": \"Canva\", \"rating\": 4.53}, {\"tool_name\": \"Figma\", \"rating\": 4.63}, {\"tool_name\": \"Affinity Photo\", \"rating\": 4.53}, {\"tool_name\": \"CorelDRAW Graphics Suite\", \"rating\": 0}, {\"tool_name\": \"Pencil Project\", \"rating\": 4.1}]}, {\"category\": \"Rating\", \"data\": [{\"tool_name\": \"Canva\", \"rating\": 4.72}, {\"tool_name\": \"Figma\", \"rating\": 4.73}, {\"tool_name\": \"Affinity Photo\", \"rating\": 4.77}, {\"tool_name\": \"CorelDRAW Graphics Suite\", \"rating\": 4.8}, {\"tool_name\": \"Pencil Project\", \"rating\": 4.33}]}, {\"category\": \"Support Rating\", \"data\": [{\"tool_name\": \"Canva\", \"rating\": 4.39}, {\"tool_name\": \"Figma\", \"rating\": 4.31}, {\"tool_name\": \"Affinity Photo\", \"rating\": 4.58}, {\"tool_name\": \"CorelDRAW Graphics Suite\", \"rating\": 0}, {\"tool_name\": \"Pencil Project\", \"rating\": 3.67}]}, {\"category\": \"Value for Money\", \"data\": [{\"tool_name\": \"Canva\", \"rating\": 4.62}, {\"tool_name\": \"Figma\", \"rating\": 4.62}, {\"tool_name\": \"Affinity Photo\", \"rating\": 4.83}, {\"tool_name\": \"CorelDRAW Graphics Suite\", \"rating\": 0}, {\"tool_name\": \"Pencil Project\", \"rating\": 4.64}]}, {\"category\": \"Likelihood to Recommend\", \"data\": [{\"tool_name\": \"Canva\", \"rating\": 4.58}, {\"tool_name\": \"Figma\", \"rating\": 4.54}, {\"tool_name\": \"Affinity Photo\", \"rating\": 4.58}, {\"tool_name\": \"CorelDRAW Graphics Suite\", \"rating\": 0}, {\"tool_name\": \"Pencil Project\", \"rating\": 4.17}]}]}\n\n### Business Implications:\n- **Canva** and **Figma** are excellent choices for users seeking user-friendly interfaces and strong collaborative features, making them suitable for teams and individuals alike.\n- **Affinity Photo** is ideal for those who require advanced photo editing tools, while **CorelDRAW** may appeal to users focused on vector graphics.\n- **Pencil Project** serves a niche market, appealing to developers and designers working on prototypes.\n\nIf you\u0027re interested in exploring these tools further, consider booking a demo to see how they can fit your specific design needs!\n**user**: create it again\n**assistant**: Absolutely! Let\u0027s dive into the analysis of the top graphic design tools based on user ratings, focusing on their strengths and weaknesses.\n\n### Data-Driven Insights:\nThe analysis reveals that **Canva** and **Figma** are leading the pack in user ratings, showcasing their popularity and user engagement. Canva is particularly noted for its ease of use and extensive features, making it ideal for beginners and quick design tasks. Figma excels in collaborative features, making it a favorite among UI/UX designers. \n\n**Affinity Photo** stands out for its value for money and advanced photo editing capabilities, while **CorelDRAW** is recognized for its vector graphics expertise, although it has a smaller user base. **Pencil Project**, while less popular, serves a niche market focused on prototyping and wireframing.\n\n### Radar Chart Visualization:\nHere\u2019s the radar chart visualizing the ratings for these tools:\n\nnull{\"graph_type\": \"radar\", \"x_label\": \"Tools\", \"y_label\": \"Ratings\", \"json_payload\": [{\"category\": \"Ease of Use\", \"data\": [{\"tool_name\": \"Canva\", \"rating\": 4.67}, {\"tool_name\": \"Figma\", \"rating\": 4.46}, {\"tool_name\": \"Affinity Photo\", \"rating\": 4.44}, {\"tool_name\": \"CorelDRAW\", \"rating\": 4.17}, {\"tool_name\": \"Pencil Project\", \"rating\": 4.33}]}, {\"category\": \"Feature Rating\", \"data\": [{\"tool_name\": \"Canva\", \"rating\": 4.53}, {\"tool_name\": \"Figma\", \"rating\": 4.63}, {\"tool_name\": \"Affinity Photo\", \"rating\": 4.53}, {\"tool_name\": \"CorelDRAW\", \"rating\": 4.45}, {\"tool_name\": \"Pencil Project\", \"rating\": 4.1}]}, {\"category\": \"Rating\", \"data\": [{\"tool_name\": \"Canva\", \"rating\": 4.72}, {\"tool_name\": \"Figma\", \"rating\": 4.73}, {\"tool_name\": \"Affinity Photo\", \"rating\": 4.77}, {\"tool_name\": \"CorelDRAW\", \"rating\": 4.52}, {\"tool_name\": \"Pencil Project\", \"rating\": 4.33}]}, {\"category\": \"Support Rating\", \"data\": [{\"tool_name\": \"Canva\", \"rating\": 4.39}, {\"tool_name\": \"Figma\", \"rating\": 4.31}, {\"tool_name\": \"Affinity Photo\", \"rating\": 4.58}, {\"tool_name\": \"CorelDRAW\", \"rating\": 4.12}, {\"tool_name\": \"Pencil Project\", \"rating\": 3.67}]}, {\"category\": \"Value for Money\", \"data\": [{\"tool_name\": \"Canva\", \"rating\": 4.62}, {\"tool_name\": \"Figma\", \"rating\": 4.62}, {\"tool_name\": \"Affinity Photo\", \"rating\": 4.83}, {\"tool_name\": \"CorelDRAW\", \"rating\": 4.23}, {\"tool_name\": \"Pencil Project\", \"rating\": 4.64}]}, {\"category\": \"Likelihood to Recommend\", \"data\": [{\"tool_name\": \"Canva\", \"rating\": 4.58}, {\"tool_name\": \"Figma\", \"rating\": 4.54}, {\"tool_name\": \"Affinity Photo\", \"rating\": 4.58}, {\"tool_name\": \"CorelDRAW\", \"rating\": 4.21}, {\"tool_name\": \"Pencil Project\", \"rating\": 4.17}]}]}\n\n### Business Implications:\n- **Canva** and **Figma** are excellent choices for users seeking user-friendly interfaces and strong collaborative features, making them suitable for teams and individuals alike.\n- **Affinity Photo** is ideal for those who require advanced photo editing tools, while **CorelDRAW** may appeal to users focused on vector graphics.\n- **Pencil Project** serves a niche market, appealing to developers and designers working on prototypes.\n\nIf you\u0027re interested in exploring these tools further, consider booking a demo to see how they can fit your specific design needs! Let me know if you have any other questions or need more information!\n**user**: Give me their pricing card\n**assistant**: Absolutely, Rakesh! Here\u2019s a comparison card showcasing the pricing and features of the graphic design tools we discussed:\n\nnull{\"graph_type\": \"comparison card\", \"x_label\": \"Tools\", \"y_label\": \"Pricing\", \"json_payload\": [{\"icon\": \"https://gdm-catalog-fmapi-prod.imgix.net/ProductLogo/974e79f0-c83c-4b9f-a75b-e3d3f66500f0.jpeg\", \"tool_name\": \"Canva\", \"tags\": {\"price\": \"12.99\", \"currency\": \"$\", \"wyzard_discount\": 5, \"wyzard_pricing\": \"12.34\", \"subscription_frequency\": \"MONTHLY\", \"plan_type\": \"Pro\"}, \"features\": [{\"description\": \"User-friendly interface\", \"availability\": \"true\"}, {\"description\": \"Extensive template library\", \"availability\": \"true\"}, {\"description\": \"Collaboration features\", \"availability\": \"true\"}], \"more_info\": \"https://www.canva.com/en_in/\"}, {\"icon\": \"https://gdm-catalog-fmapi-prod.imgix.net/ProductLogo/18df3d15-0497-4661-b2e1-bfeba3a21cd2.jpeg\", \"tool_name\": \"Figma\", \"tags\": {\"price\": \"12.00\", \"currency\": \"$\", \"wyzard_discount\": 5, \"wyzard_pricing\": \"11.40\", \"subscription_frequency\": \"MONTHLY\", \"plan_type\": \"Professional\"}, \"features\": [{\"description\": \"Real-time collaboration\", \"availability\": \"true\"}, {\"description\": \"Prototyping capabilities\", \"availability\": \"true\"}, {\"description\": \"Version control\", \"availability\": \"true\"}], \"more_info\": \"https://www.figma.com/\"}, {\"icon\": \"https://gdm-catalog-fmapi-prod.imgix.net/ProductLogo/b4a11aaf-afdc-4d1c-8cab-fe6594b96a6a.png\", \"tool_name\": \"Affinity Photo\", \"tags\": {\"price\": \"54.99\", \"currency\": \"$\", \"wyzard_discount\": 5, \"wyzard_pricing\": \"52.24\", \"subscription_frequency\": \"YEARLY\", \"plan_type\": \"One Time License\"}, \"features\": [{\"description\": \"Advanced photo editing tools\", \"availability\": \"true\"}, {\"description\": \"Non-destructive editing\", \"availability\": \"true\"}, {\"description\": \"Supports various file formats\", \"availability\": \"true\"}], \"more_info\": \"https://affinity.serif.com/en-us/photo/\"}, {\"icon\": \"none\", \"tool_name\": \"CorelDRAW Graphics Suite\", \"tags\": {\"price\": \"249.00\", \"currency\": \"$\", \"wyzard_discount\": 5, \"wyzard_pricing\": \"236.55\", \"subscription_frequency\": \"YEARLY\", \"plan_type\": \"One Time Fee\"}, \"features\": [{\"description\": \"Vector illustration tools\", \"availability\": \"true\"}, {\"description\": \"Page layout capabilities\", \"availability\": \"true\"}, {\"description\": \"Photo editing integration\", \"availability\": \"true\"}], \"more_info\": \"https://www.coreldraw.com/en/\"}, {\"icon\": \"https://gdm-catalog-fmapi-prod.imgix.net/ProductLogo/1738a478-5c20-42e2-a5cc-a37a166f2ea8.png\", \"tool_name\": \"Pencil Project\", \"tags\": {\"price\": \"0.00\", \"currency\": \"$\", \"wyzard_discount\": 5, \"wyzard_pricing\": \"0.00\", \"subscription_frequency\": \"FREE\", \"plan_type\": \"Free\"}, \"features\": [{\"description\": \"Open-source prototyping tool\", \"availability\": \"true\"}, {\"description\": \"Built-in shape collections\", \"availability\": \"true\"}, {\"description\": \"Document export options\", \"availability\": \"true\"}], \"more_info\": \"https://pencil.evolus.vn/\"}]}.\n\n### Business Implications:\n- **Canva** and **Figma** are excellent for users looking for collaborative features and ease of use, making them suitable for teams and quick projects.\n- **Affinity Photo** offers advanced editing capabilities at a one-time cost, ideal for professionals focused on photo editing.\n- **CorelDRAW Graphics Suite** provides robust vector graphics tools, suitable for print design and illustrations.\n- **Pencil Project** is a free option for those focusing on prototyping and wireframing.\n\nIf you\u0027re interested in any of these tools, consider booking a demo to see how they can fit your specific design needs! Let me know if you have any other questions or need more information!\n**user**: Give me their Ratings\n**assistant**: ### Data-Driven Insights:\nThe ratings for the graphic design tools indicate that **Affinity Photo** holds the highest rating among the tools analyzed, showcasing its strong value for money and user satisfaction. **Figma** follows closely, particularly excelling in features and ease of use, making it a favorite for collaborative projects. **Canva** also has a high rating, with a significant user base, indicating its popularity and effectiveness for beginners and quick design tasks. **CorelDRAW** has a decent rating but with a smaller user base, while **Pencil Project** has the lowest rating, reflecting its niche appeal.\n\n### Bubble Chart Visualization:\nnull{\"graph_type\": \"bubble\", \"x_label\": \"Tools\", \"y_label\": \"Ratings\", \"json_payload\": [{\"id\": \"Canva\", \"data\": [{\"y\": 4.72, \"x\": \"Canva\", \"icon\": \"https://gdm-catalog-fmapi-prod.imgix.net/ProductLogo/974e79f0-c83c-4b9f-a75b-e3d3f66500f0.jpeg\", \"size\": 11852}]}, {\"id\": \"Figma\", \"data\": [{\"y\": 4.73, \"x\": \"Figma\", \"icon\": \"https://gdm-catalog-fmapi-prod.imgix.net/ProductLogo/18df3d15-0497-4661-b2e1-bfeba3a21cd2.jpeg\", \"size\": 733}]}, {\"id\": \"Affinity Photo\", \"data\": [{\"y\": 4.77, \"x\": \"Affinity Photo\", \"icon\": \"https://gdm-catalog-fmapi-prod.imgix.net/ProductLogo/b4a11aaf-afdc-4d1c-8cab-fe6594b96a6a.png\", \"size\": 87}]}, {\"id\": \"CorelDRAW\", \"data\": [{\"y\": 4.52, \"x\": \"CorelDRAW\", \"icon\": \"https://gdm-catalog-fmapi-prod.imgix.net/ProductLogo/c777fa4c-f314-481f-a60a-fa59bfb43c42.jpeg\", \"size\": 1494}]}, {\"id\": \"Pencil Project\", \"data\": [{\"y\": 4.33, \"x\": \"Pencil Project\", \"icon\": \"https://gdm-catalog-fmapi-prod.imgix.net/ProductLogo/1738a478-5c20-42e2-a5cc-a37a166f2ea8.png\", \"size\": 21}]}]}\n\n### Business Implications:\n- **Affinity Photo** is ideal for professionals seeking advanced photo editing capabilities, while **Figma** and **Canva** cater well to collaborative and beginner-friendly environments, respectively.\n- **CorelDRAW** is suitable for users focused on vector graphics, although it has a smaller user base.\n- **Pencil Project** serves a niche market, appealing to those focused on prototyping.\n\nIf you\u0027re interested in exploring these tools further, consider booking a demo to see how they can fit your specific design needs! Let me know if you have any other questions or need more information!\n**user**: give me features of each tool\n**assistant**: Sure, Rakesh! Here are the features of each graphic design tool we discussed:\n\n### Features of Graphic Design Tools\n\n| **Tool** | **Key Features** |\n|----------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| **Canva** | - User-friendly drag-and-drop interface\u003cbr\u003e- Extensive library of templates for various projects\u003cbr\u003e- Collaboration features for team projects\u003cbr\u003e- Ability to create social media graphics, presentations, and more\u003cbr\u003e- Free and paid plans available |\n| **Figma** | - Real-time collaboration for teams\u003cbr\u003e- Prototyping capabilities with interactive elements\u003cbr\u003e- Version control to track changes\u003cbr\u003e- Plugin support for extended functionality\u003cbr\u003e- Cloud-based for easy access across devices |\n| **Affinity Photo** | - Advanced photo editing tools\u003cbr\u003e- Non-destructive editing capabilities\u003cbr\u003e- Supports various file formats including PSD\u003cbr\u003e- Extensive retouching and color correction tools\u003cbr\u003e- One-time purchase with no subscription fees |\n| **CorelDRAW** | - Powerful vector illustration tools\u003cbr\u003e- Page layout capabilities for brochures and multi-page documents\u003cbr\u003e- Advanced typography tools\u003cbr\u003e- Color management for print accuracy\u003cbr\u003e- Supports various file formats for import/export |\n| **Pencil Project** | - Open-source GUI prototyping tool\u003cbr\u003e- Built-in shape collections for UI design\u003cbr\u003e- Document export options in multiple formats (PNG, PDF, SVG)\u003cbr\u003e- Inter-page linking for creating interactive prototypes\u003cbr\u003e- Free to use |\n\nIf you have any specific needs or projects in mind, let me know, and I can help you choose the best tool for your requirements!", "status": "pending", "turn": 0 } ``` ### Data Splits The dataset contains a single split, which is `train`. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation guidelines Review the user interactions with the chatbot. #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

IlyasMoutawwakil/OpenVINO-Benchmarks

IlyasMoutawwakil

--- license: apache-2.0 ---

wangyubo/hatefulM

wangyubo

--- license: unknown ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_9ca7b7d7-54cc-4f0f-a3d7-5323cb3dce4d

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1454 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_f2ce2eba-f2fe-4fae-8e37-425915796892

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1454 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_6112cd34-5fce-4c13-b0d7-676a2bb953f1

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1454 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_4680180e-3fdb-4823-9c22-e7d9334c27d7

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1454 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_dbc77f7f-3096-45a9-87ed-5c9e8613f4e9

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1454 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_a8139a23-c25f-4bc4-ab16-ecf5e7c5eea9

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_d58377ab-1c1c-4a73-8a34-933e5b46cc43

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_d74754d1-9fc6-4aae-8803-95d8f11771a3

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_a087f349-b4a2-465b-9394-71dbfcafa2fd

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_9b5fc76c-2660-4c38-8577-86dfe9b99ae7

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_8b705105-ba12-420f-9d01-91e628c5a9d3

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_bdb92b4f-3814-46c4-a25e-24b291aeae22

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_8d487bc2-47bd-461f-b7a8-b1ddc67521f5

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_eb58d6b2-282f-4bd6-8da4-100337288155

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_7ee81ab6-4305-425d-b1d3-7c42675b0218

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_556d8b4a-ea9f-4bf7-a1fa-21dac503257e

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_143add39-e304-4541-8699-70598629205d

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_3a3f1eb1-edfa-41ff-b19c-aa1916d8ae65

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_17287d6f-46bb-4027-9feb-66dd191d7e9e

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_4b77bd13-0e97-4b94-98b8-b0b76cf25823

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

yaniokodf/alpaca_zh_demo

yaniokodf

--- license: apache-2.0 ---

OpenScholar/OpenScholar-DataStore-V2

OpenScholar

--- license: odc-by --- This repository consists of two directories: - pes2o-chunks-32-shards: Embeddings (split into 32 shards) for peS2o V2 - pes2o-embs-32-shards: Corresponding text chunks (split into 32 shards) for peS2o V2

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_bb984135-06e3-444c-9b1f-a56e50c0ad37

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_192f91bb-4f43-4c04-a584-27eece2ff77e

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_36fe9989-9ace-49a0-876a-b074b7e22dc5

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_11eb5a74-eebe-4592-b1fb-b899a71790ad

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_2f8106af-14b2-4708-975c-688725cfd911

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

corniclr25/stack-mined-python-v1

corniclr25

--- dataset_info: features: - name: query dtype: string - name: document dtype: string - name: negatives sequence: string - name: metadata struct: - name: objective struct: - name: paired sequence: 'null' - name: self sequence: 'null' - name: triplet sequence: sequence: string splits: - name: train num_bytes: 107750038700 num_examples: 10000000 download_size: 41516082386 dataset_size: 107750038700 configs: - config_name: default data_files: - split: train path: data/train-* ---

corniclr25/stack-mined-javascript-v1

corniclr25

--- dataset_info: features: - name: query dtype: string - name: document dtype: string - name: negatives sequence: string - name: metadata struct: - name: objective struct: - name: paired sequence: 'null' - name: self sequence: 'null' - name: triplet sequence: sequence: string splits: - name: train num_bytes: 29123533349 num_examples: 2334784 download_size: 11806323547 dataset_size: 29123533349 configs: - config_name: default data_files: - split: train path: data/train-* ---

kz-transformers/mmlu-translated-kk

kz-transformers

--- language: - kk license: apache-2.0 task_categories: - multiple-choice pretty_name: mmlu-kk-translated dataset_info: features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: id dtype: int64 splits: - name: test num_bytes: 11819526 num_examples: 14038 - name: validation num_bytes: 1295921 num_examples: 1531 - name: dev num_bytes: 212047 num_examples: 285 download_size: 5565517 dataset_size: 13327494 configs: - config_name: default data_files: - split: test path: data/test-* - split: validation path: data/validation-* - split: dev path: data/dev-* ---

Lakshay1Dagar/marketing_prompts_v3

Lakshay1Dagar

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 12784 num_examples: 19 download_size: 8681 dataset_size: 12784 configs: - config_name: default data_files: - split: train path: data/train-* ---

Vinisf/Vinirp

Vinisf

--- license: openrail ---

enjalot/ls-dadabase

enjalot

--- tags: - latent-scope --- # ls-dadabase This dataset contains the files necessary to view in [latentscope](https://github.com/enjalot/latent-scope). The files in the `latentscope` are used by the app to view. You can also preview the scope TODO Total size of dataset files: 376.7 MB TODO: download script inside latentscope

open-llm-leaderboard/EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-details

open-llm-leaderboard

--- pretty_name: Evaluation run of EpistemeAI/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [EpistemeAI/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto](https://huggingface.co/EpistemeAI/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto)\n\ The dataset is composed of 38 configuration(s), each one corresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can\ \ be found as a specific split in each configuration, the split being named using\ \ the timestamp of the run.The \"train\" split is always pointing to the latest\ \ results.\n\nAn additional configuration \"results\" store all the aggregated results\ \ of the run.\n\nTo load the details from a run, you can for instance do the following:\n\ ```python\nfrom datasets import load_dataset\ndata = load_dataset(\n\t\"open-llm-leaderboard/EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-details\"\ ,\n\tname=\"EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_bbh_boolean_expressions\"\ ,\n\tsplit=\"latest\"\n)\n```\n\n## Latest results\n\nThese are the [latest results\ \ from run 2024-11-19T21-26-08.744302](https://huggingface.co/datasets/open-llm-leaderboard/EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-details/blob/main/EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto/results_2024-11-19T21-26-08.744302.json)\ \ (note that there might be results for other tasks in the repos if successive evals\ \ didn't cover the same tasks. You find each in the results and the \"latest\" split\ \ for each eval):\n\n```python\n{\n \"all\": {\n \"leaderboard\": {\n\ \ \"prompt_level_loose_acc,none\": 0.41035120147874304,\n \ \ \"prompt_level_loose_acc_stderr,none\": 0.021167895542791835,\n \"\ inst_level_strict_acc,none\": 0.5203836930455635,\n \"inst_level_strict_acc_stderr,none\"\ : \"N/A\",\n \"acc,none\": 0.3515625,\n \"acc_stderr,none\"\ : 0.00435295793176245,\n \"acc_norm,none\": 0.4471397068361655,\n \ \ \"acc_norm_stderr,none\": 0.0053604900103817995,\n \"inst_level_loose_acc,none\"\ : 0.5551558752997602,\n \"inst_level_loose_acc_stderr,none\": \"N/A\"\ ,\n \"prompt_level_strict_acc,none\": 0.3659889094269871,\n \ \ \"prompt_level_strict_acc_stderr,none\": 0.02072934168450657,\n \"\ exact_match,none\": 0.1336858006042296,\n \"exact_match_stderr,none\"\ : 0.00888347241287297,\n \"alias\": \"leaderboard\"\n },\n \ \ \"leaderboard_bbh\": {\n \"acc_norm,none\": 0.4804721402534282,\n\ \ \"acc_norm_stderr,none\": 0.006196076922023256,\n \"alias\"\ : \" - leaderboard_bbh\"\n },\n \"leaderboard_bbh_boolean_expressions\"\ : {\n \"alias\": \" - leaderboard_bbh_boolean_expressions\",\n \ \ \"acc_norm,none\": 0.816,\n \"acc_norm_stderr,none\": 0.02455581299422255\n\ \ },\n \"leaderboard_bbh_causal_judgement\": {\n \"alias\"\ : \" - leaderboard_bbh_causal_judgement\",\n \"acc_norm,none\": 0.5935828877005348,\n\ \ \"acc_norm_stderr,none\": 0.036013904358574385\n },\n \ \ \"leaderboard_bbh_date_understanding\": {\n \"alias\": \" - leaderboard_bbh_date_understanding\"\ ,\n \"acc_norm,none\": 0.424,\n \"acc_norm_stderr,none\":\ \ 0.03131803437491622\n },\n \"leaderboard_bbh_disambiguation_qa\"\ : {\n \"alias\": \" - leaderboard_bbh_disambiguation_qa\",\n \ \ \"acc_norm,none\": 0.46,\n \"acc_norm_stderr,none\": 0.031584653891499004\n\ \ },\n \"leaderboard_bbh_formal_fallacies\": {\n \"alias\"\ : \" - leaderboard_bbh_formal_fallacies\",\n \"acc_norm,none\": 0.536,\n\ \ \"acc_norm_stderr,none\": 0.031603975145223735\n },\n \ \ \"leaderboard_bbh_geometric_shapes\": {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\"\ ,\n \"acc_norm,none\": 0.328,\n \"acc_norm_stderr,none\":\ \ 0.029752391824475363\n },\n \"leaderboard_bbh_hyperbaton\": {\n\ \ \"alias\": \" - leaderboard_bbh_hyperbaton\",\n \"acc_norm,none\"\ : 0.62,\n \"acc_norm_stderr,none\": 0.030760116042626098\n },\n\ \ \"leaderboard_bbh_logical_deduction_five_objects\": {\n \"alias\"\ : \" - leaderboard_bbh_logical_deduction_five_objects\",\n \"acc_norm,none\"\ : 0.436,\n \"acc_norm_stderr,none\": 0.031425567060281365\n },\n\ \ \"leaderboard_bbh_logical_deduction_seven_objects\": {\n \"\ alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\",\n \"\ acc_norm,none\": 0.408,\n \"acc_norm_stderr,none\": 0.031145209846548512\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n\ \ \"acc_norm,none\": 0.688,\n \"acc_norm_stderr,none\": 0.029361067575219852\n\ \ },\n \"leaderboard_bbh_movie_recommendation\": {\n \"\ alias\": \" - leaderboard_bbh_movie_recommendation\",\n \"acc_norm,none\"\ : 0.612,\n \"acc_norm_stderr,none\": 0.030881038748993974\n },\n\ \ \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.616,\n \"acc_norm_stderr,none\":\ \ 0.030821679117375447\n },\n \"leaderboard_bbh_object_counting\"\ : {\n \"alias\": \" - leaderboard_bbh_object_counting\",\n \ \ \"acc_norm,none\": 0.476,\n \"acc_norm_stderr,none\": 0.03164968895968774\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"\ alias\": \" - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\"\ : 0.4726027397260274,\n \"acc_norm_stderr,none\": 0.04146035831053314\n\ \ },\n \"leaderboard_bbh_reasoning_about_colored_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\",\n\ \ \"acc_norm,none\": 0.552,\n \"acc_norm_stderr,none\": 0.03151438761115348\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \"\ \ - leaderboard_bbh_ruin_names\",\n \"acc_norm,none\": 0.552,\n \ \ \"acc_norm_stderr,none\": 0.03151438761115348\n },\n \"leaderboard_bbh_salient_translation_error_detection\"\ : {\n \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\"\ ,\n \"acc_norm,none\": 0.448,\n \"acc_norm_stderr,none\":\ \ 0.03151438761115349\n },\n \"leaderboard_bbh_snarks\": {\n \ \ \"alias\": \" - leaderboard_bbh_snarks\",\n \"acc_norm,none\"\ : 0.550561797752809,\n \"acc_norm_stderr,none\": 0.037389649660569645\n\ \ },\n \"leaderboard_bbh_sports_understanding\": {\n \"\ alias\": \" - leaderboard_bbh_sports_understanding\",\n \"acc_norm,none\"\ : 0.716,\n \"acc_norm_stderr,none\": 0.028576958730437443\n },\n\ \ \"leaderboard_bbh_temporal_sequences\": {\n \"alias\": \" -\ \ leaderboard_bbh_temporal_sequences\",\n \"acc_norm,none\": 0.164,\n\ \ \"acc_norm_stderr,none\": 0.02346526100207671\n },\n \ \ \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \"\ alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\",\n \ \ \"acc_norm,none\": 0.16,\n \"acc_norm_stderr,none\": 0.023232714782060626\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.14,\n \"acc_norm_stderr,none\": 0.021989409645240245\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.308,\n \"acc_norm_stderr,none\":\ \ 0.02925692860650181\n },\n \"leaderboard_bbh_web_of_lies\": {\n\ \ \"alias\": \" - leaderboard_bbh_web_of_lies\",\n \"acc_norm,none\"\ : 0.5,\n \"acc_norm_stderr,none\": 0.031686212526223896\n },\n\ \ \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.31208053691275167,\n\ \ \"acc_norm_stderr,none\": 0.013434067707656962,\n \"alias\"\ : \" - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n\ \ \"alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\"\ : 0.3333333333333333,\n \"acc_norm_stderr,none\": 0.033586181457325226\n\ \ },\n \"leaderboard_gpqa_extended\": {\n \"alias\": \"\ \ - leaderboard_gpqa_extended\",\n \"acc_norm,none\": 0.31135531135531136,\n\ \ \"acc_norm_stderr,none\": 0.019834784635900788\n },\n \ \ \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.30357142857142855,\n \"acc_norm_stderr,none\"\ : 0.021747782232917543\n },\n \"leaderboard_ifeval\": {\n \ \ \"alias\": \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\"\ : 0.3659889094269871,\n \"prompt_level_strict_acc_stderr,none\": 0.02072934168450657,\n\ \ \"inst_level_strict_acc,none\": 0.5203836930455635,\n \"\ inst_level_strict_acc_stderr,none\": \"N/A\",\n \"prompt_level_loose_acc,none\"\ : 0.41035120147874304,\n \"prompt_level_loose_acc_stderr,none\": 0.021167895542791835,\n\ \ \"inst_level_loose_acc,none\": 0.5551558752997602,\n \"\ inst_level_loose_acc_stderr,none\": \"N/A\"\n },\n \"leaderboard_math_hard\"\ : {\n \"exact_match,none\": 0.1336858006042296,\n \"exact_match_stderr,none\"\ : 0.00888347241287297,\n \"alias\": \" - leaderboard_math_hard\"\n \ \ },\n \"leaderboard_math_algebra_hard\": {\n \"alias\":\ \ \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\": 0.28013029315960913,\n\ \ \"exact_match_stderr,none\": 0.025671206118429726\n },\n \ \ \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\": \"\ \ - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.056910569105691054,\n \"exact_match_stderr,none\": 0.020974566219895126\n\ \ },\n \"leaderboard_math_geometry_hard\": {\n \"alias\"\ : \" - leaderboard_math_geometry_hard\",\n \"exact_match,none\": 0.015151515151515152,\n\ \ \"exact_match_stderr,none\": 0.01067276863717474\n },\n \ \ \"leaderboard_math_intermediate_algebra_hard\": {\n \"alias\": \"\ \ - leaderboard_math_intermediate_algebra_hard\",\n \"exact_match,none\"\ : 0.02857142857142857,\n \"exact_match_stderr,none\": 0.009973998820736053\n\ \ },\n \"leaderboard_math_num_theory_hard\": {\n \"alias\"\ : \" - leaderboard_math_num_theory_hard\",\n \"exact_match,none\": 0.12337662337662338,\n\ \ \"exact_match_stderr,none\": 0.026587484423674337\n },\n \ \ \"leaderboard_math_prealgebra_hard\": {\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\ ,\n \"exact_match,none\": 0.24870466321243523,\n \"exact_match_stderr,none\"\ : 0.03119584087770025\n },\n \"leaderboard_math_precalculus_hard\"\ : {\n \"alias\": \" - leaderboard_math_precalculus_hard\",\n \ \ \"exact_match,none\": 0.05185185185185185,\n \"exact_match_stderr,none\"\ : 0.019154368449050496\n },\n \"leaderboard_mmlu_pro\": {\n \ \ \"alias\": \" - leaderboard_mmlu_pro\",\n \"acc,none\": 0.3515625,\n\ \ \"acc_stderr,none\": 0.00435295793176245\n },\n \"leaderboard_musr\"\ : {\n \"acc_norm,none\": 0.4060846560846561,\n \"acc_norm_stderr,none\"\ : 0.017602041943777794,\n \"alias\": \" - leaderboard_musr\"\n \ \ },\n \"leaderboard_musr_murder_mysteries\": {\n \"alias\":\ \ \" - leaderboard_musr_murder_mysteries\",\n \"acc_norm,none\": 0.532,\n\ \ \"acc_norm_stderr,none\": 0.031621252575725574\n },\n \ \ \"leaderboard_musr_object_placements\": {\n \"alias\": \" - leaderboard_musr_object_placements\"\ ,\n \"acc_norm,none\": 0.3359375,\n \"acc_norm_stderr,none\"\ : 0.029577647634376425\n },\n \"leaderboard_musr_team_allocation\"\ : {\n \"alias\": \" - leaderboard_musr_team_allocation\",\n \ \ \"acc_norm,none\": 0.352,\n \"acc_norm_stderr,none\": 0.030266288057359866\n\ \ }\n },\n \"leaderboard\": {\n \"prompt_level_loose_acc,none\"\ : 0.41035120147874304,\n \"prompt_level_loose_acc_stderr,none\": 0.021167895542791835,\n\ \ \"inst_level_strict_acc,none\": 0.5203836930455635,\n \"inst_level_strict_acc_stderr,none\"\ : \"N/A\",\n \"acc,none\": 0.3515625,\n \"acc_stderr,none\": 0.00435295793176245,\n\ \ \"acc_norm,none\": 0.4471397068361655,\n \"acc_norm_stderr,none\"\ : 0.0053604900103817995,\n \"inst_level_loose_acc,none\": 0.5551558752997602,\n\ \ \"inst_level_loose_acc_stderr,none\": \"N/A\",\n \"prompt_level_strict_acc,none\"\ : 0.3659889094269871,\n \"prompt_level_strict_acc_stderr,none\": 0.02072934168450657,\n\ \ \"exact_match,none\": 0.1336858006042296,\n \"exact_match_stderr,none\"\ : 0.00888347241287297,\n \"alias\": \"leaderboard\"\n },\n \"leaderboard_bbh\"\ : {\n \"acc_norm,none\": 0.4804721402534282,\n \"acc_norm_stderr,none\"\ : 0.006196076922023256,\n \"alias\": \" - leaderboard_bbh\"\n },\n \ \ \"leaderboard_bbh_boolean_expressions\": {\n \"alias\": \" - leaderboard_bbh_boolean_expressions\"\ ,\n \"acc_norm,none\": 0.816,\n \"acc_norm_stderr,none\": 0.02455581299422255\n\ \ },\n \"leaderboard_bbh_causal_judgement\": {\n \"alias\": \" - leaderboard_bbh_causal_judgement\"\ ,\n \"acc_norm,none\": 0.5935828877005348,\n \"acc_norm_stderr,none\"\ : 0.036013904358574385\n },\n \"leaderboard_bbh_date_understanding\": {\n\ \ \"alias\": \" - leaderboard_bbh_date_understanding\",\n \"acc_norm,none\"\ : 0.424,\n \"acc_norm_stderr,none\": 0.03131803437491622\n },\n \"\ leaderboard_bbh_disambiguation_qa\": {\n \"alias\": \" - leaderboard_bbh_disambiguation_qa\"\ ,\n \"acc_norm,none\": 0.46,\n \"acc_norm_stderr,none\": 0.031584653891499004\n\ \ },\n \"leaderboard_bbh_formal_fallacies\": {\n \"alias\": \" - leaderboard_bbh_formal_fallacies\"\ ,\n \"acc_norm,none\": 0.536,\n \"acc_norm_stderr,none\": 0.031603975145223735\n\ \ },\n \"leaderboard_bbh_geometric_shapes\": {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\"\ ,\n \"acc_norm,none\": 0.328,\n \"acc_norm_stderr,none\": 0.029752391824475363\n\ \ },\n \"leaderboard_bbh_hyperbaton\": {\n \"alias\": \" - leaderboard_bbh_hyperbaton\"\ ,\n \"acc_norm,none\": 0.62,\n \"acc_norm_stderr,none\": 0.030760116042626098\n\ \ },\n \"leaderboard_bbh_logical_deduction_five_objects\": {\n \"alias\"\ : \" - leaderboard_bbh_logical_deduction_five_objects\",\n \"acc_norm,none\"\ : 0.436,\n \"acc_norm_stderr,none\": 0.031425567060281365\n },\n \"\ leaderboard_bbh_logical_deduction_seven_objects\": {\n \"alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\"\ ,\n \"acc_norm,none\": 0.408,\n \"acc_norm_stderr,none\": 0.031145209846548512\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \"\ alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n \"acc_norm,none\"\ : 0.688,\n \"acc_norm_stderr,none\": 0.029361067575219852\n },\n \"\ leaderboard_bbh_movie_recommendation\": {\n \"alias\": \" - leaderboard_bbh_movie_recommendation\"\ ,\n \"acc_norm,none\": 0.612,\n \"acc_norm_stderr,none\": 0.030881038748993974\n\ \ },\n \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.616,\n \"acc_norm_stderr,none\": 0.030821679117375447\n\ \ },\n \"leaderboard_bbh_object_counting\": {\n \"alias\": \" - leaderboard_bbh_object_counting\"\ ,\n \"acc_norm,none\": 0.476,\n \"acc_norm_stderr,none\": 0.03164968895968774\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"alias\": \" \ \ - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\": 0.4726027397260274,\n\ \ \"acc_norm_stderr,none\": 0.04146035831053314\n },\n \"leaderboard_bbh_reasoning_about_colored_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\"\ ,\n \"acc_norm,none\": 0.552,\n \"acc_norm_stderr,none\": 0.03151438761115348\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \" - leaderboard_bbh_ruin_names\"\ ,\n \"acc_norm,none\": 0.552,\n \"acc_norm_stderr,none\": 0.03151438761115348\n\ \ },\n \"leaderboard_bbh_salient_translation_error_detection\": {\n \ \ \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\",\n \ \ \"acc_norm,none\": 0.448,\n \"acc_norm_stderr,none\": 0.03151438761115349\n\ \ },\n \"leaderboard_bbh_snarks\": {\n \"alias\": \" - leaderboard_bbh_snarks\"\ ,\n \"acc_norm,none\": 0.550561797752809,\n \"acc_norm_stderr,none\"\ : 0.037389649660569645\n },\n \"leaderboard_bbh_sports_understanding\": {\n\ \ \"alias\": \" - leaderboard_bbh_sports_understanding\",\n \"acc_norm,none\"\ : 0.716,\n \"acc_norm_stderr,none\": 0.028576958730437443\n },\n \"\ leaderboard_bbh_temporal_sequences\": {\n \"alias\": \" - leaderboard_bbh_temporal_sequences\"\ ,\n \"acc_norm,none\": 0.164,\n \"acc_norm_stderr,none\": 0.02346526100207671\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\"\ ,\n \"acc_norm,none\": 0.16,\n \"acc_norm_stderr,none\": 0.023232714782060626\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.14,\n \"acc_norm_stderr,none\": 0.021989409645240245\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.308,\n \"acc_norm_stderr,none\": 0.02925692860650181\n\ \ },\n \"leaderboard_bbh_web_of_lies\": {\n \"alias\": \" - leaderboard_bbh_web_of_lies\"\ ,\n \"acc_norm,none\": 0.5,\n \"acc_norm_stderr,none\": 0.031686212526223896\n\ \ },\n \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.31208053691275167,\n\ \ \"acc_norm_stderr,none\": 0.013434067707656962,\n \"alias\": \"\ \ - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n \"\ alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\": 0.3333333333333333,\n\ \ \"acc_norm_stderr,none\": 0.033586181457325226\n },\n \"leaderboard_gpqa_extended\"\ : {\n \"alias\": \" - leaderboard_gpqa_extended\",\n \"acc_norm,none\"\ : 0.31135531135531136,\n \"acc_norm_stderr,none\": 0.019834784635900788\n\ \ },\n \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.30357142857142855,\n \"acc_norm_stderr,none\"\ : 0.021747782232917543\n },\n \"leaderboard_ifeval\": {\n \"alias\"\ : \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\": 0.3659889094269871,\n\ \ \"prompt_level_strict_acc_stderr,none\": 0.02072934168450657,\n \ \ \"inst_level_strict_acc,none\": 0.5203836930455635,\n \"inst_level_strict_acc_stderr,none\"\ : \"N/A\",\n \"prompt_level_loose_acc,none\": 0.41035120147874304,\n \ \ \"prompt_level_loose_acc_stderr,none\": 0.021167895542791835,\n \"inst_level_loose_acc,none\"\ : 0.5551558752997602,\n \"inst_level_loose_acc_stderr,none\": \"N/A\"\n \ \ },\n \"leaderboard_math_hard\": {\n \"exact_match,none\": 0.1336858006042296,\n\ \ \"exact_match_stderr,none\": 0.00888347241287297,\n \"alias\": \"\ \ - leaderboard_math_hard\"\n },\n \"leaderboard_math_algebra_hard\": {\n\ \ \"alias\": \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\"\ : 0.28013029315960913,\n \"exact_match_stderr,none\": 0.025671206118429726\n\ \ },\n \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\":\ \ \" - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.056910569105691054,\n \"exact_match_stderr,none\": 0.020974566219895126\n\ \ },\n \"leaderboard_math_geometry_hard\": {\n \"alias\": \" - leaderboard_math_geometry_hard\"\ ,\n \"exact_match,none\": 0.015151515151515152,\n \"exact_match_stderr,none\"\ : 0.01067276863717474\n },\n \"leaderboard_math_intermediate_algebra_hard\"\ : {\n \"alias\": \" - leaderboard_math_intermediate_algebra_hard\",\n \ \ \"exact_match,none\": 0.02857142857142857,\n \"exact_match_stderr,none\"\ : 0.009973998820736053\n },\n \"leaderboard_math_num_theory_hard\": {\n \ \ \"alias\": \" - leaderboard_math_num_theory_hard\",\n \"exact_match,none\"\ : 0.12337662337662338,\n \"exact_match_stderr,none\": 0.026587484423674337\n\ \ },\n \"leaderboard_math_prealgebra_hard\": {\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\ ,\n \"exact_match,none\": 0.24870466321243523,\n \"exact_match_stderr,none\"\ : 0.03119584087770025\n },\n \"leaderboard_math_precalculus_hard\": {\n \ \ \"alias\": \" - leaderboard_math_precalculus_hard\",\n \"exact_match,none\"\ : 0.05185185185185185,\n \"exact_match_stderr,none\": 0.019154368449050496\n\ \ },\n \"leaderboard_mmlu_pro\": {\n \"alias\": \" - leaderboard_mmlu_pro\"\ ,\n \"acc,none\": 0.3515625,\n \"acc_stderr,none\": 0.00435295793176245\n\ \ },\n \"leaderboard_musr\": {\n \"acc_norm,none\": 0.4060846560846561,\n\ \ \"acc_norm_stderr,none\": 0.017602041943777794,\n \"alias\": \"\ \ - leaderboard_musr\"\n },\n \"leaderboard_musr_murder_mysteries\": {\n \ \ \"alias\": \" - leaderboard_musr_murder_mysteries\",\n \"acc_norm,none\"\ : 0.532,\n \"acc_norm_stderr,none\": 0.031621252575725574\n },\n \"\ leaderboard_musr_object_placements\": {\n \"alias\": \" - leaderboard_musr_object_placements\"\ ,\n \"acc_norm,none\": 0.3359375,\n \"acc_norm_stderr,none\": 0.029577647634376425\n\ \ },\n \"leaderboard_musr_team_allocation\": {\n \"alias\": \" - leaderboard_musr_team_allocation\"\ ,\n \"acc_norm,none\": 0.352,\n \"acc_norm_stderr,none\": 0.030266288057359866\n\ \ }\n}\n```" repo_url: https://huggingface.co/EpistemeAI/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto leaderboard_url: '' point_of_contact: '' configs: - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_bbh_boolean_expressions data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_bbh_causal_judgement data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_bbh_date_understanding data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_bbh_disambiguation_qa data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_bbh_formal_fallacies data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_bbh_geometric_shapes data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_bbh_hyperbaton data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_bbh_logical_deduction_five_objects data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_bbh_logical_deduction_seven_objects data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_bbh_logical_deduction_three_objects data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_bbh_movie_recommendation data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_bbh_navigate data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_bbh_navigate_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_navigate_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_bbh_object_counting data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_bbh_object_counting_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_object_counting_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_bbh_penguins_in_a_table data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_bbh_reasoning_about_colored_objects data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_bbh_ruin_names data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_bbh_salient_translation_error_detection data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_bbh_snarks data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_bbh_snarks_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_snarks_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_bbh_sports_understanding data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_bbh_temporal_sequences data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_bbh_tracking_shuffled_objects_five_objects data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_bbh_tracking_shuffled_objects_seven_objects data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_bbh_tracking_shuffled_objects_three_objects data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_bbh_web_of_lies data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_gpqa_diamond data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_gpqa_diamond_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_diamond_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_gpqa_extended data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_gpqa_extended_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_extended_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_gpqa_main data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_gpqa_main_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_main_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_ifeval data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_ifeval_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_ifeval_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_math_algebra_hard data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_math_algebra_hard_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_math_algebra_hard_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_math_counting_and_prob_hard data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_math_geometry_hard data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_math_geometry_hard_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_math_geometry_hard_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_math_intermediate_algebra_hard data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_math_num_theory_hard data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_math_prealgebra_hard data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_math_precalculus_hard data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_mmlu_pro data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_mmlu_pro_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_mmlu_pro_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_musr_murder_mysteries data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_musr_object_placements data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_musr_object_placements_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_musr_object_placements_2024-11-19T21-26-08.744302.jsonl' - config_name: EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_musr_team_allocation data_files: - split: 2024_11_19T21_26_08.744302 path: - '**/samples_leaderboard_musr_team_allocation_2024-11-19T21-26-08.744302.jsonl' - split: latest path: - '**/samples_leaderboard_musr_team_allocation_2024-11-19T21-26-08.744302.jsonl' --- # Dataset Card for Evaluation run of EpistemeAI/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [EpistemeAI/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto](https://huggingface.co/EpistemeAI/Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto) The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run. To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset( "open-llm-leaderboard/EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-details", name="EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto__leaderboard_bbh_boolean_expressions", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-11-19T21-26-08.744302](https://huggingface.co/datasets/open-llm-leaderboard/EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto-details/blob/main/EpistemeAI__Fireball-Meta-Llama-3.1-8B-Instruct-0.001-128K-auto/results_2024-11-19T21-26-08.744302.json) (note that there might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "leaderboard": { "prompt_level_loose_acc,none": 0.41035120147874304, "prompt_level_loose_acc_stderr,none": 0.021167895542791835, "inst_level_strict_acc,none": 0.5203836930455635, "inst_level_strict_acc_stderr,none": "N/A", "acc,none": 0.3515625, "acc_stderr,none": 0.00435295793176245, "acc_norm,none": 0.4471397068361655, "acc_norm_stderr,none": 0.0053604900103817995, "inst_level_loose_acc,none": 0.5551558752997602, "inst_level_loose_acc_stderr,none": "N/A", "prompt_level_strict_acc,none": 0.3659889094269871, "prompt_level_strict_acc_stderr,none": 0.02072934168450657, "exact_match,none": 0.1336858006042296, "exact_match_stderr,none": 0.00888347241287297, "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.4804721402534282, "acc_norm_stderr,none": 0.006196076922023256, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.816, "acc_norm_stderr,none": 0.02455581299422255 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.5935828877005348, "acc_norm_stderr,none": 0.036013904358574385 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.424, "acc_norm_stderr,none": 0.03131803437491622 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.46, "acc_norm_stderr,none": 0.031584653891499004 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.536, "acc_norm_stderr,none": 0.031603975145223735 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.328, "acc_norm_stderr,none": 0.029752391824475363 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.62, "acc_norm_stderr,none": 0.030760116042626098 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.436, "acc_norm_stderr,none": 0.031425567060281365 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.408, "acc_norm_stderr,none": 0.031145209846548512 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.688, "acc_norm_stderr,none": 0.029361067575219852 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.612, "acc_norm_stderr,none": 0.030881038748993974 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.616, "acc_norm_stderr,none": 0.030821679117375447 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.476, "acc_norm_stderr,none": 0.03164968895968774 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.4726027397260274, "acc_norm_stderr,none": 0.04146035831053314 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.552, "acc_norm_stderr,none": 0.03151438761115348 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.552, "acc_norm_stderr,none": 0.03151438761115348 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.448, "acc_norm_stderr,none": 0.03151438761115349 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.550561797752809, "acc_norm_stderr,none": 0.037389649660569645 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.716, "acc_norm_stderr,none": 0.028576958730437443 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.164, "acc_norm_stderr,none": 0.02346526100207671 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.16, "acc_norm_stderr,none": 0.023232714782060626 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.14, "acc_norm_stderr,none": 0.021989409645240245 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.308, "acc_norm_stderr,none": 0.02925692860650181 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.5, "acc_norm_stderr,none": 0.031686212526223896 }, "leaderboard_gpqa": { "acc_norm,none": 0.31208053691275167, "acc_norm_stderr,none": 0.013434067707656962, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.3333333333333333, "acc_norm_stderr,none": 0.033586181457325226 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.31135531135531136, "acc_norm_stderr,none": 0.019834784635900788 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.30357142857142855, "acc_norm_stderr,none": 0.021747782232917543 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.3659889094269871, "prompt_level_strict_acc_stderr,none": 0.02072934168450657, "inst_level_strict_acc,none": 0.5203836930455635, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.41035120147874304, "prompt_level_loose_acc_stderr,none": 0.021167895542791835, "inst_level_loose_acc,none": 0.5551558752997602, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.1336858006042296, "exact_match_stderr,none": 0.00888347241287297, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.28013029315960913, "exact_match_stderr,none": 0.025671206118429726 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.056910569105691054, "exact_match_stderr,none": 0.020974566219895126 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.015151515151515152, "exact_match_stderr,none": 0.01067276863717474 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.02857142857142857, "exact_match_stderr,none": 0.009973998820736053 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.12337662337662338, "exact_match_stderr,none": 0.026587484423674337 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.24870466321243523, "exact_match_stderr,none": 0.03119584087770025 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.05185185185185185, "exact_match_stderr,none": 0.019154368449050496 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.3515625, "acc_stderr,none": 0.00435295793176245 }, "leaderboard_musr": { "acc_norm,none": 0.4060846560846561, "acc_norm_stderr,none": 0.017602041943777794, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.532, "acc_norm_stderr,none": 0.031621252575725574 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.3359375, "acc_norm_stderr,none": 0.029577647634376425 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.352, "acc_norm_stderr,none": 0.030266288057359866 } }, "leaderboard": { "prompt_level_loose_acc,none": 0.41035120147874304, "prompt_level_loose_acc_stderr,none": 0.021167895542791835, "inst_level_strict_acc,none": 0.5203836930455635, "inst_level_strict_acc_stderr,none": "N/A", "acc,none": 0.3515625, "acc_stderr,none": 0.00435295793176245, "acc_norm,none": 0.4471397068361655, "acc_norm_stderr,none": 0.0053604900103817995, "inst_level_loose_acc,none": 0.5551558752997602, "inst_level_loose_acc_stderr,none": "N/A", "prompt_level_strict_acc,none": 0.3659889094269871, "prompt_level_strict_acc_stderr,none": 0.02072934168450657, "exact_match,none": 0.1336858006042296, "exact_match_stderr,none": 0.00888347241287297, "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.4804721402534282, "acc_norm_stderr,none": 0.006196076922023256, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.816, "acc_norm_stderr,none": 0.02455581299422255 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.5935828877005348, "acc_norm_stderr,none": 0.036013904358574385 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.424, "acc_norm_stderr,none": 0.03131803437491622 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.46, "acc_norm_stderr,none": 0.031584653891499004 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.536, "acc_norm_stderr,none": 0.031603975145223735 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.328, "acc_norm_stderr,none": 0.029752391824475363 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.62, "acc_norm_stderr,none": 0.030760116042626098 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.436, "acc_norm_stderr,none": 0.031425567060281365 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.408, "acc_norm_stderr,none": 0.031145209846548512 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.688, "acc_norm_stderr,none": 0.029361067575219852 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.612, "acc_norm_stderr,none": 0.030881038748993974 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.616, "acc_norm_stderr,none": 0.030821679117375447 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.476, "acc_norm_stderr,none": 0.03164968895968774 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.4726027397260274, "acc_norm_stderr,none": 0.04146035831053314 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.552, "acc_norm_stderr,none": 0.03151438761115348 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.552, "acc_norm_stderr,none": 0.03151438761115348 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.448, "acc_norm_stderr,none": 0.03151438761115349 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.550561797752809, "acc_norm_stderr,none": 0.037389649660569645 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.716, "acc_norm_stderr,none": 0.028576958730437443 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.164, "acc_norm_stderr,none": 0.02346526100207671 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.16, "acc_norm_stderr,none": 0.023232714782060626 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.14, "acc_norm_stderr,none": 0.021989409645240245 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.308, "acc_norm_stderr,none": 0.02925692860650181 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.5, "acc_norm_stderr,none": 0.031686212526223896 }, "leaderboard_gpqa": { "acc_norm,none": 0.31208053691275167, "acc_norm_stderr,none": 0.013434067707656962, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.3333333333333333, "acc_norm_stderr,none": 0.033586181457325226 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.31135531135531136, "acc_norm_stderr,none": 0.019834784635900788 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.30357142857142855, "acc_norm_stderr,none": 0.021747782232917543 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.3659889094269871, "prompt_level_strict_acc_stderr,none": 0.02072934168450657, "inst_level_strict_acc,none": 0.5203836930455635, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.41035120147874304, "prompt_level_loose_acc_stderr,none": 0.021167895542791835, "inst_level_loose_acc,none": 0.5551558752997602, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.1336858006042296, "exact_match_stderr,none": 0.00888347241287297, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.28013029315960913, "exact_match_stderr,none": 0.025671206118429726 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.056910569105691054, "exact_match_stderr,none": 0.020974566219895126 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.015151515151515152, "exact_match_stderr,none": 0.01067276863717474 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.02857142857142857, "exact_match_stderr,none": 0.009973998820736053 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.12337662337662338, "exact_match_stderr,none": 0.026587484423674337 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.24870466321243523, "exact_match_stderr,none": 0.03119584087770025 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.05185185185185185, "exact_match_stderr,none": 0.019154368449050496 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.3515625, "acc_stderr,none": 0.00435295793176245 }, "leaderboard_musr": { "acc_norm,none": 0.4060846560846561, "acc_norm_stderr,none": 0.017602041943777794, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.532, "acc_norm_stderr,none": 0.031621252575725574 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.3359375, "acc_norm_stderr,none": 0.029577647634376425 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.352, "acc_norm_stderr,none": 0.030266288057359866 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - 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open-llm-leaderboard/cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo-details

open-llm-leaderboard

--- pretty_name: Evaluation run of cluebbers/Llama-3.1-8B-paraphrase-type-generation-apty-ipo dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [cluebbers/Llama-3.1-8B-paraphrase-type-generation-apty-ipo](https://huggingface.co/cluebbers/Llama-3.1-8B-paraphrase-type-generation-apty-ipo)\n\ The dataset is composed of 38 configuration(s), each one corresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can\ \ be found as a specific split in each configuration, the split being named using\ \ the timestamp of the run.The \"train\" split is always pointing to the latest\ \ results.\n\nAn additional configuration \"results\" store all the aggregated results\ \ of the run.\n\nTo load the details from a run, you can for instance do the following:\n\ ```python\nfrom datasets import load_dataset\ndata = load_dataset(\n\t\"open-llm-leaderboard/cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo-details\"\ ,\n\tname=\"cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_bbh_boolean_expressions\"\ ,\n\tsplit=\"latest\"\n)\n```\n\n## Latest results\n\nThese are the [latest results\ \ from run 2024-11-19T21-27-07.770301](https://huggingface.co/datasets/open-llm-leaderboard/cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo-details/blob/main/cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo/results_2024-11-19T21-27-07.770301.json)\ \ (note that there might be results for other tasks in the repos if successive evals\ \ didn't cover the same tasks. You find each in the results and the \"latest\" split\ \ for each eval):\n\n```python\n{\n \"all\": {\n \"leaderboard\": {\n\ \ \"acc_norm,none\": 0.3674925411856272,\n \"acc_norm_stderr,none\"\ : 0.005284553107918915,\n \"inst_level_strict_acc,none\": 0.18585131894484413,\n\ \ \"inst_level_strict_acc_stderr,none\": \"N/A\",\n \"inst_level_loose_acc,none\"\ : 0.20503597122302158,\n \"inst_level_loose_acc_stderr,none\": \"N/A\"\ ,\n \"exact_match,none\": 0.006797583081570997,\n \"exact_match_stderr,none\"\ : 0.002250499312351689,\n \"prompt_level_loose_acc,none\": 0.09611829944547134,\n\ \ \"prompt_level_loose_acc_stderr,none\": 0.01268416714715648,\n \ \ \"acc,none\": 0.2590591755319149,\n \"acc_stderr,none\": 0.00399429731369927,\n\ \ \"prompt_level_strict_acc,none\": 0.07948243992606285,\n \ \ \"prompt_level_strict_acc_stderr,none\": 0.011640047542656398,\n \"\ alias\": \"leaderboard\"\n },\n \"leaderboard_bbh\": {\n \ \ \"acc_norm,none\": 0.3804894983509807,\n \"acc_norm_stderr,none\"\ : 0.006125798368883372,\n \"alias\": \" - leaderboard_bbh\"\n \ \ },\n \"leaderboard_bbh_boolean_expressions\": {\n \"alias\"\ : \" - leaderboard_bbh_boolean_expressions\",\n \"acc_norm,none\": 0.744,\n\ \ \"acc_norm_stderr,none\": 0.027657108718204846\n },\n \ \ \"leaderboard_bbh_causal_judgement\": {\n \"alias\": \" - leaderboard_bbh_causal_judgement\"\ ,\n \"acc_norm,none\": 0.5347593582887701,\n \"acc_norm_stderr,none\"\ : 0.036573080985189216\n },\n \"leaderboard_bbh_date_understanding\"\ : {\n \"alias\": \" - leaderboard_bbh_date_understanding\",\n \ \ \"acc_norm,none\": 0.332,\n \"acc_norm_stderr,none\": 0.029844039047465857\n\ \ },\n \"leaderboard_bbh_disambiguation_qa\": {\n \"alias\"\ : \" - leaderboard_bbh_disambiguation_qa\",\n \"acc_norm,none\": 0.46,\n\ \ \"acc_norm_stderr,none\": 0.031584653891499004\n },\n \ \ \"leaderboard_bbh_formal_fallacies\": {\n \"alias\": \" - leaderboard_bbh_formal_fallacies\"\ ,\n \"acc_norm,none\": 0.5,\n \"acc_norm_stderr,none\": 0.031686212526223896\n\ \ },\n \"leaderboard_bbh_geometric_shapes\": {\n \"alias\"\ : \" - leaderboard_bbh_geometric_shapes\",\n \"acc_norm,none\": 0.348,\n\ \ \"acc_norm_stderr,none\": 0.030186568464511673\n },\n \ \ \"leaderboard_bbh_hyperbaton\": {\n \"alias\": \" - leaderboard_bbh_hyperbaton\"\ ,\n \"acc_norm,none\": 0.532,\n \"acc_norm_stderr,none\":\ \ 0.031621252575725574\n },\n \"leaderboard_bbh_logical_deduction_five_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_logical_deduction_five_objects\"\ ,\n \"acc_norm,none\": 0.284,\n \"acc_norm_stderr,none\":\ \ 0.02857695873043744\n },\n \"leaderboard_bbh_logical_deduction_seven_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\"\ ,\n \"acc_norm,none\": 0.276,\n \"acc_norm_stderr,none\":\ \ 0.02832853727421142\n },\n \"leaderboard_bbh_logical_deduction_three_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_logical_deduction_three_objects\"\ ,\n \"acc_norm,none\": 0.424,\n \"acc_norm_stderr,none\":\ \ 0.03131803437491622\n },\n \"leaderboard_bbh_movie_recommendation\"\ : {\n \"alias\": \" - leaderboard_bbh_movie_recommendation\",\n \ \ \"acc_norm,none\": 0.496,\n \"acc_norm_stderr,none\": 0.0316851985511992\n\ \ },\n \"leaderboard_bbh_navigate\": {\n \"alias\": \"\ \ - leaderboard_bbh_navigate\",\n \"acc_norm,none\": 0.428,\n \ \ \"acc_norm_stderr,none\": 0.031355968923772626\n },\n \"leaderboard_bbh_object_counting\"\ : {\n \"alias\": \" - leaderboard_bbh_object_counting\",\n \ \ \"acc_norm,none\": 0.376,\n \"acc_norm_stderr,none\": 0.03069633626739458\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"\ alias\": \" - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\"\ : 0.21232876712328766,\n \"acc_norm_stderr,none\": 0.03396197282917473\n\ \ },\n \"leaderboard_bbh_reasoning_about_colored_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\",\n\ \ \"acc_norm,none\": 0.276,\n \"acc_norm_stderr,none\": 0.02832853727421142\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \"\ \ - leaderboard_bbh_ruin_names\",\n \"acc_norm,none\": 0.24,\n \ \ \"acc_norm_stderr,none\": 0.027065293652238982\n },\n \"leaderboard_bbh_salient_translation_error_detection\"\ : {\n \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\"\ ,\n \"acc_norm,none\": 0.248,\n \"acc_norm_stderr,none\":\ \ 0.027367497504863593\n },\n \"leaderboard_bbh_snarks\": {\n \ \ \"alias\": \" - leaderboard_bbh_snarks\",\n \"acc_norm,none\"\ : 0.449438202247191,\n \"acc_norm_stderr,none\": 0.037389649660569645\n\ \ },\n \"leaderboard_bbh_sports_understanding\": {\n \"\ alias\": \" - leaderboard_bbh_sports_understanding\",\n \"acc_norm,none\"\ : 0.584,\n \"acc_norm_stderr,none\": 0.031235856237014505\n },\n\ \ \"leaderboard_bbh_temporal_sequences\": {\n \"alias\": \" -\ \ leaderboard_bbh_temporal_sequences\",\n \"acc_norm,none\": 0.252,\n\ \ \"acc_norm_stderr,none\": 0.027513851933031318\n },\n \ \ \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \"\ alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\",\n \ \ \"acc_norm,none\": 0.208,\n \"acc_norm_stderr,none\": 0.02572139890141637\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.132,\n \"acc_norm_stderr,none\":\ \ 0.021450980824038166\n },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.296,\n \"acc_norm_stderr,none\":\ \ 0.028928939388379694\n },\n \"leaderboard_bbh_web_of_lies\": {\n\ \ \"alias\": \" - leaderboard_bbh_web_of_lies\",\n \"acc_norm,none\"\ : 0.488,\n \"acc_norm_stderr,none\": 0.03167708558254714\n },\n\ \ \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.2634228187919463,\n\ \ \"acc_norm_stderr,none\": 0.012770163430421852,\n \"alias\"\ : \" - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n\ \ \"alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\"\ : 0.2727272727272727,\n \"acc_norm_stderr,none\": 0.03173071239071728\n\ \ },\n \"leaderboard_gpqa_extended\": {\n \"alias\": \"\ \ - leaderboard_gpqa_extended\",\n \"acc_norm,none\": 0.2509157509157509,\n\ \ \"acc_norm_stderr,none\": 0.018570818425352315\n },\n \ \ \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.27455357142857145,\n \"acc_norm_stderr,none\"\ : 0.021108747290633768\n },\n \"leaderboard_ifeval\": {\n \ \ \"alias\": \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\"\ : 0.07948243992606285,\n \"prompt_level_strict_acc_stderr,none\": 0.011640047542656396,\n\ \ \"inst_level_strict_acc,none\": 0.18585131894484413,\n \"\ inst_level_strict_acc_stderr,none\": \"N/A\",\n \"prompt_level_loose_acc,none\"\ : 0.09611829944547134,\n \"prompt_level_loose_acc_stderr,none\": 0.01268416714715648,\n\ \ \"inst_level_loose_acc,none\": 0.20503597122302158,\n \"\ inst_level_loose_acc_stderr,none\": \"N/A\"\n },\n \"leaderboard_math_hard\"\ : {\n \"exact_match,none\": 0.006797583081570997,\n \"exact_match_stderr,none\"\ : 0.002250499312351689,\n \"alias\": \" - leaderboard_math_hard\"\n \ \ },\n \"leaderboard_math_algebra_hard\": {\n \"alias\"\ : \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\": 0.009771986970684038,\n\ \ \"exact_match_stderr,none\": 0.005623391633915856\n },\n \ \ \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\": \"\ \ - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.0,\n \"exact_match_stderr,none\": 0.0\n },\n \"leaderboard_math_geometry_hard\"\ : {\n \"alias\": \" - leaderboard_math_geometry_hard\",\n \ \ \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\": 0.0\n \ \ },\n \"leaderboard_math_intermediate_algebra_hard\": {\n \ \ \"alias\": \" - leaderboard_math_intermediate_algebra_hard\",\n \ \ \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\": 0.0\n \ \ },\n \"leaderboard_math_num_theory_hard\": {\n \"alias\"\ : \" - leaderboard_math_num_theory_hard\",\n \"exact_match,none\": 0.0,\n\ \ \"exact_match_stderr,none\": 0.0\n },\n \"leaderboard_math_prealgebra_hard\"\ : {\n \"alias\": \" - leaderboard_math_prealgebra_hard\",\n \ \ \"exact_match,none\": 0.010362694300518135,\n \"exact_match_stderr,none\"\ : 0.007308424386792209\n },\n \"leaderboard_math_precalculus_hard\"\ : {\n \"alias\": \" - leaderboard_math_precalculus_hard\",\n \ \ \"exact_match,none\": 0.02962962962962963,\n \"exact_match_stderr,none\"\ : 0.014648038602753809\n },\n \"leaderboard_mmlu_pro\": {\n \ \ \"alias\": \" - leaderboard_mmlu_pro\",\n \"acc,none\": 0.2590591755319149,\n\ \ \"acc_stderr,none\": 0.00399429731369927\n },\n \"leaderboard_musr\"\ : {\n \"acc_norm,none\": 0.43253968253968256,\n \"acc_norm_stderr,none\"\ : 0.017870703594488484,\n \"alias\": \" - leaderboard_musr\"\n \ \ },\n \"leaderboard_musr_murder_mysteries\": {\n \"alias\":\ \ \" - leaderboard_musr_murder_mysteries\",\n \"acc_norm,none\": 0.52,\n\ \ \"acc_norm_stderr,none\": 0.03166085340849512\n },\n \ \ \"leaderboard_musr_object_placements\": {\n \"alias\": \" - leaderboard_musr_object_placements\"\ ,\n \"acc_norm,none\": 0.34765625,\n \"acc_norm_stderr,none\"\ : 0.029822440677853123\n },\n \"leaderboard_musr_team_allocation\"\ : {\n \"alias\": \" - leaderboard_musr_team_allocation\",\n \ \ \"acc_norm,none\": 0.432,\n \"acc_norm_stderr,none\": 0.03139181076542942\n\ \ }\n },\n \"leaderboard\": {\n \"acc_norm,none\": 0.3674925411856272,\n\ \ \"acc_norm_stderr,none\": 0.005284553107918915,\n \"inst_level_strict_acc,none\"\ : 0.18585131894484413,\n \"inst_level_strict_acc_stderr,none\": \"N/A\",\n\ \ \"inst_level_loose_acc,none\": 0.20503597122302158,\n \"inst_level_loose_acc_stderr,none\"\ : \"N/A\",\n \"exact_match,none\": 0.006797583081570997,\n \"exact_match_stderr,none\"\ : 0.002250499312351689,\n \"prompt_level_loose_acc,none\": 0.09611829944547134,\n\ \ \"prompt_level_loose_acc_stderr,none\": 0.01268416714715648,\n \"\ acc,none\": 0.2590591755319149,\n \"acc_stderr,none\": 0.00399429731369927,\n\ \ \"prompt_level_strict_acc,none\": 0.07948243992606285,\n \"prompt_level_strict_acc_stderr,none\"\ : 0.011640047542656398,\n \"alias\": \"leaderboard\"\n },\n \"leaderboard_bbh\"\ : {\n \"acc_norm,none\": 0.3804894983509807,\n \"acc_norm_stderr,none\"\ : 0.006125798368883372,\n \"alias\": \" - leaderboard_bbh\"\n },\n \ \ \"leaderboard_bbh_boolean_expressions\": {\n \"alias\": \" - leaderboard_bbh_boolean_expressions\"\ ,\n \"acc_norm,none\": 0.744,\n \"acc_norm_stderr,none\": 0.027657108718204846\n\ \ },\n \"leaderboard_bbh_causal_judgement\": {\n \"alias\": \" - leaderboard_bbh_causal_judgement\"\ ,\n \"acc_norm,none\": 0.5347593582887701,\n \"acc_norm_stderr,none\"\ : 0.036573080985189216\n },\n \"leaderboard_bbh_date_understanding\": {\n\ \ \"alias\": \" - leaderboard_bbh_date_understanding\",\n \"acc_norm,none\"\ : 0.332,\n \"acc_norm_stderr,none\": 0.029844039047465857\n },\n \"\ leaderboard_bbh_disambiguation_qa\": {\n \"alias\": \" - leaderboard_bbh_disambiguation_qa\"\ ,\n \"acc_norm,none\": 0.46,\n \"acc_norm_stderr,none\": 0.031584653891499004\n\ \ },\n \"leaderboard_bbh_formal_fallacies\": {\n \"alias\": \" - leaderboard_bbh_formal_fallacies\"\ ,\n \"acc_norm,none\": 0.5,\n \"acc_norm_stderr,none\": 0.031686212526223896\n\ \ },\n \"leaderboard_bbh_geometric_shapes\": {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\"\ ,\n \"acc_norm,none\": 0.348,\n \"acc_norm_stderr,none\": 0.030186568464511673\n\ \ },\n \"leaderboard_bbh_hyperbaton\": {\n \"alias\": \" - leaderboard_bbh_hyperbaton\"\ ,\n \"acc_norm,none\": 0.532,\n \"acc_norm_stderr,none\": 0.031621252575725574\n\ \ },\n \"leaderboard_bbh_logical_deduction_five_objects\": {\n \"alias\"\ : \" - leaderboard_bbh_logical_deduction_five_objects\",\n \"acc_norm,none\"\ : 0.284,\n \"acc_norm_stderr,none\": 0.02857695873043744\n },\n \"\ leaderboard_bbh_logical_deduction_seven_objects\": {\n \"alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\"\ ,\n \"acc_norm,none\": 0.276,\n \"acc_norm_stderr,none\": 0.02832853727421142\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \"\ alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n \"acc_norm,none\"\ : 0.424,\n \"acc_norm_stderr,none\": 0.03131803437491622\n },\n \"\ leaderboard_bbh_movie_recommendation\": {\n \"alias\": \" - leaderboard_bbh_movie_recommendation\"\ ,\n \"acc_norm,none\": 0.496,\n \"acc_norm_stderr,none\": 0.0316851985511992\n\ \ },\n \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.428,\n \"acc_norm_stderr,none\": 0.031355968923772626\n\ \ },\n \"leaderboard_bbh_object_counting\": {\n \"alias\": \" - leaderboard_bbh_object_counting\"\ ,\n \"acc_norm,none\": 0.376,\n \"acc_norm_stderr,none\": 0.03069633626739458\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"alias\": \" \ \ - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\": 0.21232876712328766,\n\ \ \"acc_norm_stderr,none\": 0.03396197282917473\n },\n \"leaderboard_bbh_reasoning_about_colored_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\"\ ,\n \"acc_norm,none\": 0.276,\n \"acc_norm_stderr,none\": 0.02832853727421142\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \" - leaderboard_bbh_ruin_names\"\ ,\n \"acc_norm,none\": 0.24,\n \"acc_norm_stderr,none\": 0.027065293652238982\n\ \ },\n \"leaderboard_bbh_salient_translation_error_detection\": {\n \ \ \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\",\n \ \ \"acc_norm,none\": 0.248,\n \"acc_norm_stderr,none\": 0.027367497504863593\n\ \ },\n \"leaderboard_bbh_snarks\": {\n \"alias\": \" - leaderboard_bbh_snarks\"\ ,\n \"acc_norm,none\": 0.449438202247191,\n \"acc_norm_stderr,none\"\ : 0.037389649660569645\n },\n \"leaderboard_bbh_sports_understanding\": {\n\ \ \"alias\": \" - leaderboard_bbh_sports_understanding\",\n \"acc_norm,none\"\ : 0.584,\n \"acc_norm_stderr,none\": 0.031235856237014505\n },\n \"\ leaderboard_bbh_temporal_sequences\": {\n \"alias\": \" - leaderboard_bbh_temporal_sequences\"\ ,\n \"acc_norm,none\": 0.252,\n \"acc_norm_stderr,none\": 0.027513851933031318\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\"\ ,\n \"acc_norm,none\": 0.208,\n \"acc_norm_stderr,none\": 0.02572139890141637\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.132,\n \"acc_norm_stderr,none\": 0.021450980824038166\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.296,\n \"acc_norm_stderr,none\": 0.028928939388379694\n\ \ },\n \"leaderboard_bbh_web_of_lies\": {\n \"alias\": \" - leaderboard_bbh_web_of_lies\"\ ,\n \"acc_norm,none\": 0.488,\n \"acc_norm_stderr,none\": 0.03167708558254714\n\ \ },\n \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.2634228187919463,\n\ \ \"acc_norm_stderr,none\": 0.012770163430421852,\n \"alias\": \"\ \ - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n \"\ alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\": 0.2727272727272727,\n\ \ \"acc_norm_stderr,none\": 0.03173071239071728\n },\n \"leaderboard_gpqa_extended\"\ : {\n \"alias\": \" - leaderboard_gpqa_extended\",\n \"acc_norm,none\"\ : 0.2509157509157509,\n \"acc_norm_stderr,none\": 0.018570818425352315\n\ \ },\n \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.27455357142857145,\n \"acc_norm_stderr,none\"\ : 0.021108747290633768\n },\n \"leaderboard_ifeval\": {\n \"alias\"\ : \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\": 0.07948243992606285,\n\ \ \"prompt_level_strict_acc_stderr,none\": 0.011640047542656396,\n \ \ \"inst_level_strict_acc,none\": 0.18585131894484413,\n \"inst_level_strict_acc_stderr,none\"\ : \"N/A\",\n \"prompt_level_loose_acc,none\": 0.09611829944547134,\n \ \ \"prompt_level_loose_acc_stderr,none\": 0.01268416714715648,\n \"inst_level_loose_acc,none\"\ : 0.20503597122302158,\n \"inst_level_loose_acc_stderr,none\": \"N/A\"\n\ \ },\n \"leaderboard_math_hard\": {\n \"exact_match,none\": 0.006797583081570997,\n\ \ \"exact_match_stderr,none\": 0.002250499312351689,\n \"alias\":\ \ \" - leaderboard_math_hard\"\n },\n \"leaderboard_math_algebra_hard\": {\n\ \ \"alias\": \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\"\ : 0.009771986970684038,\n \"exact_match_stderr,none\": 0.005623391633915856\n\ \ },\n \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\":\ \ \" - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.0,\n \"exact_match_stderr,none\": 0.0\n },\n \"leaderboard_math_geometry_hard\"\ : {\n \"alias\": \" - leaderboard_math_geometry_hard\",\n \"exact_match,none\"\ : 0.0,\n \"exact_match_stderr,none\": 0.0\n },\n \"leaderboard_math_intermediate_algebra_hard\"\ : {\n \"alias\": \" - leaderboard_math_intermediate_algebra_hard\",\n \ \ \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\": 0.0\n \ \ },\n \"leaderboard_math_num_theory_hard\": {\n \"alias\": \" - leaderboard_math_num_theory_hard\"\ ,\n \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\": 0.0\n\ \ },\n \"leaderboard_math_prealgebra_hard\": {\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\ ,\n \"exact_match,none\": 0.010362694300518135,\n \"exact_match_stderr,none\"\ : 0.007308424386792209\n },\n \"leaderboard_math_precalculus_hard\": {\n \ \ \"alias\": \" - leaderboard_math_precalculus_hard\",\n \"exact_match,none\"\ : 0.02962962962962963,\n \"exact_match_stderr,none\": 0.014648038602753809\n\ \ },\n \"leaderboard_mmlu_pro\": {\n \"alias\": \" - leaderboard_mmlu_pro\"\ ,\n \"acc,none\": 0.2590591755319149,\n \"acc_stderr,none\": 0.00399429731369927\n\ \ },\n \"leaderboard_musr\": {\n \"acc_norm,none\": 0.43253968253968256,\n\ \ \"acc_norm_stderr,none\": 0.017870703594488484,\n \"alias\": \"\ \ - leaderboard_musr\"\n },\n \"leaderboard_musr_murder_mysteries\": {\n \ \ \"alias\": \" - leaderboard_musr_murder_mysteries\",\n \"acc_norm,none\"\ : 0.52,\n \"acc_norm_stderr,none\": 0.03166085340849512\n },\n \"leaderboard_musr_object_placements\"\ : {\n \"alias\": \" - leaderboard_musr_object_placements\",\n \"\ acc_norm,none\": 0.34765625,\n \"acc_norm_stderr,none\": 0.029822440677853123\n\ \ },\n \"leaderboard_musr_team_allocation\": {\n \"alias\": \" - leaderboard_musr_team_allocation\"\ ,\n \"acc_norm,none\": 0.432,\n \"acc_norm_stderr,none\": 0.03139181076542942\n\ \ }\n}\n```" repo_url: https://huggingface.co/cluebbers/Llama-3.1-8B-paraphrase-type-generation-apty-ipo leaderboard_url: '' point_of_contact: '' configs: - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_bbh_boolean_expressions data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_bbh_causal_judgement data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_bbh_date_understanding data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_bbh_disambiguation_qa data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_bbh_formal_fallacies data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_bbh_geometric_shapes data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_bbh_hyperbaton data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_bbh_logical_deduction_five_objects data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_bbh_logical_deduction_seven_objects data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_bbh_logical_deduction_three_objects data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_bbh_movie_recommendation data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_bbh_navigate data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_bbh_navigate_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_navigate_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_bbh_object_counting data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_bbh_object_counting_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_object_counting_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_bbh_penguins_in_a_table data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_bbh_reasoning_about_colored_objects data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_bbh_ruin_names data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_bbh_salient_translation_error_detection data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_bbh_snarks data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_bbh_snarks_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_snarks_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_bbh_sports_understanding data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_bbh_temporal_sequences data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_bbh_tracking_shuffled_objects_five_objects data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_bbh_tracking_shuffled_objects_seven_objects data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_bbh_tracking_shuffled_objects_three_objects data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_bbh_web_of_lies data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_gpqa_diamond data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_gpqa_diamond_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_diamond_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_gpqa_extended data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_gpqa_extended_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_extended_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_gpqa_main data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_gpqa_main_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_main_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_ifeval data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_ifeval_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_ifeval_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_math_algebra_hard data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_math_algebra_hard_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_math_algebra_hard_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_math_counting_and_prob_hard data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_math_geometry_hard data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_math_geometry_hard_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_math_geometry_hard_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_math_intermediate_algebra_hard data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_math_num_theory_hard data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_math_prealgebra_hard data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_math_precalculus_hard data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_mmlu_pro data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_mmlu_pro_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_mmlu_pro_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_musr_murder_mysteries data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_musr_object_placements data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_musr_object_placements_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_musr_object_placements_2024-11-19T21-27-07.770301.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_musr_team_allocation data_files: - split: 2024_11_19T21_27_07.770301 path: - '**/samples_leaderboard_musr_team_allocation_2024-11-19T21-27-07.770301.jsonl' - split: latest path: - '**/samples_leaderboard_musr_team_allocation_2024-11-19T21-27-07.770301.jsonl' --- # Dataset Card for Evaluation run of cluebbers/Llama-3.1-8B-paraphrase-type-generation-apty-ipo <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [cluebbers/Llama-3.1-8B-paraphrase-type-generation-apty-ipo](https://huggingface.co/cluebbers/Llama-3.1-8B-paraphrase-type-generation-apty-ipo) The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run. To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset( "open-llm-leaderboard/cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo-details", name="cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo__leaderboard_bbh_boolean_expressions", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-11-19T21-27-07.770301](https://huggingface.co/datasets/open-llm-leaderboard/cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo-details/blob/main/cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-ipo/results_2024-11-19T21-27-07.770301.json) (note that there might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "leaderboard": { "acc_norm,none": 0.3674925411856272, "acc_norm_stderr,none": 0.005284553107918915, "inst_level_strict_acc,none": 0.18585131894484413, "inst_level_strict_acc_stderr,none": "N/A", "inst_level_loose_acc,none": 0.20503597122302158, "inst_level_loose_acc_stderr,none": "N/A", "exact_match,none": 0.006797583081570997, "exact_match_stderr,none": 0.002250499312351689, "prompt_level_loose_acc,none": 0.09611829944547134, "prompt_level_loose_acc_stderr,none": 0.01268416714715648, "acc,none": 0.2590591755319149, "acc_stderr,none": 0.00399429731369927, "prompt_level_strict_acc,none": 0.07948243992606285, "prompt_level_strict_acc_stderr,none": 0.011640047542656398, "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.3804894983509807, "acc_norm_stderr,none": 0.006125798368883372, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.744, "acc_norm_stderr,none": 0.027657108718204846 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.5347593582887701, "acc_norm_stderr,none": 0.036573080985189216 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.332, "acc_norm_stderr,none": 0.029844039047465857 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.46, "acc_norm_stderr,none": 0.031584653891499004 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.5, "acc_norm_stderr,none": 0.031686212526223896 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.348, "acc_norm_stderr,none": 0.030186568464511673 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.532, "acc_norm_stderr,none": 0.031621252575725574 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.284, "acc_norm_stderr,none": 0.02857695873043744 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.276, "acc_norm_stderr,none": 0.02832853727421142 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.424, "acc_norm_stderr,none": 0.03131803437491622 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.496, "acc_norm_stderr,none": 0.0316851985511992 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.428, "acc_norm_stderr,none": 0.031355968923772626 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.376, "acc_norm_stderr,none": 0.03069633626739458 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.21232876712328766, "acc_norm_stderr,none": 0.03396197282917473 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.276, "acc_norm_stderr,none": 0.02832853727421142 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.24, "acc_norm_stderr,none": 0.027065293652238982 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.248, "acc_norm_stderr,none": 0.027367497504863593 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.449438202247191, "acc_norm_stderr,none": 0.037389649660569645 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.584, "acc_norm_stderr,none": 0.031235856237014505 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.252, "acc_norm_stderr,none": 0.027513851933031318 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.208, "acc_norm_stderr,none": 0.02572139890141637 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.132, "acc_norm_stderr,none": 0.021450980824038166 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.296, "acc_norm_stderr,none": 0.028928939388379694 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.488, "acc_norm_stderr,none": 0.03167708558254714 }, "leaderboard_gpqa": { "acc_norm,none": 0.2634228187919463, "acc_norm_stderr,none": 0.012770163430421852, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.2727272727272727, "acc_norm_stderr,none": 0.03173071239071728 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.2509157509157509, "acc_norm_stderr,none": 0.018570818425352315 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.27455357142857145, "acc_norm_stderr,none": 0.021108747290633768 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.07948243992606285, "prompt_level_strict_acc_stderr,none": 0.011640047542656396, "inst_level_strict_acc,none": 0.18585131894484413, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.09611829944547134, "prompt_level_loose_acc_stderr,none": 0.01268416714715648, "inst_level_loose_acc,none": 0.20503597122302158, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.006797583081570997, "exact_match_stderr,none": 0.002250499312351689, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.009771986970684038, "exact_match_stderr,none": 0.005623391633915856 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.010362694300518135, "exact_match_stderr,none": 0.007308424386792209 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.02962962962962963, "exact_match_stderr,none": 0.014648038602753809 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.2590591755319149, "acc_stderr,none": 0.00399429731369927 }, "leaderboard_musr": { "acc_norm,none": 0.43253968253968256, "acc_norm_stderr,none": 0.017870703594488484, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.52, "acc_norm_stderr,none": 0.03166085340849512 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.34765625, "acc_norm_stderr,none": 0.029822440677853123 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.432, "acc_norm_stderr,none": 0.03139181076542942 } }, "leaderboard": { "acc_norm,none": 0.3674925411856272, "acc_norm_stderr,none": 0.005284553107918915, "inst_level_strict_acc,none": 0.18585131894484413, "inst_level_strict_acc_stderr,none": "N/A", "inst_level_loose_acc,none": 0.20503597122302158, "inst_level_loose_acc_stderr,none": "N/A", "exact_match,none": 0.006797583081570997, "exact_match_stderr,none": 0.002250499312351689, "prompt_level_loose_acc,none": 0.09611829944547134, "prompt_level_loose_acc_stderr,none": 0.01268416714715648, "acc,none": 0.2590591755319149, "acc_stderr,none": 0.00399429731369927, "prompt_level_strict_acc,none": 0.07948243992606285, "prompt_level_strict_acc_stderr,none": 0.011640047542656398, "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.3804894983509807, "acc_norm_stderr,none": 0.006125798368883372, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.744, "acc_norm_stderr,none": 0.027657108718204846 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.5347593582887701, "acc_norm_stderr,none": 0.036573080985189216 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.332, "acc_norm_stderr,none": 0.029844039047465857 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.46, "acc_norm_stderr,none": 0.031584653891499004 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.5, "acc_norm_stderr,none": 0.031686212526223896 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.348, "acc_norm_stderr,none": 0.030186568464511673 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.532, "acc_norm_stderr,none": 0.031621252575725574 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.284, "acc_norm_stderr,none": 0.02857695873043744 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.276, "acc_norm_stderr,none": 0.02832853727421142 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.424, "acc_norm_stderr,none": 0.03131803437491622 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.496, "acc_norm_stderr,none": 0.0316851985511992 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.428, "acc_norm_stderr,none": 0.031355968923772626 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.376, "acc_norm_stderr,none": 0.03069633626739458 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.21232876712328766, "acc_norm_stderr,none": 0.03396197282917473 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.276, "acc_norm_stderr,none": 0.02832853727421142 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.24, "acc_norm_stderr,none": 0.027065293652238982 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.248, "acc_norm_stderr,none": 0.027367497504863593 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.449438202247191, "acc_norm_stderr,none": 0.037389649660569645 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.584, "acc_norm_stderr,none": 0.031235856237014505 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.252, "acc_norm_stderr,none": 0.027513851933031318 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.208, "acc_norm_stderr,none": 0.02572139890141637 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.132, "acc_norm_stderr,none": 0.021450980824038166 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.296, "acc_norm_stderr,none": 0.028928939388379694 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.488, "acc_norm_stderr,none": 0.03167708558254714 }, "leaderboard_gpqa": { "acc_norm,none": 0.2634228187919463, "acc_norm_stderr,none": 0.012770163430421852, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.2727272727272727, "acc_norm_stderr,none": 0.03173071239071728 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.2509157509157509, "acc_norm_stderr,none": 0.018570818425352315 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.27455357142857145, "acc_norm_stderr,none": 0.021108747290633768 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.07948243992606285, "prompt_level_strict_acc_stderr,none": 0.011640047542656396, "inst_level_strict_acc,none": 0.18585131894484413, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.09611829944547134, "prompt_level_loose_acc_stderr,none": 0.01268416714715648, "inst_level_loose_acc,none": 0.20503597122302158, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.006797583081570997, "exact_match_stderr,none": 0.002250499312351689, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.009771986970684038, "exact_match_stderr,none": 0.005623391633915856 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.010362694300518135, "exact_match_stderr,none": 0.007308424386792209 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.02962962962962963, "exact_match_stderr,none": 0.014648038602753809 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.2590591755319149, "acc_stderr,none": 0.00399429731369927 }, "leaderboard_musr": { "acc_norm,none": 0.43253968253968256, "acc_norm_stderr,none": 0.017870703594488484, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.52, "acc_norm_stderr,none": 0.03166085340849512 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.34765625, "acc_norm_stderr,none": 0.029822440677853123 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.432, "acc_norm_stderr,none": 0.03139181076542942 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - 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open-llm-leaderboard/cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc-details

open-llm-leaderboard

--- pretty_name: Evaluation run of cluebbers/Llama-3.1-8B-paraphrase-type-generation-etpc dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [cluebbers/Llama-3.1-8B-paraphrase-type-generation-etpc](https://huggingface.co/cluebbers/Llama-3.1-8B-paraphrase-type-generation-etpc)\n\ The dataset is composed of 38 configuration(s), each one corresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can\ \ be found as a specific split in each configuration, the split being named using\ \ the timestamp of the run.The \"train\" split is always pointing to the latest\ \ results.\n\nAn additional configuration \"results\" store all the aggregated results\ \ of the run.\n\nTo load the details from a run, you can for instance do the following:\n\ ```python\nfrom datasets import load_dataset\ndata = load_dataset(\n\t\"open-llm-leaderboard/cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc-details\"\ ,\n\tname=\"cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_bbh_boolean_expressions\"\ ,\n\tsplit=\"latest\"\n)\n```\n\n## Latest results\n\nThese are the [latest results\ \ from run 2024-11-19T21-27-59.247727](https://huggingface.co/datasets/open-llm-leaderboard/cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc-details/blob/main/cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc/results_2024-11-19T21-27-59.247727.json)\ \ (note that there might be results for other tasks in the repos if successive evals\ \ didn't cover the same tasks. You find each in the results and the \"latest\" split\ \ for each eval):\n\n```python\n{\n \"all\": {\n \"leaderboard\": {\n\ \ \"acc_norm,none\": 0.36619535607731224,\n \"acc_norm_stderr,none\"\ : 0.0052734901127333995,\n \"prompt_level_loose_acc,none\": 0.08317929759704251,\n\ \ \"prompt_level_loose_acc_stderr,none\": 0.01188373476400857,\n \ \ \"inst_level_strict_acc,none\": 0.17146282973621102,\n \"inst_level_strict_acc_stderr,none\"\ : \"N/A\",\n \"acc,none\": 0.25556848404255317,\n \"acc_stderr,none\"\ : 0.003976629780041046,\n \"prompt_level_strict_acc,none\": 0.07024029574861368,\n\ \ \"prompt_level_strict_acc_stderr,none\": 0.010997187906692914,\n \ \ \"exact_match,none\": 0.004531722054380665,\n \"exact_match_stderr,none\"\ : 0.0018490874508892755,\n \"inst_level_loose_acc,none\": 0.19184652278177458,\n\ \ \"inst_level_loose_acc_stderr,none\": \"N/A\",\n \"alias\"\ : \"leaderboard\"\n },\n \"leaderboard_bbh\": {\n \"acc_norm,none\"\ : 0.37858010762020483,\n \"acc_norm_stderr,none\": 0.006105180850940205,\n\ \ \"alias\": \" - leaderboard_bbh\"\n },\n \"leaderboard_bbh_boolean_expressions\"\ : {\n \"alias\": \" - leaderboard_bbh_boolean_expressions\",\n \ \ \"acc_norm,none\": 0.764,\n \"acc_norm_stderr,none\": 0.026909337594953852\n\ \ },\n \"leaderboard_bbh_causal_judgement\": {\n \"alias\"\ : \" - leaderboard_bbh_causal_judgement\",\n \"acc_norm,none\": 0.5294117647058824,\n\ \ \"acc_norm_stderr,none\": 0.03659829510813266\n },\n \ \ \"leaderboard_bbh_date_understanding\": {\n \"alias\": \" - leaderboard_bbh_date_understanding\"\ ,\n \"acc_norm,none\": 0.32,\n \"acc_norm_stderr,none\": 0.029561724955240978\n\ \ },\n \"leaderboard_bbh_disambiguation_qa\": {\n \"alias\"\ : \" - leaderboard_bbh_disambiguation_qa\",\n \"acc_norm,none\": 0.456,\n\ \ \"acc_norm_stderr,none\": 0.031563285061213475\n },\n \ \ \"leaderboard_bbh_formal_fallacies\": {\n \"alias\": \" - leaderboard_bbh_formal_fallacies\"\ ,\n \"acc_norm,none\": 0.488,\n \"acc_norm_stderr,none\":\ \ 0.03167708558254714\n },\n \"leaderboard_bbh_geometric_shapes\"\ : {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\",\n \ \ \"acc_norm,none\": 0.332,\n \"acc_norm_stderr,none\": 0.029844039047465857\n\ \ },\n \"leaderboard_bbh_hyperbaton\": {\n \"alias\": \"\ \ - leaderboard_bbh_hyperbaton\",\n \"acc_norm,none\": 0.552,\n \ \ \"acc_norm_stderr,none\": 0.03151438761115348\n },\n \"leaderboard_bbh_logical_deduction_five_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_logical_deduction_five_objects\"\ ,\n \"acc_norm,none\": 0.284,\n \"acc_norm_stderr,none\":\ \ 0.02857695873043744\n },\n \"leaderboard_bbh_logical_deduction_seven_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\"\ ,\n \"acc_norm,none\": 0.284,\n \"acc_norm_stderr,none\":\ \ 0.02857695873043744\n },\n \"leaderboard_bbh_logical_deduction_three_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_logical_deduction_three_objects\"\ ,\n \"acc_norm,none\": 0.416,\n \"acc_norm_stderr,none\":\ \ 0.031235856237014505\n },\n \"leaderboard_bbh_movie_recommendation\"\ : {\n \"alias\": \" - leaderboard_bbh_movie_recommendation\",\n \ \ \"acc_norm,none\": 0.496,\n \"acc_norm_stderr,none\": 0.0316851985511992\n\ \ },\n \"leaderboard_bbh_navigate\": {\n \"alias\": \"\ \ - leaderboard_bbh_navigate\",\n \"acc_norm,none\": 0.42,\n \ \ \"acc_norm_stderr,none\": 0.03127799950463661\n },\n \"leaderboard_bbh_object_counting\"\ : {\n \"alias\": \" - leaderboard_bbh_object_counting\",\n \ \ \"acc_norm,none\": 0.368,\n \"acc_norm_stderr,none\": 0.03056207062099311\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"\ alias\": \" - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\"\ : 0.2054794520547945,\n \"acc_norm_stderr,none\": 0.03355465401072847\n\ \ },\n \"leaderboard_bbh_reasoning_about_colored_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\",\n\ \ \"acc_norm,none\": 0.26,\n \"acc_norm_stderr,none\": 0.027797315752644335\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \"\ \ - leaderboard_bbh_ruin_names\",\n \"acc_norm,none\": 0.248,\n \ \ \"acc_norm_stderr,none\": 0.027367497504863593\n },\n \"\ leaderboard_bbh_salient_translation_error_detection\": {\n \"alias\"\ : \" - leaderboard_bbh_salient_translation_error_detection\",\n \"acc_norm,none\"\ : 0.26,\n \"acc_norm_stderr,none\": 0.027797315752644335\n },\n\ \ \"leaderboard_bbh_snarks\": {\n \"alias\": \" - leaderboard_bbh_snarks\"\ ,\n \"acc_norm,none\": 0.4550561797752809,\n \"acc_norm_stderr,none\"\ : 0.03743016495716991\n },\n \"leaderboard_bbh_sports_understanding\"\ : {\n \"alias\": \" - leaderboard_bbh_sports_understanding\",\n \ \ \"acc_norm,none\": 0.58,\n \"acc_norm_stderr,none\": 0.03127799950463661\n\ \ },\n \"leaderboard_bbh_temporal_sequences\": {\n \"alias\"\ : \" - leaderboard_bbh_temporal_sequences\",\n \"acc_norm,none\": 0.256,\n\ \ \"acc_norm_stderr,none\": 0.027657108718204846\n },\n \ \ \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \"\ alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\",\n \ \ \"acc_norm,none\": 0.192,\n \"acc_norm_stderr,none\": 0.024960691989171963\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.128,\n \"acc_norm_stderr,none\":\ \ 0.021172081336336534\n },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.292,\n \"acc_norm_stderr,none\":\ \ 0.02881432040220563\n },\n \"leaderboard_bbh_web_of_lies\": {\n\ \ \"alias\": \" - leaderboard_bbh_web_of_lies\",\n \"acc_norm,none\"\ : 0.488,\n \"acc_norm_stderr,none\": 0.03167708558254714\n },\n\ \ \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.2651006711409396,\n\ \ \"acc_norm_stderr,none\": 0.012799282786329112,\n \"alias\"\ : \" - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n\ \ \"alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\"\ : 0.2777777777777778,\n \"acc_norm_stderr,none\": 0.03191178226713548\n\ \ },\n \"leaderboard_gpqa_extended\": {\n \"alias\": \"\ \ - leaderboard_gpqa_extended\",\n \"acc_norm,none\": 0.2600732600732601,\n\ \ \"acc_norm_stderr,none\": 0.018790743352015988\n },\n \ \ \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.265625,\n \"acc_norm_stderr,none\"\ : 0.02089005840079951\n },\n \"leaderboard_ifeval\": {\n \ \ \"alias\": \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\"\ : 0.07024029574861368,\n \"prompt_level_strict_acc_stderr,none\": 0.010997187906692914,\n\ \ \"inst_level_strict_acc,none\": 0.17146282973621102,\n \"\ inst_level_strict_acc_stderr,none\": \"N/A\",\n \"prompt_level_loose_acc,none\"\ : 0.08317929759704251,\n \"prompt_level_loose_acc_stderr,none\": 0.01188373476400857,\n\ \ \"inst_level_loose_acc,none\": 0.19184652278177458,\n \"\ inst_level_loose_acc_stderr,none\": \"N/A\"\n },\n \"leaderboard_math_hard\"\ : {\n \"exact_match,none\": 0.004531722054380665,\n \"exact_match_stderr,none\"\ : 0.0018490874508892755,\n \"alias\": \" - leaderboard_math_hard\"\n\ \ },\n \"leaderboard_math_algebra_hard\": {\n \"alias\"\ : \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\": 0.003257328990228013,\n\ \ \"exact_match_stderr,none\": 0.003257328990228013\n },\n \ \ \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\": \"\ \ - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.008130081300813009,\n \"exact_match_stderr,none\": 0.008130081300813007\n\ \ },\n \"leaderboard_math_geometry_hard\": {\n \"alias\"\ : \" - leaderboard_math_geometry_hard\",\n \"exact_match,none\": 0.007575757575757576,\n\ \ \"exact_match_stderr,none\": 0.007575757575757577\n },\n \ \ \"leaderboard_math_intermediate_algebra_hard\": {\n \"alias\":\ \ \" - leaderboard_math_intermediate_algebra_hard\",\n \"exact_match,none\"\ : 0.0,\n \"exact_match_stderr,none\": 0.0\n },\n \"leaderboard_math_num_theory_hard\"\ : {\n \"alias\": \" - leaderboard_math_num_theory_hard\",\n \ \ \"exact_match,none\": 0.006493506493506494,\n \"exact_match_stderr,none\"\ : 0.006493506493506494\n },\n \"leaderboard_math_prealgebra_hard\"\ : {\n \"alias\": \" - leaderboard_math_prealgebra_hard\",\n \ \ \"exact_match,none\": 0.0051813471502590676,\n \"exact_match_stderr,none\"\ : 0.0051813471502590676\n },\n \"leaderboard_math_precalculus_hard\"\ : {\n \"alias\": \" - leaderboard_math_precalculus_hard\",\n \ \ \"exact_match,none\": 0.007407407407407408,\n \"exact_match_stderr,none\"\ : 0.007407407407407408\n },\n \"leaderboard_mmlu_pro\": {\n \ \ \"alias\": \" - leaderboard_mmlu_pro\",\n \"acc,none\": 0.25556848404255317,\n\ \ \"acc_stderr,none\": 0.003976629780041046\n },\n \"leaderboard_musr\"\ : {\n \"acc_norm,none\": 0.4312169312169312,\n \"acc_norm_stderr,none\"\ : 0.01788862311572941,\n \"alias\": \" - leaderboard_musr\"\n \ \ },\n \"leaderboard_musr_murder_mysteries\": {\n \"alias\": \"\ \ - leaderboard_musr_murder_mysteries\",\n \"acc_norm,none\": 0.512,\n\ \ \"acc_norm_stderr,none\": 0.03167708558254714\n },\n \ \ \"leaderboard_musr_object_placements\": {\n \"alias\": \" - leaderboard_musr_object_placements\"\ ,\n \"acc_norm,none\": 0.3515625,\n \"acc_norm_stderr,none\"\ : 0.029899590697818237\n },\n \"leaderboard_musr_team_allocation\"\ : {\n \"alias\": \" - leaderboard_musr_team_allocation\",\n \ \ \"acc_norm,none\": 0.432,\n \"acc_norm_stderr,none\": 0.03139181076542942\n\ \ }\n },\n \"leaderboard\": {\n \"acc_norm,none\": 0.36619535607731224,\n\ \ \"acc_norm_stderr,none\": 0.0052734901127333995,\n \"prompt_level_loose_acc,none\"\ : 0.08317929759704251,\n \"prompt_level_loose_acc_stderr,none\": 0.01188373476400857,\n\ \ \"inst_level_strict_acc,none\": 0.17146282973621102,\n \"inst_level_strict_acc_stderr,none\"\ : \"N/A\",\n \"acc,none\": 0.25556848404255317,\n \"acc_stderr,none\"\ : 0.003976629780041046,\n \"prompt_level_strict_acc,none\": 0.07024029574861368,\n\ \ \"prompt_level_strict_acc_stderr,none\": 0.010997187906692914,\n \ \ \"exact_match,none\": 0.004531722054380665,\n \"exact_match_stderr,none\"\ : 0.0018490874508892755,\n \"inst_level_loose_acc,none\": 0.19184652278177458,\n\ \ \"inst_level_loose_acc_stderr,none\": \"N/A\",\n \"alias\": \"leaderboard\"\ \n },\n \"leaderboard_bbh\": {\n \"acc_norm,none\": 0.37858010762020483,\n\ \ \"acc_norm_stderr,none\": 0.006105180850940205,\n \"alias\": \"\ \ - leaderboard_bbh\"\n },\n \"leaderboard_bbh_boolean_expressions\": {\n\ \ \"alias\": \" - leaderboard_bbh_boolean_expressions\",\n \"acc_norm,none\"\ : 0.764,\n \"acc_norm_stderr,none\": 0.026909337594953852\n },\n \"\ leaderboard_bbh_causal_judgement\": {\n \"alias\": \" - leaderboard_bbh_causal_judgement\"\ ,\n \"acc_norm,none\": 0.5294117647058824,\n \"acc_norm_stderr,none\"\ : 0.03659829510813266\n },\n \"leaderboard_bbh_date_understanding\": {\n \ \ \"alias\": \" - leaderboard_bbh_date_understanding\",\n \"acc_norm,none\"\ : 0.32,\n \"acc_norm_stderr,none\": 0.029561724955240978\n },\n \"\ leaderboard_bbh_disambiguation_qa\": {\n \"alias\": \" - leaderboard_bbh_disambiguation_qa\"\ ,\n \"acc_norm,none\": 0.456,\n \"acc_norm_stderr,none\": 0.031563285061213475\n\ \ },\n \"leaderboard_bbh_formal_fallacies\": {\n \"alias\": \" - leaderboard_bbh_formal_fallacies\"\ ,\n \"acc_norm,none\": 0.488,\n \"acc_norm_stderr,none\": 0.03167708558254714\n\ \ },\n \"leaderboard_bbh_geometric_shapes\": {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\"\ ,\n \"acc_norm,none\": 0.332,\n \"acc_norm_stderr,none\": 0.029844039047465857\n\ \ },\n \"leaderboard_bbh_hyperbaton\": {\n \"alias\": \" - leaderboard_bbh_hyperbaton\"\ ,\n \"acc_norm,none\": 0.552,\n \"acc_norm_stderr,none\": 0.03151438761115348\n\ \ },\n \"leaderboard_bbh_logical_deduction_five_objects\": {\n \"alias\"\ : \" - leaderboard_bbh_logical_deduction_five_objects\",\n \"acc_norm,none\"\ : 0.284,\n \"acc_norm_stderr,none\": 0.02857695873043744\n },\n \"\ leaderboard_bbh_logical_deduction_seven_objects\": {\n \"alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\"\ ,\n \"acc_norm,none\": 0.284,\n \"acc_norm_stderr,none\": 0.02857695873043744\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \"\ alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n \"acc_norm,none\"\ : 0.416,\n \"acc_norm_stderr,none\": 0.031235856237014505\n },\n \"\ leaderboard_bbh_movie_recommendation\": {\n \"alias\": \" - leaderboard_bbh_movie_recommendation\"\ ,\n \"acc_norm,none\": 0.496,\n \"acc_norm_stderr,none\": 0.0316851985511992\n\ \ },\n \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.42,\n \"acc_norm_stderr,none\": 0.03127799950463661\n\ \ },\n \"leaderboard_bbh_object_counting\": {\n \"alias\": \" - leaderboard_bbh_object_counting\"\ ,\n \"acc_norm,none\": 0.368,\n \"acc_norm_stderr,none\": 0.03056207062099311\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"alias\": \" \ \ - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\": 0.2054794520547945,\n\ \ \"acc_norm_stderr,none\": 0.03355465401072847\n },\n \"leaderboard_bbh_reasoning_about_colored_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\"\ ,\n \"acc_norm,none\": 0.26,\n \"acc_norm_stderr,none\": 0.027797315752644335\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \" - leaderboard_bbh_ruin_names\"\ ,\n \"acc_norm,none\": 0.248,\n \"acc_norm_stderr,none\": 0.027367497504863593\n\ \ },\n \"leaderboard_bbh_salient_translation_error_detection\": {\n \ \ \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\",\n \ \ \"acc_norm,none\": 0.26,\n \"acc_norm_stderr,none\": 0.027797315752644335\n\ \ },\n \"leaderboard_bbh_snarks\": {\n \"alias\": \" - leaderboard_bbh_snarks\"\ ,\n \"acc_norm,none\": 0.4550561797752809,\n \"acc_norm_stderr,none\"\ : 0.03743016495716991\n },\n \"leaderboard_bbh_sports_understanding\": {\n\ \ \"alias\": \" - leaderboard_bbh_sports_understanding\",\n \"acc_norm,none\"\ : 0.58,\n \"acc_norm_stderr,none\": 0.03127799950463661\n },\n \"leaderboard_bbh_temporal_sequences\"\ : {\n \"alias\": \" - leaderboard_bbh_temporal_sequences\",\n \"\ acc_norm,none\": 0.256,\n \"acc_norm_stderr,none\": 0.027657108718204846\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\"\ ,\n \"acc_norm,none\": 0.192,\n \"acc_norm_stderr,none\": 0.024960691989171963\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.128,\n \"acc_norm_stderr,none\": 0.021172081336336534\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.292,\n \"acc_norm_stderr,none\": 0.02881432040220563\n\ \ },\n \"leaderboard_bbh_web_of_lies\": {\n \"alias\": \" - leaderboard_bbh_web_of_lies\"\ ,\n \"acc_norm,none\": 0.488,\n \"acc_norm_stderr,none\": 0.03167708558254714\n\ \ },\n \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.2651006711409396,\n\ \ \"acc_norm_stderr,none\": 0.012799282786329112,\n \"alias\": \"\ \ - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n \"\ alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\": 0.2777777777777778,\n\ \ \"acc_norm_stderr,none\": 0.03191178226713548\n },\n \"leaderboard_gpqa_extended\"\ : {\n \"alias\": \" - leaderboard_gpqa_extended\",\n \"acc_norm,none\"\ : 0.2600732600732601,\n \"acc_norm_stderr,none\": 0.018790743352015988\n\ \ },\n \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.265625,\n \"acc_norm_stderr,none\": 0.02089005840079951\n\ \ },\n \"leaderboard_ifeval\": {\n \"alias\": \" - leaderboard_ifeval\"\ ,\n \"prompt_level_strict_acc,none\": 0.07024029574861368,\n \"prompt_level_strict_acc_stderr,none\"\ : 0.010997187906692914,\n \"inst_level_strict_acc,none\": 0.17146282973621102,\n\ \ \"inst_level_strict_acc_stderr,none\": \"N/A\",\n \"prompt_level_loose_acc,none\"\ : 0.08317929759704251,\n \"prompt_level_loose_acc_stderr,none\": 0.01188373476400857,\n\ \ \"inst_level_loose_acc,none\": 0.19184652278177458,\n \"inst_level_loose_acc_stderr,none\"\ : \"N/A\"\n },\n \"leaderboard_math_hard\": {\n \"exact_match,none\"\ : 0.004531722054380665,\n \"exact_match_stderr,none\": 0.0018490874508892755,\n\ \ \"alias\": \" - leaderboard_math_hard\"\n },\n \"leaderboard_math_algebra_hard\"\ : {\n \"alias\": \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\"\ : 0.003257328990228013,\n \"exact_match_stderr,none\": 0.003257328990228013\n\ \ },\n \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\":\ \ \" - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.008130081300813009,\n \"exact_match_stderr,none\": 0.008130081300813007\n\ \ },\n \"leaderboard_math_geometry_hard\": {\n \"alias\": \" - leaderboard_math_geometry_hard\"\ ,\n \"exact_match,none\": 0.007575757575757576,\n \"exact_match_stderr,none\"\ : 0.007575757575757577\n },\n \"leaderboard_math_intermediate_algebra_hard\"\ : {\n \"alias\": \" - leaderboard_math_intermediate_algebra_hard\",\n \ \ \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\": 0.0\n \ \ },\n \"leaderboard_math_num_theory_hard\": {\n \"alias\": \" - leaderboard_math_num_theory_hard\"\ ,\n \"exact_match,none\": 0.006493506493506494,\n \"exact_match_stderr,none\"\ : 0.006493506493506494\n },\n \"leaderboard_math_prealgebra_hard\": {\n \ \ \"alias\": \" - leaderboard_math_prealgebra_hard\",\n \"exact_match,none\"\ : 0.0051813471502590676,\n \"exact_match_stderr,none\": 0.0051813471502590676\n\ \ },\n \"leaderboard_math_precalculus_hard\": {\n \"alias\": \" -\ \ leaderboard_math_precalculus_hard\",\n \"exact_match,none\": 0.007407407407407408,\n\ \ \"exact_match_stderr,none\": 0.007407407407407408\n },\n \"leaderboard_mmlu_pro\"\ : {\n \"alias\": \" - leaderboard_mmlu_pro\",\n \"acc,none\": 0.25556848404255317,\n\ \ \"acc_stderr,none\": 0.003976629780041046\n },\n \"leaderboard_musr\"\ : {\n \"acc_norm,none\": 0.4312169312169312,\n \"acc_norm_stderr,none\"\ : 0.01788862311572941,\n \"alias\": \" - leaderboard_musr\"\n },\n \ \ \"leaderboard_musr_murder_mysteries\": {\n \"alias\": \" - leaderboard_musr_murder_mysteries\"\ ,\n \"acc_norm,none\": 0.512,\n \"acc_norm_stderr,none\": 0.03167708558254714\n\ \ },\n \"leaderboard_musr_object_placements\": {\n \"alias\": \" -\ \ leaderboard_musr_object_placements\",\n \"acc_norm,none\": 0.3515625,\n\ \ \"acc_norm_stderr,none\": 0.029899590697818237\n },\n \"leaderboard_musr_team_allocation\"\ : {\n \"alias\": \" - leaderboard_musr_team_allocation\",\n \"acc_norm,none\"\ : 0.432,\n \"acc_norm_stderr,none\": 0.03139181076542942\n }\n}\n```" repo_url: https://huggingface.co/cluebbers/Llama-3.1-8B-paraphrase-type-generation-etpc leaderboard_url: '' point_of_contact: '' configs: - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_bbh_boolean_expressions data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_bbh_causal_judgement data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_bbh_date_understanding data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_bbh_disambiguation_qa data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_bbh_formal_fallacies data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_bbh_geometric_shapes data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_bbh_hyperbaton data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_bbh_logical_deduction_five_objects data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_bbh_logical_deduction_seven_objects data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_bbh_logical_deduction_three_objects data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_bbh_movie_recommendation data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_bbh_navigate data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_bbh_navigate_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_navigate_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_bbh_object_counting data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_bbh_object_counting_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_object_counting_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_bbh_penguins_in_a_table data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_bbh_reasoning_about_colored_objects data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_bbh_ruin_names data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_bbh_salient_translation_error_detection data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_bbh_snarks data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_bbh_snarks_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_snarks_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_bbh_sports_understanding data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_bbh_temporal_sequences data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_bbh_tracking_shuffled_objects_five_objects data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_bbh_tracking_shuffled_objects_seven_objects data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_bbh_tracking_shuffled_objects_three_objects data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_bbh_web_of_lies data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_gpqa_diamond data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_gpqa_diamond_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_diamond_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_gpqa_extended data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_gpqa_extended_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_extended_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_gpqa_main data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_gpqa_main_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_main_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_ifeval data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_ifeval_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_ifeval_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_math_algebra_hard data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_math_algebra_hard_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_math_algebra_hard_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_math_counting_and_prob_hard data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_math_geometry_hard data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_math_geometry_hard_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_math_geometry_hard_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_math_intermediate_algebra_hard data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_math_num_theory_hard data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_math_prealgebra_hard data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_math_precalculus_hard data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_mmlu_pro data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_mmlu_pro_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_mmlu_pro_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_musr_murder_mysteries data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_musr_object_placements data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_musr_object_placements_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_musr_object_placements_2024-11-19T21-27-59.247727.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_musr_team_allocation data_files: - split: 2024_11_19T21_27_59.247727 path: - '**/samples_leaderboard_musr_team_allocation_2024-11-19T21-27-59.247727.jsonl' - split: latest path: - '**/samples_leaderboard_musr_team_allocation_2024-11-19T21-27-59.247727.jsonl' --- # Dataset Card for Evaluation run of cluebbers/Llama-3.1-8B-paraphrase-type-generation-etpc <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [cluebbers/Llama-3.1-8B-paraphrase-type-generation-etpc](https://huggingface.co/cluebbers/Llama-3.1-8B-paraphrase-type-generation-etpc) The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run. To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset( "open-llm-leaderboard/cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc-details", name="cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc__leaderboard_bbh_boolean_expressions", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-11-19T21-27-59.247727](https://huggingface.co/datasets/open-llm-leaderboard/cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc-details/blob/main/cluebbers__Llama-3.1-8B-paraphrase-type-generation-etpc/results_2024-11-19T21-27-59.247727.json) (note that there might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "leaderboard": { "acc_norm,none": 0.36619535607731224, "acc_norm_stderr,none": 0.0052734901127333995, "prompt_level_loose_acc,none": 0.08317929759704251, "prompt_level_loose_acc_stderr,none": 0.01188373476400857, "inst_level_strict_acc,none": 0.17146282973621102, "inst_level_strict_acc_stderr,none": "N/A", "acc,none": 0.25556848404255317, "acc_stderr,none": 0.003976629780041046, "prompt_level_strict_acc,none": 0.07024029574861368, "prompt_level_strict_acc_stderr,none": 0.010997187906692914, "exact_match,none": 0.004531722054380665, "exact_match_stderr,none": 0.0018490874508892755, "inst_level_loose_acc,none": 0.19184652278177458, "inst_level_loose_acc_stderr,none": "N/A", "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.37858010762020483, "acc_norm_stderr,none": 0.006105180850940205, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.764, "acc_norm_stderr,none": 0.026909337594953852 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.5294117647058824, "acc_norm_stderr,none": 0.03659829510813266 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.32, "acc_norm_stderr,none": 0.029561724955240978 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.456, "acc_norm_stderr,none": 0.031563285061213475 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.488, "acc_norm_stderr,none": 0.03167708558254714 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.332, "acc_norm_stderr,none": 0.029844039047465857 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.552, "acc_norm_stderr,none": 0.03151438761115348 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.284, "acc_norm_stderr,none": 0.02857695873043744 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.284, "acc_norm_stderr,none": 0.02857695873043744 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.416, "acc_norm_stderr,none": 0.031235856237014505 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.496, "acc_norm_stderr,none": 0.0316851985511992 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.42, "acc_norm_stderr,none": 0.03127799950463661 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.368, "acc_norm_stderr,none": 0.03056207062099311 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.2054794520547945, "acc_norm_stderr,none": 0.03355465401072847 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.26, "acc_norm_stderr,none": 0.027797315752644335 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.248, "acc_norm_stderr,none": 0.027367497504863593 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.26, "acc_norm_stderr,none": 0.027797315752644335 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.4550561797752809, "acc_norm_stderr,none": 0.03743016495716991 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.58, "acc_norm_stderr,none": 0.03127799950463661 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.256, "acc_norm_stderr,none": 0.027657108718204846 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.192, "acc_norm_stderr,none": 0.024960691989171963 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.128, "acc_norm_stderr,none": 0.021172081336336534 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.292, "acc_norm_stderr,none": 0.02881432040220563 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.488, "acc_norm_stderr,none": 0.03167708558254714 }, "leaderboard_gpqa": { "acc_norm,none": 0.2651006711409396, "acc_norm_stderr,none": 0.012799282786329112, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.2777777777777778, "acc_norm_stderr,none": 0.03191178226713548 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.2600732600732601, "acc_norm_stderr,none": 0.018790743352015988 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.265625, "acc_norm_stderr,none": 0.02089005840079951 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.07024029574861368, "prompt_level_strict_acc_stderr,none": 0.010997187906692914, "inst_level_strict_acc,none": 0.17146282973621102, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.08317929759704251, "prompt_level_loose_acc_stderr,none": 0.01188373476400857, "inst_level_loose_acc,none": 0.19184652278177458, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.004531722054380665, "exact_match_stderr,none": 0.0018490874508892755, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.003257328990228013, "exact_match_stderr,none": 0.003257328990228013 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.008130081300813009, "exact_match_stderr,none": 0.008130081300813007 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.007575757575757576, "exact_match_stderr,none": 0.007575757575757577 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.006493506493506494, "exact_match_stderr,none": 0.006493506493506494 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.0051813471502590676, "exact_match_stderr,none": 0.0051813471502590676 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.007407407407407408, "exact_match_stderr,none": 0.007407407407407408 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.25556848404255317, "acc_stderr,none": 0.003976629780041046 }, "leaderboard_musr": { "acc_norm,none": 0.4312169312169312, "acc_norm_stderr,none": 0.01788862311572941, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.512, "acc_norm_stderr,none": 0.03167708558254714 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.3515625, "acc_norm_stderr,none": 0.029899590697818237 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.432, "acc_norm_stderr,none": 0.03139181076542942 } }, "leaderboard": { "acc_norm,none": 0.36619535607731224, "acc_norm_stderr,none": 0.0052734901127333995, "prompt_level_loose_acc,none": 0.08317929759704251, "prompt_level_loose_acc_stderr,none": 0.01188373476400857, "inst_level_strict_acc,none": 0.17146282973621102, "inst_level_strict_acc_stderr,none": "N/A", "acc,none": 0.25556848404255317, "acc_stderr,none": 0.003976629780041046, "prompt_level_strict_acc,none": 0.07024029574861368, "prompt_level_strict_acc_stderr,none": 0.010997187906692914, "exact_match,none": 0.004531722054380665, "exact_match_stderr,none": 0.0018490874508892755, "inst_level_loose_acc,none": 0.19184652278177458, "inst_level_loose_acc_stderr,none": "N/A", "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.37858010762020483, "acc_norm_stderr,none": 0.006105180850940205, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.764, "acc_norm_stderr,none": 0.026909337594953852 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.5294117647058824, "acc_norm_stderr,none": 0.03659829510813266 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.32, "acc_norm_stderr,none": 0.029561724955240978 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.456, "acc_norm_stderr,none": 0.031563285061213475 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.488, "acc_norm_stderr,none": 0.03167708558254714 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.332, "acc_norm_stderr,none": 0.029844039047465857 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.552, "acc_norm_stderr,none": 0.03151438761115348 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.284, "acc_norm_stderr,none": 0.02857695873043744 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.284, "acc_norm_stderr,none": 0.02857695873043744 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.416, "acc_norm_stderr,none": 0.031235856237014505 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.496, "acc_norm_stderr,none": 0.0316851985511992 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.42, "acc_norm_stderr,none": 0.03127799950463661 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.368, "acc_norm_stderr,none": 0.03056207062099311 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.2054794520547945, "acc_norm_stderr,none": 0.03355465401072847 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.26, "acc_norm_stderr,none": 0.027797315752644335 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.248, "acc_norm_stderr,none": 0.027367497504863593 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.26, "acc_norm_stderr,none": 0.027797315752644335 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.4550561797752809, "acc_norm_stderr,none": 0.03743016495716991 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.58, "acc_norm_stderr,none": 0.03127799950463661 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.256, "acc_norm_stderr,none": 0.027657108718204846 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.192, "acc_norm_stderr,none": 0.024960691989171963 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.128, "acc_norm_stderr,none": 0.021172081336336534 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.292, "acc_norm_stderr,none": 0.02881432040220563 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.488, "acc_norm_stderr,none": 0.03167708558254714 }, "leaderboard_gpqa": { "acc_norm,none": 0.2651006711409396, "acc_norm_stderr,none": 0.012799282786329112, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.2777777777777778, "acc_norm_stderr,none": 0.03191178226713548 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.2600732600732601, "acc_norm_stderr,none": 0.018790743352015988 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.265625, "acc_norm_stderr,none": 0.02089005840079951 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.07024029574861368, "prompt_level_strict_acc_stderr,none": 0.010997187906692914, "inst_level_strict_acc,none": 0.17146282973621102, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.08317929759704251, "prompt_level_loose_acc_stderr,none": 0.01188373476400857, "inst_level_loose_acc,none": 0.19184652278177458, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.004531722054380665, "exact_match_stderr,none": 0.0018490874508892755, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.003257328990228013, "exact_match_stderr,none": 0.003257328990228013 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.008130081300813009, "exact_match_stderr,none": 0.008130081300813007 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.007575757575757576, "exact_match_stderr,none": 0.007575757575757577 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.006493506493506494, "exact_match_stderr,none": 0.006493506493506494 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.0051813471502590676, "exact_match_stderr,none": 0.0051813471502590676 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.007407407407407408, "exact_match_stderr,none": 0.007407407407407408 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.25556848404255317, "acc_stderr,none": 0.003976629780041046 }, "leaderboard_musr": { "acc_norm,none": 0.4312169312169312, "acc_norm_stderr,none": 0.01788862311572941, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.512, "acc_norm_stderr,none": 0.03167708558254714 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.3515625, "acc_norm_stderr,none": 0.029899590697818237 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.432, "acc_norm_stderr,none": 0.03139181076542942 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - 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open-llm-leaderboard/vonjack__SmolLM2-1.7B-Merged-details

open-llm-leaderboard

--- pretty_name: Evaluation run of vonjack/SmolLM2-1.7B-Merged dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [vonjack/SmolLM2-1.7B-Merged](https://huggingface.co/vonjack/SmolLM2-1.7B-Merged)\n\ The dataset is composed of 38 configuration(s), each one corresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can\ \ be found as a specific split in each configuration, the split being named using\ \ the timestamp of the run.The \"train\" split is always pointing to the latest\ \ results.\n\nAn additional configuration \"results\" store all the aggregated results\ \ of the run.\n\nTo load the details from a run, you can for instance do the following:\n\ ```python\nfrom datasets import load_dataset\ndata = load_dataset(\n\t\"open-llm-leaderboard/vonjack__SmolLM2-1.7B-Merged-details\"\ ,\n\tname=\"vonjack__SmolLM2-1.7B-Merged__leaderboard_bbh_boolean_expressions\"\ ,\n\tsplit=\"latest\"\n)\n```\n\n## Latest results\n\nThese are the [latest results\ \ from run 2024-11-19T21-44-26.742480](https://huggingface.co/datasets/open-llm-leaderboard/vonjack__SmolLM2-1.7B-Merged-details/blob/main/vonjack__SmolLM2-1.7B-Merged/results_2024-11-19T21-44-26.742480.json)\ \ (note that there might be results for other tasks in the repos if successive evals\ \ didn't cover the same tasks. You find each in the results and the \"latest\" split\ \ for each eval):\n\n```python\n{\n \"all\": {\n \"leaderboard\": {\n\ \ \"prompt_level_strict_acc,none\": 0.3031423290203327,\n \ \ \"prompt_level_strict_acc_stderr,none\": 0.019778732375985417,\n \"\ inst_level_loose_acc,none\": 0.4592326139088729,\n \"inst_level_loose_acc_stderr,none\"\ : \"N/A\",\n \"exact_match,none\": 0.045317220543806644,\n \ \ \"exact_match_stderr,none\": 0.005596079941752634,\n \"inst_level_strict_acc,none\"\ : 0.4364508393285372,\n \"inst_level_strict_acc_stderr,none\": \"N/A\"\ ,\n \"prompt_level_loose_acc,none\": 0.32162661737523107,\n \ \ \"prompt_level_loose_acc_stderr,none\": 0.020100807813645375,\n \"\ acc,none\": 0.2047872340425532,\n \"acc_stderr,none\": 0.0036791055578188313,\n\ \ \"acc_norm,none\": 0.34193799455182255,\n \"acc_norm_stderr,none\"\ : 0.00513143649230213,\n \"alias\": \"leaderboard\"\n },\n \ \ \"leaderboard_bbh\": {\n \"acc_norm,none\": 0.3551466759243187,\n\ \ \"acc_norm_stderr,none\": 0.0059334827105570745,\n \"alias\"\ : \" - leaderboard_bbh\"\n },\n \"leaderboard_bbh_boolean_expressions\"\ : {\n \"alias\": \" - leaderboard_bbh_boolean_expressions\",\n \ \ \"acc_norm,none\": 0.68,\n \"acc_norm_stderr,none\": 0.02956172495524098\n\ \ },\n \"leaderboard_bbh_causal_judgement\": {\n \"alias\"\ : \" - leaderboard_bbh_causal_judgement\",\n \"acc_norm,none\": 0.5721925133689839,\n\ \ \"acc_norm_stderr,none\": 0.03627762136497335\n },\n \ \ \"leaderboard_bbh_date_understanding\": {\n \"alias\": \" - leaderboard_bbh_date_understanding\"\ ,\n \"acc_norm,none\": 0.472,\n \"acc_norm_stderr,none\":\ \ 0.031636489531544396\n },\n \"leaderboard_bbh_disambiguation_qa\"\ : {\n \"alias\": \" - leaderboard_bbh_disambiguation_qa\",\n \ \ \"acc_norm,none\": 0.316,\n \"acc_norm_stderr,none\": 0.029462657598578648\n\ \ },\n \"leaderboard_bbh_formal_fallacies\": {\n \"alias\"\ : \" - leaderboard_bbh_formal_fallacies\",\n \"acc_norm,none\": 0.536,\n\ \ \"acc_norm_stderr,none\": 0.031603975145223735\n },\n \ \ \"leaderboard_bbh_geometric_shapes\": {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\"\ ,\n \"acc_norm,none\": 0.328,\n \"acc_norm_stderr,none\":\ \ 0.029752391824475363\n },\n \"leaderboard_bbh_hyperbaton\": {\n\ \ \"alias\": \" - leaderboard_bbh_hyperbaton\",\n \"acc_norm,none\"\ : 0.496,\n \"acc_norm_stderr,none\": 0.0316851985511992\n },\n\ \ \"leaderboard_bbh_logical_deduction_five_objects\": {\n \"alias\"\ : \" - leaderboard_bbh_logical_deduction_five_objects\",\n \"acc_norm,none\"\ : 0.244,\n \"acc_norm_stderr,none\": 0.02721799546455311\n },\n\ \ \"leaderboard_bbh_logical_deduction_seven_objects\": {\n \"\ alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\",\n \"\ acc_norm,none\": 0.156,\n \"acc_norm_stderr,none\": 0.022995023034068682\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n\ \ \"acc_norm,none\": 0.408,\n \"acc_norm_stderr,none\": 0.031145209846548512\n\ \ },\n \"leaderboard_bbh_movie_recommendation\": {\n \"\ alias\": \" - leaderboard_bbh_movie_recommendation\",\n \"acc_norm,none\"\ : 0.524,\n \"acc_norm_stderr,none\": 0.03164968895968774\n },\n\ \ \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.464,\n \"acc_norm_stderr,none\":\ \ 0.03160397514522374\n },\n \"leaderboard_bbh_object_counting\":\ \ {\n \"alias\": \" - leaderboard_bbh_object_counting\",\n \ \ \"acc_norm,none\": 0.268,\n \"acc_norm_stderr,none\": 0.02806876238252672\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"\ alias\": \" - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\"\ : 0.3219178082191781,\n \"acc_norm_stderr,none\": 0.038799816296271356\n\ \ },\n \"leaderboard_bbh_reasoning_about_colored_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\",\n\ \ \"acc_norm,none\": 0.216,\n \"acc_norm_stderr,none\": 0.02607865766373279\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \"\ \ - leaderboard_bbh_ruin_names\",\n \"acc_norm,none\": 0.136,\n \ \ \"acc_norm_stderr,none\": 0.021723342617052086\n },\n \"\ leaderboard_bbh_salient_translation_error_detection\": {\n \"alias\"\ : \" - leaderboard_bbh_salient_translation_error_detection\",\n \"acc_norm,none\"\ : 0.312,\n \"acc_norm_stderr,none\": 0.02936106757521985\n },\n\ \ \"leaderboard_bbh_snarks\": {\n \"alias\": \" - leaderboard_bbh_snarks\"\ ,\n \"acc_norm,none\": 0.5056179775280899,\n \"acc_norm_stderr,none\"\ : 0.03757992900475984\n },\n \"leaderboard_bbh_sports_understanding\"\ : {\n \"alias\": \" - leaderboard_bbh_sports_understanding\",\n \ \ \"acc_norm,none\": 0.488,\n \"acc_norm_stderr,none\": 0.03167708558254714\n\ \ },\n \"leaderboard_bbh_temporal_sequences\": {\n \"alias\"\ : \" - leaderboard_bbh_temporal_sequences\",\n \"acc_norm,none\": 0.052,\n\ \ \"acc_norm_stderr,none\": 0.014070391025641678\n },\n \ \ \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \"\ alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\",\n \ \ \"acc_norm,none\": 0.128,\n \"acc_norm_stderr,none\": 0.021172081336336534\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.14,\n \"acc_norm_stderr,none\": 0.021989409645240245\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.356,\n \"acc_norm_stderr,none\":\ \ 0.0303436806571532\n },\n \"leaderboard_bbh_web_of_lies\": {\n \ \ \"alias\": \" - leaderboard_bbh_web_of_lies\",\n \"acc_norm,none\"\ : 0.488,\n \"acc_norm_stderr,none\": 0.03167708558254714\n },\n\ \ \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.27936241610738255,\n\ \ \"acc_norm_stderr,none\": 0.01301112498383348,\n \"alias\"\ : \" - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n\ \ \"alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\"\ : 0.2878787878787879,\n \"acc_norm_stderr,none\": 0.03225883512300998\n\ \ },\n \"leaderboard_gpqa_extended\": {\n \"alias\": \"\ \ - leaderboard_gpqa_extended\",\n \"acc_norm,none\": 0.28205128205128205,\n\ \ \"acc_norm_stderr,none\": 0.019275803929950375\n },\n \ \ \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.27232142857142855,\n \"acc_norm_stderr,none\"\ : 0.02105508212932411\n },\n \"leaderboard_ifeval\": {\n \ \ \"alias\": \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\"\ : 0.3031423290203327,\n \"prompt_level_strict_acc_stderr,none\": 0.019778732375985417,\n\ \ \"inst_level_strict_acc,none\": 0.4364508393285372,\n \"\ inst_level_strict_acc_stderr,none\": \"N/A\",\n \"prompt_level_loose_acc,none\"\ : 0.32162661737523107,\n \"prompt_level_loose_acc_stderr,none\": 0.020100807813645375,\n\ \ \"inst_level_loose_acc,none\": 0.4592326139088729,\n \"\ inst_level_loose_acc_stderr,none\": \"N/A\"\n },\n \"leaderboard_math_hard\"\ : {\n \"exact_match,none\": 0.045317220543806644,\n \"exact_match_stderr,none\"\ : 0.005596079941752634,\n \"alias\": \" - leaderboard_math_hard\"\n \ \ },\n \"leaderboard_math_algebra_hard\": {\n \"alias\"\ : \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\": 0.11074918566775244,\n\ \ \"exact_match_stderr,none\": 0.017939969209400983\n },\n \ \ \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\": \"\ \ - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.008130081300813009,\n \"exact_match_stderr,none\": 0.008130081300813007\n\ \ },\n \"leaderboard_math_geometry_hard\": {\n \"alias\"\ : \" - leaderboard_math_geometry_hard\",\n \"exact_match,none\": 0.007575757575757576,\n\ \ \"exact_match_stderr,none\": 0.007575757575757577\n },\n \ \ \"leaderboard_math_intermediate_algebra_hard\": {\n \"alias\":\ \ \" - leaderboard_math_intermediate_algebra_hard\",\n \"exact_match,none\"\ : 0.017857142857142856,\n \"exact_match_stderr,none\": 0.007928503387888855\n\ \ },\n \"leaderboard_math_num_theory_hard\": {\n \"alias\"\ : \" - leaderboard_math_num_theory_hard\",\n \"exact_match,none\": 0.012987012987012988,\n\ \ \"exact_match_stderr,none\": 0.009153145279150204\n },\n \ \ \"leaderboard_math_prealgebra_hard\": {\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\ ,\n \"exact_match,none\": 0.08808290155440414,\n \"exact_match_stderr,none\"\ : 0.020453746601601056\n },\n \"leaderboard_math_precalculus_hard\"\ : {\n \"alias\": \" - leaderboard_math_precalculus_hard\",\n \ \ \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\": 0.0\n\ \ },\n \"leaderboard_mmlu_pro\": {\n \"alias\": \" - leaderboard_mmlu_pro\"\ ,\n \"acc,none\": 0.2047872340425532,\n \"acc_stderr,none\"\ : 0.0036791055578188313\n },\n \"leaderboard_musr\": {\n \ \ \"acc_norm,none\": 0.33994708994708994,\n \"acc_norm_stderr,none\"\ : 0.016513530548897988,\n \"alias\": \" - leaderboard_musr\"\n \ \ },\n \"leaderboard_musr_murder_mysteries\": {\n \"alias\":\ \ \" - leaderboard_musr_murder_mysteries\",\n \"acc_norm,none\": 0.536,\n\ \ \"acc_norm_stderr,none\": 0.031603975145223735\n },\n \ \ \"leaderboard_musr_object_placements\": {\n \"alias\": \" - leaderboard_musr_object_placements\"\ ,\n \"acc_norm,none\": 0.234375,\n \"acc_norm_stderr,none\"\ : 0.02652733398834892\n },\n \"leaderboard_musr_team_allocation\"\ : {\n \"alias\": \" - leaderboard_musr_team_allocation\",\n \ \ \"acc_norm,none\": 0.252,\n \"acc_norm_stderr,none\": 0.027513851933031318\n\ \ }\n },\n \"leaderboard\": {\n \"prompt_level_strict_acc,none\"\ : 0.3031423290203327,\n \"prompt_level_strict_acc_stderr,none\": 0.019778732375985417,\n\ \ \"inst_level_loose_acc,none\": 0.4592326139088729,\n \"inst_level_loose_acc_stderr,none\"\ : \"N/A\",\n \"exact_match,none\": 0.045317220543806644,\n \"exact_match_stderr,none\"\ : 0.005596079941752634,\n \"inst_level_strict_acc,none\": 0.4364508393285372,\n\ \ \"inst_level_strict_acc_stderr,none\": \"N/A\",\n \"prompt_level_loose_acc,none\"\ : 0.32162661737523107,\n \"prompt_level_loose_acc_stderr,none\": 0.020100807813645375,\n\ \ \"acc,none\": 0.2047872340425532,\n \"acc_stderr,none\": 0.0036791055578188313,\n\ \ \"acc_norm,none\": 0.34193799455182255,\n \"acc_norm_stderr,none\"\ : 0.00513143649230213,\n \"alias\": \"leaderboard\"\n },\n \"leaderboard_bbh\"\ : {\n \"acc_norm,none\": 0.3551466759243187,\n \"acc_norm_stderr,none\"\ : 0.0059334827105570745,\n \"alias\": \" - leaderboard_bbh\"\n },\n \ \ \"leaderboard_bbh_boolean_expressions\": {\n \"alias\": \" - leaderboard_bbh_boolean_expressions\"\ ,\n \"acc_norm,none\": 0.68,\n \"acc_norm_stderr,none\": 0.02956172495524098\n\ \ },\n \"leaderboard_bbh_causal_judgement\": {\n \"alias\": \" - leaderboard_bbh_causal_judgement\"\ ,\n \"acc_norm,none\": 0.5721925133689839,\n \"acc_norm_stderr,none\"\ : 0.03627762136497335\n },\n \"leaderboard_bbh_date_understanding\": {\n \ \ \"alias\": \" - leaderboard_bbh_date_understanding\",\n \"acc_norm,none\"\ : 0.472,\n \"acc_norm_stderr,none\": 0.031636489531544396\n },\n \"\ leaderboard_bbh_disambiguation_qa\": {\n \"alias\": \" - leaderboard_bbh_disambiguation_qa\"\ ,\n \"acc_norm,none\": 0.316,\n \"acc_norm_stderr,none\": 0.029462657598578648\n\ \ },\n \"leaderboard_bbh_formal_fallacies\": {\n \"alias\": \" - leaderboard_bbh_formal_fallacies\"\ ,\n \"acc_norm,none\": 0.536,\n \"acc_norm_stderr,none\": 0.031603975145223735\n\ \ },\n \"leaderboard_bbh_geometric_shapes\": {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\"\ ,\n \"acc_norm,none\": 0.328,\n \"acc_norm_stderr,none\": 0.029752391824475363\n\ \ },\n \"leaderboard_bbh_hyperbaton\": {\n \"alias\": \" - leaderboard_bbh_hyperbaton\"\ ,\n \"acc_norm,none\": 0.496,\n \"acc_norm_stderr,none\": 0.0316851985511992\n\ \ },\n \"leaderboard_bbh_logical_deduction_five_objects\": {\n \"alias\"\ : \" - leaderboard_bbh_logical_deduction_five_objects\",\n \"acc_norm,none\"\ : 0.244,\n \"acc_norm_stderr,none\": 0.02721799546455311\n },\n \"\ leaderboard_bbh_logical_deduction_seven_objects\": {\n \"alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\"\ ,\n \"acc_norm,none\": 0.156,\n \"acc_norm_stderr,none\": 0.022995023034068682\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \"\ alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n \"acc_norm,none\"\ : 0.408,\n \"acc_norm_stderr,none\": 0.031145209846548512\n },\n \"\ leaderboard_bbh_movie_recommendation\": {\n \"alias\": \" - leaderboard_bbh_movie_recommendation\"\ ,\n \"acc_norm,none\": 0.524,\n \"acc_norm_stderr,none\": 0.03164968895968774\n\ \ },\n \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.464,\n \"acc_norm_stderr,none\": 0.03160397514522374\n\ \ },\n \"leaderboard_bbh_object_counting\": {\n \"alias\": \" - leaderboard_bbh_object_counting\"\ ,\n \"acc_norm,none\": 0.268,\n \"acc_norm_stderr,none\": 0.02806876238252672\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"alias\": \" \ \ - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\": 0.3219178082191781,\n\ \ \"acc_norm_stderr,none\": 0.038799816296271356\n },\n \"leaderboard_bbh_reasoning_about_colored_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\"\ ,\n \"acc_norm,none\": 0.216,\n \"acc_norm_stderr,none\": 0.02607865766373279\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \" - leaderboard_bbh_ruin_names\"\ ,\n \"acc_norm,none\": 0.136,\n \"acc_norm_stderr,none\": 0.021723342617052086\n\ \ },\n \"leaderboard_bbh_salient_translation_error_detection\": {\n \ \ \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\",\n \ \ \"acc_norm,none\": 0.312,\n \"acc_norm_stderr,none\": 0.02936106757521985\n\ \ },\n \"leaderboard_bbh_snarks\": {\n \"alias\": \" - leaderboard_bbh_snarks\"\ ,\n \"acc_norm,none\": 0.5056179775280899,\n \"acc_norm_stderr,none\"\ : 0.03757992900475984\n },\n \"leaderboard_bbh_sports_understanding\": {\n\ \ \"alias\": \" - leaderboard_bbh_sports_understanding\",\n \"acc_norm,none\"\ : 0.488,\n \"acc_norm_stderr,none\": 0.03167708558254714\n },\n \"\ leaderboard_bbh_temporal_sequences\": {\n \"alias\": \" - leaderboard_bbh_temporal_sequences\"\ ,\n \"acc_norm,none\": 0.052,\n \"acc_norm_stderr,none\": 0.014070391025641678\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\"\ ,\n \"acc_norm,none\": 0.128,\n \"acc_norm_stderr,none\": 0.021172081336336534\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.14,\n \"acc_norm_stderr,none\": 0.021989409645240245\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.356,\n \"acc_norm_stderr,none\": 0.0303436806571532\n\ \ },\n \"leaderboard_bbh_web_of_lies\": {\n \"alias\": \" - leaderboard_bbh_web_of_lies\"\ ,\n \"acc_norm,none\": 0.488,\n \"acc_norm_stderr,none\": 0.03167708558254714\n\ \ },\n \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.27936241610738255,\n\ \ \"acc_norm_stderr,none\": 0.01301112498383348,\n \"alias\": \" -\ \ leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n \"alias\"\ : \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\": 0.2878787878787879,\n\ \ \"acc_norm_stderr,none\": 0.03225883512300998\n },\n \"leaderboard_gpqa_extended\"\ : {\n \"alias\": \" - leaderboard_gpqa_extended\",\n \"acc_norm,none\"\ : 0.28205128205128205,\n \"acc_norm_stderr,none\": 0.019275803929950375\n\ \ },\n \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.27232142857142855,\n \"acc_norm_stderr,none\"\ : 0.02105508212932411\n },\n \"leaderboard_ifeval\": {\n \"alias\"\ : \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\": 0.3031423290203327,\n\ \ \"prompt_level_strict_acc_stderr,none\": 0.019778732375985417,\n \ \ \"inst_level_strict_acc,none\": 0.4364508393285372,\n \"inst_level_strict_acc_stderr,none\"\ : \"N/A\",\n \"prompt_level_loose_acc,none\": 0.32162661737523107,\n \ \ \"prompt_level_loose_acc_stderr,none\": 0.020100807813645375,\n \"inst_level_loose_acc,none\"\ : 0.4592326139088729,\n \"inst_level_loose_acc_stderr,none\": \"N/A\"\n \ \ },\n \"leaderboard_math_hard\": {\n \"exact_match,none\": 0.045317220543806644,\n\ \ \"exact_match_stderr,none\": 0.005596079941752634,\n \"alias\":\ \ \" - leaderboard_math_hard\"\n },\n \"leaderboard_math_algebra_hard\": {\n\ \ \"alias\": \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\"\ : 0.11074918566775244,\n \"exact_match_stderr,none\": 0.017939969209400983\n\ \ },\n \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\":\ \ \" - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.008130081300813009,\n \"exact_match_stderr,none\": 0.008130081300813007\n\ \ },\n \"leaderboard_math_geometry_hard\": {\n \"alias\": \" - leaderboard_math_geometry_hard\"\ ,\n \"exact_match,none\": 0.007575757575757576,\n \"exact_match_stderr,none\"\ : 0.007575757575757577\n },\n \"leaderboard_math_intermediate_algebra_hard\"\ : {\n \"alias\": \" - leaderboard_math_intermediate_algebra_hard\",\n \ \ \"exact_match,none\": 0.017857142857142856,\n \"exact_match_stderr,none\"\ : 0.007928503387888855\n },\n \"leaderboard_math_num_theory_hard\": {\n \ \ \"alias\": \" - leaderboard_math_num_theory_hard\",\n \"exact_match,none\"\ : 0.012987012987012988,\n \"exact_match_stderr,none\": 0.009153145279150204\n\ \ },\n \"leaderboard_math_prealgebra_hard\": {\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\ ,\n \"exact_match,none\": 0.08808290155440414,\n \"exact_match_stderr,none\"\ : 0.020453746601601056\n },\n \"leaderboard_math_precalculus_hard\": {\n \ \ \"alias\": \" - leaderboard_math_precalculus_hard\",\n \"exact_match,none\"\ : 0.0,\n \"exact_match_stderr,none\": 0.0\n },\n \"leaderboard_mmlu_pro\"\ : {\n \"alias\": \" - leaderboard_mmlu_pro\",\n \"acc,none\": 0.2047872340425532,\n\ \ \"acc_stderr,none\": 0.0036791055578188313\n },\n \"leaderboard_musr\"\ : {\n \"acc_norm,none\": 0.33994708994708994,\n \"acc_norm_stderr,none\"\ : 0.016513530548897988,\n \"alias\": \" - leaderboard_musr\"\n },\n \ \ \"leaderboard_musr_murder_mysteries\": {\n \"alias\": \" - leaderboard_musr_murder_mysteries\"\ ,\n \"acc_norm,none\": 0.536,\n \"acc_norm_stderr,none\": 0.031603975145223735\n\ \ },\n \"leaderboard_musr_object_placements\": {\n \"alias\": \" -\ \ leaderboard_musr_object_placements\",\n \"acc_norm,none\": 0.234375,\n\ \ \"acc_norm_stderr,none\": 0.02652733398834892\n },\n \"leaderboard_musr_team_allocation\"\ : {\n \"alias\": \" - leaderboard_musr_team_allocation\",\n \"acc_norm,none\"\ : 0.252,\n \"acc_norm_stderr,none\": 0.027513851933031318\n }\n}\n```" repo_url: https://huggingface.co/vonjack/SmolLM2-1.7B-Merged leaderboard_url: '' point_of_contact: '' configs: - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_bbh_boolean_expressions data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_bbh_causal_judgement data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_bbh_date_understanding data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_bbh_disambiguation_qa data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_bbh_formal_fallacies data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_bbh_geometric_shapes data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_bbh_hyperbaton data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_bbh_logical_deduction_five_objects data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_bbh_logical_deduction_seven_objects data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_bbh_logical_deduction_three_objects data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_bbh_movie_recommendation data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_bbh_navigate data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_bbh_navigate_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_navigate_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_bbh_object_counting data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_bbh_object_counting_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_object_counting_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_bbh_penguins_in_a_table data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_bbh_reasoning_about_colored_objects data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_bbh_ruin_names data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_bbh_salient_translation_error_detection data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_bbh_snarks data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_bbh_snarks_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_snarks_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_bbh_sports_understanding data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_bbh_temporal_sequences data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_bbh_tracking_shuffled_objects_five_objects data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_bbh_tracking_shuffled_objects_seven_objects data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_bbh_tracking_shuffled_objects_three_objects data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_bbh_web_of_lies data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_gpqa_diamond data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_gpqa_diamond_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_diamond_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_gpqa_extended data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_gpqa_extended_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_extended_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_gpqa_main data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_gpqa_main_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_main_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_ifeval data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_ifeval_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_ifeval_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_math_algebra_hard data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_math_algebra_hard_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_math_algebra_hard_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_math_counting_and_prob_hard data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_math_geometry_hard data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_math_geometry_hard_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_math_geometry_hard_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_math_intermediate_algebra_hard data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_math_num_theory_hard data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_math_prealgebra_hard data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_math_precalculus_hard data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_mmlu_pro data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_mmlu_pro_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_mmlu_pro_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_musr_murder_mysteries data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_musr_object_placements data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_musr_object_placements_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_musr_object_placements_2024-11-19T21-44-26.742480.jsonl' - config_name: vonjack__SmolLM2-1.7B-Merged__leaderboard_musr_team_allocation data_files: - split: 2024_11_19T21_44_26.742480 path: - '**/samples_leaderboard_musr_team_allocation_2024-11-19T21-44-26.742480.jsonl' - split: latest path: - '**/samples_leaderboard_musr_team_allocation_2024-11-19T21-44-26.742480.jsonl' --- # Dataset Card for Evaluation run of vonjack/SmolLM2-1.7B-Merged <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [vonjack/SmolLM2-1.7B-Merged](https://huggingface.co/vonjack/SmolLM2-1.7B-Merged) The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run. To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset( "open-llm-leaderboard/vonjack__SmolLM2-1.7B-Merged-details", name="vonjack__SmolLM2-1.7B-Merged__leaderboard_bbh_boolean_expressions", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-11-19T21-44-26.742480](https://huggingface.co/datasets/open-llm-leaderboard/vonjack__SmolLM2-1.7B-Merged-details/blob/main/vonjack__SmolLM2-1.7B-Merged/results_2024-11-19T21-44-26.742480.json) (note that there might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "leaderboard": { "prompt_level_strict_acc,none": 0.3031423290203327, "prompt_level_strict_acc_stderr,none": 0.019778732375985417, "inst_level_loose_acc,none": 0.4592326139088729, "inst_level_loose_acc_stderr,none": "N/A", "exact_match,none": 0.045317220543806644, "exact_match_stderr,none": 0.005596079941752634, "inst_level_strict_acc,none": 0.4364508393285372, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.32162661737523107, "prompt_level_loose_acc_stderr,none": 0.020100807813645375, "acc,none": 0.2047872340425532, "acc_stderr,none": 0.0036791055578188313, "acc_norm,none": 0.34193799455182255, "acc_norm_stderr,none": 0.00513143649230213, "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.3551466759243187, "acc_norm_stderr,none": 0.0059334827105570745, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.68, "acc_norm_stderr,none": 0.02956172495524098 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.5721925133689839, "acc_norm_stderr,none": 0.03627762136497335 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.472, "acc_norm_stderr,none": 0.031636489531544396 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.316, "acc_norm_stderr,none": 0.029462657598578648 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.536, "acc_norm_stderr,none": 0.031603975145223735 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.328, "acc_norm_stderr,none": 0.029752391824475363 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.496, "acc_norm_stderr,none": 0.0316851985511992 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.244, "acc_norm_stderr,none": 0.02721799546455311 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.156, "acc_norm_stderr,none": 0.022995023034068682 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.408, "acc_norm_stderr,none": 0.031145209846548512 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.524, "acc_norm_stderr,none": 0.03164968895968774 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.464, "acc_norm_stderr,none": 0.03160397514522374 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.268, "acc_norm_stderr,none": 0.02806876238252672 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.3219178082191781, "acc_norm_stderr,none": 0.038799816296271356 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.216, "acc_norm_stderr,none": 0.02607865766373279 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.136, "acc_norm_stderr,none": 0.021723342617052086 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.312, "acc_norm_stderr,none": 0.02936106757521985 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.5056179775280899, "acc_norm_stderr,none": 0.03757992900475984 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.488, "acc_norm_stderr,none": 0.03167708558254714 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.052, "acc_norm_stderr,none": 0.014070391025641678 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.128, "acc_norm_stderr,none": 0.021172081336336534 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.14, "acc_norm_stderr,none": 0.021989409645240245 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.356, "acc_norm_stderr,none": 0.0303436806571532 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.488, "acc_norm_stderr,none": 0.03167708558254714 }, "leaderboard_gpqa": { "acc_norm,none": 0.27936241610738255, "acc_norm_stderr,none": 0.01301112498383348, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.2878787878787879, "acc_norm_stderr,none": 0.03225883512300998 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.28205128205128205, "acc_norm_stderr,none": 0.019275803929950375 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.27232142857142855, "acc_norm_stderr,none": 0.02105508212932411 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.3031423290203327, "prompt_level_strict_acc_stderr,none": 0.019778732375985417, "inst_level_strict_acc,none": 0.4364508393285372, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.32162661737523107, "prompt_level_loose_acc_stderr,none": 0.020100807813645375, "inst_level_loose_acc,none": 0.4592326139088729, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.045317220543806644, "exact_match_stderr,none": 0.005596079941752634, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.11074918566775244, "exact_match_stderr,none": 0.017939969209400983 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.008130081300813009, "exact_match_stderr,none": 0.008130081300813007 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.007575757575757576, "exact_match_stderr,none": 0.007575757575757577 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.017857142857142856, "exact_match_stderr,none": 0.007928503387888855 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.012987012987012988, "exact_match_stderr,none": 0.009153145279150204 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.08808290155440414, "exact_match_stderr,none": 0.020453746601601056 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.2047872340425532, "acc_stderr,none": 0.0036791055578188313 }, "leaderboard_musr": { "acc_norm,none": 0.33994708994708994, "acc_norm_stderr,none": 0.016513530548897988, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.536, "acc_norm_stderr,none": 0.031603975145223735 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.234375, "acc_norm_stderr,none": 0.02652733398834892 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.252, "acc_norm_stderr,none": 0.027513851933031318 } }, "leaderboard": { "prompt_level_strict_acc,none": 0.3031423290203327, "prompt_level_strict_acc_stderr,none": 0.019778732375985417, "inst_level_loose_acc,none": 0.4592326139088729, "inst_level_loose_acc_stderr,none": "N/A", "exact_match,none": 0.045317220543806644, "exact_match_stderr,none": 0.005596079941752634, "inst_level_strict_acc,none": 0.4364508393285372, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.32162661737523107, "prompt_level_loose_acc_stderr,none": 0.020100807813645375, "acc,none": 0.2047872340425532, "acc_stderr,none": 0.0036791055578188313, "acc_norm,none": 0.34193799455182255, "acc_norm_stderr,none": 0.00513143649230213, "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.3551466759243187, "acc_norm_stderr,none": 0.0059334827105570745, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.68, "acc_norm_stderr,none": 0.02956172495524098 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.5721925133689839, "acc_norm_stderr,none": 0.03627762136497335 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.472, "acc_norm_stderr,none": 0.031636489531544396 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.316, "acc_norm_stderr,none": 0.029462657598578648 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.536, "acc_norm_stderr,none": 0.031603975145223735 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.328, "acc_norm_stderr,none": 0.029752391824475363 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.496, "acc_norm_stderr,none": 0.0316851985511992 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.244, "acc_norm_stderr,none": 0.02721799546455311 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.156, "acc_norm_stderr,none": 0.022995023034068682 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.408, "acc_norm_stderr,none": 0.031145209846548512 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.524, "acc_norm_stderr,none": 0.03164968895968774 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.464, "acc_norm_stderr,none": 0.03160397514522374 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.268, "acc_norm_stderr,none": 0.02806876238252672 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.3219178082191781, "acc_norm_stderr,none": 0.038799816296271356 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.216, "acc_norm_stderr,none": 0.02607865766373279 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.136, "acc_norm_stderr,none": 0.021723342617052086 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.312, "acc_norm_stderr,none": 0.02936106757521985 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.5056179775280899, "acc_norm_stderr,none": 0.03757992900475984 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.488, "acc_norm_stderr,none": 0.03167708558254714 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.052, "acc_norm_stderr,none": 0.014070391025641678 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.128, "acc_norm_stderr,none": 0.021172081336336534 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.14, "acc_norm_stderr,none": 0.021989409645240245 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.356, "acc_norm_stderr,none": 0.0303436806571532 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.488, "acc_norm_stderr,none": 0.03167708558254714 }, "leaderboard_gpqa": { "acc_norm,none": 0.27936241610738255, "acc_norm_stderr,none": 0.01301112498383348, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.2878787878787879, "acc_norm_stderr,none": 0.03225883512300998 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.28205128205128205, "acc_norm_stderr,none": 0.019275803929950375 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.27232142857142855, "acc_norm_stderr,none": 0.02105508212932411 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.3031423290203327, "prompt_level_strict_acc_stderr,none": 0.019778732375985417, "inst_level_strict_acc,none": 0.4364508393285372, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.32162661737523107, "prompt_level_loose_acc_stderr,none": 0.020100807813645375, "inst_level_loose_acc,none": 0.4592326139088729, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.045317220543806644, "exact_match_stderr,none": 0.005596079941752634, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.11074918566775244, "exact_match_stderr,none": 0.017939969209400983 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.008130081300813009, "exact_match_stderr,none": 0.008130081300813007 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.007575757575757576, "exact_match_stderr,none": 0.007575757575757577 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.017857142857142856, "exact_match_stderr,none": 0.007928503387888855 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.012987012987012988, "exact_match_stderr,none": 0.009153145279150204 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.08808290155440414, "exact_match_stderr,none": 0.020453746601601056 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.2047872340425532, "acc_stderr,none": 0.0036791055578188313 }, "leaderboard_musr": { "acc_norm,none": 0.33994708994708994, "acc_norm_stderr,none": 0.016513530548897988, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.536, "acc_norm_stderr,none": 0.031603975145223735 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.234375, "acc_norm_stderr,none": 0.02652733398834892 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.252, "acc_norm_stderr,none": 0.027513851933031318 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - 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open-llm-leaderboard/cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid-details

open-llm-leaderboard

--- pretty_name: Evaluation run of cluebbers/Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [cluebbers/Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid](https://huggingface.co/cluebbers/Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid)\n\ The dataset is composed of 38 configuration(s), each one corresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can\ \ be found as a specific split in each configuration, the split being named using\ \ the timestamp of the run.The \"train\" split is always pointing to the latest\ \ results.\n\nAn additional configuration \"results\" store all the aggregated results\ \ of the run.\n\nTo load the details from a run, you can for instance do the following:\n\ ```python\nfrom datasets import load_dataset\ndata = load_dataset(\n\t\"open-llm-leaderboard/cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid-details\"\ ,\n\tname=\"cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_bbh_boolean_expressions\"\ ,\n\tsplit=\"latest\"\n)\n```\n\n## Latest results\n\nThese are the [latest results\ \ from run 2024-11-19T22-02-56.232495](https://huggingface.co/datasets/open-llm-leaderboard/cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid-details/blob/main/cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid/results_2024-11-19T22-02-56.232495.json)\ \ (note that there might be results for other tasks in the repos if successive evals\ \ didn't cover the same tasks. You find each in the results and the \"latest\" split\ \ for each eval):\n\n```python\n{\n \"all\": {\n \"leaderboard\": {\n\ \ \"prompt_level_loose_acc,none\": 0.09242144177449169,\n \ \ \"prompt_level_loose_acc_stderr,none\": 0.012463258511767319,\n \"\ inst_level_loose_acc,none\": 0.19664268585131894,\n \"inst_level_loose_acc_stderr,none\"\ : \"N/A\",\n \"acc,none\": 0.2562333776595745,\n \"acc_stderr,none\"\ : 0.003980020690139148,\n \"exact_match,none\": 0.006797583081570997,\n\ \ \"exact_match_stderr,none\": 0.0022605202818857095,\n \"\ acc_norm,none\": 0.3673628226747957,\n \"acc_norm_stderr,none\": 0.005279715115717286,\n\ \ \"prompt_level_strict_acc,none\": 0.08502772643253234,\n \ \ \"prompt_level_strict_acc_stderr,none\": 0.012002932679468982,\n \"\ inst_level_strict_acc,none\": 0.17865707434052758,\n \"inst_level_strict_acc_stderr,none\"\ : \"N/A\",\n \"alias\": \"leaderboard\"\n },\n \"leaderboard_bbh\"\ : {\n \"acc_norm,none\": 0.37962159347335533,\n \"acc_norm_stderr,none\"\ : 0.006111875587492996,\n \"alias\": \" - leaderboard_bbh\"\n \ \ },\n \"leaderboard_bbh_boolean_expressions\": {\n \"alias\"\ : \" - leaderboard_bbh_boolean_expressions\",\n \"acc_norm,none\": 0.752,\n\ \ \"acc_norm_stderr,none\": 0.027367497504863593\n },\n \ \ \"leaderboard_bbh_causal_judgement\": {\n \"alias\": \" - leaderboard_bbh_causal_judgement\"\ ,\n \"acc_norm,none\": 0.5240641711229946,\n \"acc_norm_stderr,none\"\ : 0.03661929361528698\n },\n \"leaderboard_bbh_date_understanding\"\ : {\n \"alias\": \" - leaderboard_bbh_date_understanding\",\n \ \ \"acc_norm,none\": 0.324,\n \"acc_norm_stderr,none\": 0.029658294924545567\n\ \ },\n \"leaderboard_bbh_disambiguation_qa\": {\n \"alias\"\ : \" - leaderboard_bbh_disambiguation_qa\",\n \"acc_norm,none\": 0.464,\n\ \ \"acc_norm_stderr,none\": 0.03160397514522374\n },\n \ \ \"leaderboard_bbh_formal_fallacies\": {\n \"alias\": \" - leaderboard_bbh_formal_fallacies\"\ ,\n \"acc_norm,none\": 0.5,\n \"acc_norm_stderr,none\": 0.031686212526223896\n\ \ },\n \"leaderboard_bbh_geometric_shapes\": {\n \"alias\"\ : \" - leaderboard_bbh_geometric_shapes\",\n \"acc_norm,none\": 0.324,\n\ \ \"acc_norm_stderr,none\": 0.029658294924545567\n },\n \ \ \"leaderboard_bbh_hyperbaton\": {\n \"alias\": \" - leaderboard_bbh_hyperbaton\"\ ,\n \"acc_norm,none\": 0.568,\n \"acc_norm_stderr,none\":\ \ 0.03139181076542941\n },\n \"leaderboard_bbh_logical_deduction_five_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_logical_deduction_five_objects\"\ ,\n \"acc_norm,none\": 0.3,\n \"acc_norm_stderr,none\": 0.029040893477575783\n\ \ },\n \"leaderboard_bbh_logical_deduction_seven_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\",\n\ \ \"acc_norm,none\": 0.272,\n \"acc_norm_stderr,none\": 0.028200088296309975\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n\ \ \"acc_norm,none\": 0.416,\n \"acc_norm_stderr,none\": 0.031235856237014505\n\ \ },\n \"leaderboard_bbh_movie_recommendation\": {\n \"\ alias\": \" - leaderboard_bbh_movie_recommendation\",\n \"acc_norm,none\"\ : 0.492,\n \"acc_norm_stderr,none\": 0.03168215643141386\n },\n\ \ \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.42,\n \"acc_norm_stderr,none\": 0.03127799950463661\n\ \ },\n \"leaderboard_bbh_object_counting\": {\n \"alias\"\ : \" - leaderboard_bbh_object_counting\",\n \"acc_norm,none\": 0.376,\n\ \ \"acc_norm_stderr,none\": 0.03069633626739458\n },\n \ \ \"leaderboard_bbh_penguins_in_a_table\": {\n \"alias\": \" - leaderboard_bbh_penguins_in_a_table\"\ ,\n \"acc_norm,none\": 0.2054794520547945,\n \"acc_norm_stderr,none\"\ : 0.03355465401072847\n },\n \"leaderboard_bbh_reasoning_about_colored_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\"\ ,\n \"acc_norm,none\": 0.28,\n \"acc_norm_stderr,none\": 0.02845414827783231\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \"\ \ - leaderboard_bbh_ruin_names\",\n \"acc_norm,none\": 0.228,\n \ \ \"acc_norm_stderr,none\": 0.026587432487268498\n },\n \"\ leaderboard_bbh_salient_translation_error_detection\": {\n \"alias\"\ : \" - leaderboard_bbh_salient_translation_error_detection\",\n \"acc_norm,none\"\ : 0.268,\n \"acc_norm_stderr,none\": 0.02806876238252672\n },\n\ \ \"leaderboard_bbh_snarks\": {\n \"alias\": \" - leaderboard_bbh_snarks\"\ ,\n \"acc_norm,none\": 0.4438202247191011,\n \"acc_norm_stderr,none\"\ : 0.03734431584194247\n },\n \"leaderboard_bbh_sports_understanding\"\ : {\n \"alias\": \" - leaderboard_bbh_sports_understanding\",\n \ \ \"acc_norm,none\": 0.58,\n \"acc_norm_stderr,none\": 0.03127799950463661\n\ \ },\n \"leaderboard_bbh_temporal_sequences\": {\n \"alias\"\ : \" - leaderboard_bbh_temporal_sequences\",\n \"acc_norm,none\": 0.24,\n\ \ \"acc_norm_stderr,none\": 0.027065293652238982\n },\n \ \ \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \"\ alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\",\n \ \ \"acc_norm,none\": 0.184,\n \"acc_norm_stderr,none\": 0.02455581299422255\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.144,\n \"acc_norm_stderr,none\":\ \ 0.022249407735450245\n },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.3,\n \"acc_norm_stderr,none\": 0.029040893477575783\n\ \ },\n \"leaderboard_bbh_web_of_lies\": {\n \"alias\":\ \ \" - leaderboard_bbh_web_of_lies\",\n \"acc_norm,none\": 0.488,\n\ \ \"acc_norm_stderr,none\": 0.03167708558254714\n },\n \ \ \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.2684563758389262,\n\ \ \"acc_norm_stderr,none\": 0.01284985608403652,\n \"alias\"\ : \" - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n\ \ \"alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\"\ : 0.2727272727272727,\n \"acc_norm_stderr,none\": 0.03173071239071728\n\ \ },\n \"leaderboard_gpqa_extended\": {\n \"alias\": \"\ \ - leaderboard_gpqa_extended\",\n \"acc_norm,none\": 0.2600732600732601,\n\ \ \"acc_norm_stderr,none\": 0.018790743352015988\n },\n \ \ \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.2767857142857143,\n \"acc_norm_stderr,none\"\ : 0.021161749643954904\n },\n \"leaderboard_ifeval\": {\n \ \ \"alias\": \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\"\ : 0.08502772643253234,\n \"prompt_level_strict_acc_stderr,none\": 0.012002932679468982,\n\ \ \"inst_level_strict_acc,none\": 0.17865707434052758,\n \"\ inst_level_strict_acc_stderr,none\": \"N/A\",\n \"prompt_level_loose_acc,none\"\ : 0.09242144177449169,\n \"prompt_level_loose_acc_stderr,none\": 0.012463258511767319,\n\ \ \"inst_level_loose_acc,none\": 0.19664268585131894,\n \"\ inst_level_loose_acc_stderr,none\": \"N/A\"\n },\n \"leaderboard_math_hard\"\ : {\n \"exact_match,none\": 0.006797583081570997,\n \"exact_match_stderr,none\"\ : 0.0022605202818857095,\n \"alias\": \" - leaderboard_math_hard\"\n\ \ },\n \"leaderboard_math_algebra_hard\": {\n \"alias\"\ : \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\": 0.009771986970684038,\n\ \ \"exact_match_stderr,none\": 0.005623391633915856\n },\n \ \ \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\": \"\ \ - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.0,\n \"exact_match_stderr,none\": 0.0\n },\n \"leaderboard_math_geometry_hard\"\ : {\n \"alias\": \" - leaderboard_math_geometry_hard\",\n \ \ \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\": 0.0\n \ \ },\n \"leaderboard_math_intermediate_algebra_hard\": {\n \ \ \"alias\": \" - leaderboard_math_intermediate_algebra_hard\",\n \ \ \"exact_match,none\": 0.0035714285714285713,\n \"exact_match_stderr,none\"\ : 0.0035714285714285713\n },\n \"leaderboard_math_num_theory_hard\"\ : {\n \"alias\": \" - leaderboard_math_num_theory_hard\",\n \ \ \"exact_match,none\": 0.006493506493506494,\n \"exact_match_stderr,none\"\ : 0.006493506493506494\n },\n \"leaderboard_math_prealgebra_hard\"\ : {\n \"alias\": \" - leaderboard_math_prealgebra_hard\",\n \ \ \"exact_match,none\": 0.010362694300518135,\n \"exact_match_stderr,none\"\ : 0.007308424386792209\n },\n \"leaderboard_math_precalculus_hard\"\ : {\n \"alias\": \" - leaderboard_math_precalculus_hard\",\n \ \ \"exact_match,none\": 0.014814814814814815,\n \"exact_match_stderr,none\"\ : 0.010436494549594376\n },\n \"leaderboard_mmlu_pro\": {\n \ \ \"alias\": \" - leaderboard_mmlu_pro\",\n \"acc,none\": 0.2562333776595745,\n\ \ \"acc_stderr,none\": 0.003980020690139148\n },\n \"leaderboard_musr\"\ : {\n \"acc_norm,none\": 0.4298941798941799,\n \"acc_norm_stderr,none\"\ : 0.017856543173457542,\n \"alias\": \" - leaderboard_musr\"\n \ \ },\n \"leaderboard_musr_murder_mysteries\": {\n \"alias\":\ \ \" - leaderboard_musr_murder_mysteries\",\n \"acc_norm,none\": 0.52,\n\ \ \"acc_norm_stderr,none\": 0.03166085340849512\n },\n \ \ \"leaderboard_musr_object_placements\": {\n \"alias\": \" - leaderboard_musr_object_placements\"\ ,\n \"acc_norm,none\": 0.34765625,\n \"acc_norm_stderr,none\"\ : 0.029822440677853123\n },\n \"leaderboard_musr_team_allocation\"\ : {\n \"alias\": \" - leaderboard_musr_team_allocation\",\n \ \ \"acc_norm,none\": 0.424,\n \"acc_norm_stderr,none\": 0.03131803437491622\n\ \ }\n },\n \"leaderboard\": {\n \"prompt_level_loose_acc,none\"\ : 0.09242144177449169,\n \"prompt_level_loose_acc_stderr,none\": 0.012463258511767319,\n\ \ \"inst_level_loose_acc,none\": 0.19664268585131894,\n \"inst_level_loose_acc_stderr,none\"\ : \"N/A\",\n \"acc,none\": 0.2562333776595745,\n \"acc_stderr,none\"\ : 0.003980020690139148,\n \"exact_match,none\": 0.006797583081570997,\n \ \ \"exact_match_stderr,none\": 0.0022605202818857095,\n \"acc_norm,none\"\ : 0.3673628226747957,\n \"acc_norm_stderr,none\": 0.005279715115717286,\n\ \ \"prompt_level_strict_acc,none\": 0.08502772643253234,\n \"prompt_level_strict_acc_stderr,none\"\ : 0.012002932679468982,\n \"inst_level_strict_acc,none\": 0.17865707434052758,\n\ \ \"inst_level_strict_acc_stderr,none\": \"N/A\",\n \"alias\": \"\ leaderboard\"\n },\n \"leaderboard_bbh\": {\n \"acc_norm,none\": 0.37962159347335533,\n\ \ \"acc_norm_stderr,none\": 0.006111875587492996,\n \"alias\": \"\ \ - leaderboard_bbh\"\n },\n \"leaderboard_bbh_boolean_expressions\": {\n\ \ \"alias\": \" - leaderboard_bbh_boolean_expressions\",\n \"acc_norm,none\"\ : 0.752,\n \"acc_norm_stderr,none\": 0.027367497504863593\n },\n \"\ leaderboard_bbh_causal_judgement\": {\n \"alias\": \" - leaderboard_bbh_causal_judgement\"\ ,\n \"acc_norm,none\": 0.5240641711229946,\n \"acc_norm_stderr,none\"\ : 0.03661929361528698\n },\n \"leaderboard_bbh_date_understanding\": {\n \ \ \"alias\": \" - leaderboard_bbh_date_understanding\",\n \"acc_norm,none\"\ : 0.324,\n \"acc_norm_stderr,none\": 0.029658294924545567\n },\n \"\ leaderboard_bbh_disambiguation_qa\": {\n \"alias\": \" - leaderboard_bbh_disambiguation_qa\"\ ,\n \"acc_norm,none\": 0.464,\n \"acc_norm_stderr,none\": 0.03160397514522374\n\ \ },\n \"leaderboard_bbh_formal_fallacies\": {\n \"alias\": \" - leaderboard_bbh_formal_fallacies\"\ ,\n \"acc_norm,none\": 0.5,\n \"acc_norm_stderr,none\": 0.031686212526223896\n\ \ },\n \"leaderboard_bbh_geometric_shapes\": {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\"\ ,\n \"acc_norm,none\": 0.324,\n \"acc_norm_stderr,none\": 0.029658294924545567\n\ \ },\n \"leaderboard_bbh_hyperbaton\": {\n \"alias\": \" - leaderboard_bbh_hyperbaton\"\ ,\n \"acc_norm,none\": 0.568,\n \"acc_norm_stderr,none\": 0.03139181076542941\n\ \ },\n \"leaderboard_bbh_logical_deduction_five_objects\": {\n \"alias\"\ : \" - leaderboard_bbh_logical_deduction_five_objects\",\n \"acc_norm,none\"\ : 0.3,\n \"acc_norm_stderr,none\": 0.029040893477575783\n },\n \"leaderboard_bbh_logical_deduction_seven_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\"\ ,\n \"acc_norm,none\": 0.272,\n \"acc_norm_stderr,none\": 0.028200088296309975\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \"\ alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n \"acc_norm,none\"\ : 0.416,\n \"acc_norm_stderr,none\": 0.031235856237014505\n },\n \"\ leaderboard_bbh_movie_recommendation\": {\n \"alias\": \" - leaderboard_bbh_movie_recommendation\"\ ,\n \"acc_norm,none\": 0.492,\n \"acc_norm_stderr,none\": 0.03168215643141386\n\ \ },\n \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.42,\n \"acc_norm_stderr,none\": 0.03127799950463661\n\ \ },\n \"leaderboard_bbh_object_counting\": {\n \"alias\": \" - leaderboard_bbh_object_counting\"\ ,\n \"acc_norm,none\": 0.376,\n \"acc_norm_stderr,none\": 0.03069633626739458\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"alias\": \" \ \ - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\": 0.2054794520547945,\n\ \ \"acc_norm_stderr,none\": 0.03355465401072847\n },\n \"leaderboard_bbh_reasoning_about_colored_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\"\ ,\n \"acc_norm,none\": 0.28,\n \"acc_norm_stderr,none\": 0.02845414827783231\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \" - leaderboard_bbh_ruin_names\"\ ,\n \"acc_norm,none\": 0.228,\n \"acc_norm_stderr,none\": 0.026587432487268498\n\ \ },\n \"leaderboard_bbh_salient_translation_error_detection\": {\n \ \ \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\",\n \ \ \"acc_norm,none\": 0.268,\n \"acc_norm_stderr,none\": 0.02806876238252672\n\ \ },\n \"leaderboard_bbh_snarks\": {\n \"alias\": \" - leaderboard_bbh_snarks\"\ ,\n \"acc_norm,none\": 0.4438202247191011,\n \"acc_norm_stderr,none\"\ : 0.03734431584194247\n },\n \"leaderboard_bbh_sports_understanding\": {\n\ \ \"alias\": \" - leaderboard_bbh_sports_understanding\",\n \"acc_norm,none\"\ : 0.58,\n \"acc_norm_stderr,none\": 0.03127799950463661\n },\n \"leaderboard_bbh_temporal_sequences\"\ : {\n \"alias\": \" - leaderboard_bbh_temporal_sequences\",\n \"\ acc_norm,none\": 0.24,\n \"acc_norm_stderr,none\": 0.027065293652238982\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\"\ ,\n \"acc_norm,none\": 0.184,\n \"acc_norm_stderr,none\": 0.02455581299422255\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.144,\n \"acc_norm_stderr,none\": 0.022249407735450245\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.3,\n \"acc_norm_stderr,none\": 0.029040893477575783\n\ \ },\n \"leaderboard_bbh_web_of_lies\": {\n \"alias\": \" - leaderboard_bbh_web_of_lies\"\ ,\n \"acc_norm,none\": 0.488,\n \"acc_norm_stderr,none\": 0.03167708558254714\n\ \ },\n \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.2684563758389262,\n\ \ \"acc_norm_stderr,none\": 0.01284985608403652,\n \"alias\": \" -\ \ leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n \"alias\"\ : \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\": 0.2727272727272727,\n\ \ \"acc_norm_stderr,none\": 0.03173071239071728\n },\n \"leaderboard_gpqa_extended\"\ : {\n \"alias\": \" - leaderboard_gpqa_extended\",\n \"acc_norm,none\"\ : 0.2600732600732601,\n \"acc_norm_stderr,none\": 0.018790743352015988\n\ \ },\n \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.2767857142857143,\n \"acc_norm_stderr,none\"\ : 0.021161749643954904\n },\n \"leaderboard_ifeval\": {\n \"alias\"\ : \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\": 0.08502772643253234,\n\ \ \"prompt_level_strict_acc_stderr,none\": 0.012002932679468982,\n \ \ \"inst_level_strict_acc,none\": 0.17865707434052758,\n \"inst_level_strict_acc_stderr,none\"\ : \"N/A\",\n \"prompt_level_loose_acc,none\": 0.09242144177449169,\n \ \ \"prompt_level_loose_acc_stderr,none\": 0.012463258511767319,\n \"inst_level_loose_acc,none\"\ : 0.19664268585131894,\n \"inst_level_loose_acc_stderr,none\": \"N/A\"\n\ \ },\n \"leaderboard_math_hard\": {\n \"exact_match,none\": 0.006797583081570997,\n\ \ \"exact_match_stderr,none\": 0.0022605202818857095,\n \"alias\"\ : \" - leaderboard_math_hard\"\n },\n \"leaderboard_math_algebra_hard\": {\n\ \ \"alias\": \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\"\ : 0.009771986970684038,\n \"exact_match_stderr,none\": 0.005623391633915856\n\ \ },\n \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\":\ \ \" - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.0,\n \"exact_match_stderr,none\": 0.0\n },\n \"leaderboard_math_geometry_hard\"\ : {\n \"alias\": \" - leaderboard_math_geometry_hard\",\n \"exact_match,none\"\ : 0.0,\n \"exact_match_stderr,none\": 0.0\n },\n \"leaderboard_math_intermediate_algebra_hard\"\ : {\n \"alias\": \" - leaderboard_math_intermediate_algebra_hard\",\n \ \ \"exact_match,none\": 0.0035714285714285713,\n \"exact_match_stderr,none\"\ : 0.0035714285714285713\n },\n \"leaderboard_math_num_theory_hard\": {\n \ \ \"alias\": \" - leaderboard_math_num_theory_hard\",\n \"exact_match,none\"\ : 0.006493506493506494,\n \"exact_match_stderr,none\": 0.006493506493506494\n\ \ },\n \"leaderboard_math_prealgebra_hard\": {\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\ ,\n \"exact_match,none\": 0.010362694300518135,\n \"exact_match_stderr,none\"\ : 0.007308424386792209\n },\n \"leaderboard_math_precalculus_hard\": {\n \ \ \"alias\": \" - leaderboard_math_precalculus_hard\",\n \"exact_match,none\"\ : 0.014814814814814815,\n \"exact_match_stderr,none\": 0.010436494549594376\n\ \ },\n \"leaderboard_mmlu_pro\": {\n \"alias\": \" - leaderboard_mmlu_pro\"\ ,\n \"acc,none\": 0.2562333776595745,\n \"acc_stderr,none\": 0.003980020690139148\n\ \ },\n \"leaderboard_musr\": {\n \"acc_norm,none\": 0.4298941798941799,\n\ \ \"acc_norm_stderr,none\": 0.017856543173457542,\n \"alias\": \"\ \ - leaderboard_musr\"\n },\n \"leaderboard_musr_murder_mysteries\": {\n \ \ \"alias\": \" - leaderboard_musr_murder_mysteries\",\n \"acc_norm,none\"\ : 0.52,\n \"acc_norm_stderr,none\": 0.03166085340849512\n },\n \"leaderboard_musr_object_placements\"\ : {\n \"alias\": \" - leaderboard_musr_object_placements\",\n \"\ acc_norm,none\": 0.34765625,\n \"acc_norm_stderr,none\": 0.029822440677853123\n\ \ },\n \"leaderboard_musr_team_allocation\": {\n \"alias\": \" - leaderboard_musr_team_allocation\"\ ,\n \"acc_norm,none\": 0.424,\n \"acc_norm_stderr,none\": 0.03131803437491622\n\ \ }\n}\n```" repo_url: https://huggingface.co/cluebbers/Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid leaderboard_url: '' point_of_contact: '' configs: - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_bbh_boolean_expressions data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_bbh_causal_judgement data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_bbh_date_understanding data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_bbh_disambiguation_qa data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_bbh_formal_fallacies data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_bbh_geometric_shapes data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_bbh_hyperbaton data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_bbh_logical_deduction_five_objects data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_bbh_logical_deduction_seven_objects data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_bbh_logical_deduction_three_objects data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_bbh_movie_recommendation data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_bbh_navigate data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_bbh_navigate_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_navigate_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_bbh_object_counting data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_bbh_object_counting_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_object_counting_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_bbh_penguins_in_a_table data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_bbh_reasoning_about_colored_objects data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_bbh_ruin_names data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_bbh_salient_translation_error_detection data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_bbh_snarks data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_bbh_snarks_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_snarks_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_bbh_sports_understanding data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_bbh_temporal_sequences data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_bbh_tracking_shuffled_objects_five_objects data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_bbh_tracking_shuffled_objects_seven_objects data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_bbh_tracking_shuffled_objects_three_objects data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_bbh_web_of_lies data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_gpqa_diamond data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_gpqa_diamond_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_diamond_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_gpqa_extended data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_gpqa_extended_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_extended_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_gpqa_main data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_gpqa_main_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_main_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_ifeval data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_ifeval_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_ifeval_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_math_algebra_hard data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_math_algebra_hard_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_math_algebra_hard_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_math_counting_and_prob_hard data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_math_geometry_hard data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_math_geometry_hard_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_math_geometry_hard_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_math_intermediate_algebra_hard data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_math_num_theory_hard data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_math_prealgebra_hard data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_math_precalculus_hard data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_mmlu_pro data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_mmlu_pro_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_mmlu_pro_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_musr_murder_mysteries data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_musr_object_placements data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_musr_object_placements_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_musr_object_placements_2024-11-19T22-02-56.232495.jsonl' - config_name: cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_musr_team_allocation data_files: - split: 2024_11_19T22_02_56.232495 path: - '**/samples_leaderboard_musr_team_allocation_2024-11-19T22-02-56.232495.jsonl' - split: latest path: - '**/samples_leaderboard_musr_team_allocation_2024-11-19T22-02-56.232495.jsonl' --- # Dataset Card for Evaluation run of cluebbers/Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [cluebbers/Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid](https://huggingface.co/cluebbers/Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid) The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run. To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset( "open-llm-leaderboard/cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid-details", name="cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid__leaderboard_bbh_boolean_expressions", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-11-19T22-02-56.232495](https://huggingface.co/datasets/open-llm-leaderboard/cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid-details/blob/main/cluebbers__Llama-3.1-8B-paraphrase-type-generation-apty-sigmoid/results_2024-11-19T22-02-56.232495.json) (note that there might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "leaderboard": { "prompt_level_loose_acc,none": 0.09242144177449169, "prompt_level_loose_acc_stderr,none": 0.012463258511767319, "inst_level_loose_acc,none": 0.19664268585131894, "inst_level_loose_acc_stderr,none": "N/A", "acc,none": 0.2562333776595745, "acc_stderr,none": 0.003980020690139148, "exact_match,none": 0.006797583081570997, "exact_match_stderr,none": 0.0022605202818857095, "acc_norm,none": 0.3673628226747957, "acc_norm_stderr,none": 0.005279715115717286, "prompt_level_strict_acc,none": 0.08502772643253234, "prompt_level_strict_acc_stderr,none": 0.012002932679468982, "inst_level_strict_acc,none": 0.17865707434052758, "inst_level_strict_acc_stderr,none": "N/A", "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.37962159347335533, "acc_norm_stderr,none": 0.006111875587492996, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.752, "acc_norm_stderr,none": 0.027367497504863593 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.5240641711229946, "acc_norm_stderr,none": 0.03661929361528698 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.324, "acc_norm_stderr,none": 0.029658294924545567 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.464, "acc_norm_stderr,none": 0.03160397514522374 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.5, "acc_norm_stderr,none": 0.031686212526223896 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.324, "acc_norm_stderr,none": 0.029658294924545567 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.568, "acc_norm_stderr,none": 0.03139181076542941 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.3, "acc_norm_stderr,none": 0.029040893477575783 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.272, "acc_norm_stderr,none": 0.028200088296309975 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.416, "acc_norm_stderr,none": 0.031235856237014505 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.492, "acc_norm_stderr,none": 0.03168215643141386 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.42, "acc_norm_stderr,none": 0.03127799950463661 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.376, "acc_norm_stderr,none": 0.03069633626739458 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.2054794520547945, "acc_norm_stderr,none": 0.03355465401072847 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.28, "acc_norm_stderr,none": 0.02845414827783231 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.228, "acc_norm_stderr,none": 0.026587432487268498 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.268, "acc_norm_stderr,none": 0.02806876238252672 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.4438202247191011, "acc_norm_stderr,none": 0.03734431584194247 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.58, "acc_norm_stderr,none": 0.03127799950463661 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.24, "acc_norm_stderr,none": 0.027065293652238982 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.184, "acc_norm_stderr,none": 0.02455581299422255 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.144, "acc_norm_stderr,none": 0.022249407735450245 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.3, "acc_norm_stderr,none": 0.029040893477575783 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.488, "acc_norm_stderr,none": 0.03167708558254714 }, "leaderboard_gpqa": { "acc_norm,none": 0.2684563758389262, "acc_norm_stderr,none": 0.01284985608403652, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.2727272727272727, "acc_norm_stderr,none": 0.03173071239071728 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.2600732600732601, "acc_norm_stderr,none": 0.018790743352015988 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.2767857142857143, "acc_norm_stderr,none": 0.021161749643954904 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.08502772643253234, "prompt_level_strict_acc_stderr,none": 0.012002932679468982, "inst_level_strict_acc,none": 0.17865707434052758, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.09242144177449169, "prompt_level_loose_acc_stderr,none": 0.012463258511767319, "inst_level_loose_acc,none": 0.19664268585131894, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.006797583081570997, "exact_match_stderr,none": 0.0022605202818857095, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.009771986970684038, "exact_match_stderr,none": 0.005623391633915856 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.0035714285714285713, "exact_match_stderr,none": 0.0035714285714285713 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.006493506493506494, "exact_match_stderr,none": 0.006493506493506494 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.010362694300518135, "exact_match_stderr,none": 0.007308424386792209 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.014814814814814815, "exact_match_stderr,none": 0.010436494549594376 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.2562333776595745, "acc_stderr,none": 0.003980020690139148 }, "leaderboard_musr": { "acc_norm,none": 0.4298941798941799, "acc_norm_stderr,none": 0.017856543173457542, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.52, "acc_norm_stderr,none": 0.03166085340849512 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.34765625, "acc_norm_stderr,none": 0.029822440677853123 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.424, "acc_norm_stderr,none": 0.03131803437491622 } }, "leaderboard": { "prompt_level_loose_acc,none": 0.09242144177449169, "prompt_level_loose_acc_stderr,none": 0.012463258511767319, "inst_level_loose_acc,none": 0.19664268585131894, "inst_level_loose_acc_stderr,none": "N/A", "acc,none": 0.2562333776595745, "acc_stderr,none": 0.003980020690139148, "exact_match,none": 0.006797583081570997, "exact_match_stderr,none": 0.0022605202818857095, "acc_norm,none": 0.3673628226747957, "acc_norm_stderr,none": 0.005279715115717286, "prompt_level_strict_acc,none": 0.08502772643253234, "prompt_level_strict_acc_stderr,none": 0.012002932679468982, "inst_level_strict_acc,none": 0.17865707434052758, "inst_level_strict_acc_stderr,none": "N/A", "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.37962159347335533, "acc_norm_stderr,none": 0.006111875587492996, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.752, "acc_norm_stderr,none": 0.027367497504863593 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.5240641711229946, "acc_norm_stderr,none": 0.03661929361528698 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.324, "acc_norm_stderr,none": 0.029658294924545567 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.464, "acc_norm_stderr,none": 0.03160397514522374 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.5, "acc_norm_stderr,none": 0.031686212526223896 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.324, "acc_norm_stderr,none": 0.029658294924545567 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.568, "acc_norm_stderr,none": 0.03139181076542941 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.3, "acc_norm_stderr,none": 0.029040893477575783 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.272, "acc_norm_stderr,none": 0.028200088296309975 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.416, "acc_norm_stderr,none": 0.031235856237014505 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.492, "acc_norm_stderr,none": 0.03168215643141386 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.42, "acc_norm_stderr,none": 0.03127799950463661 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.376, "acc_norm_stderr,none": 0.03069633626739458 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.2054794520547945, "acc_norm_stderr,none": 0.03355465401072847 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.28, "acc_norm_stderr,none": 0.02845414827783231 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.228, "acc_norm_stderr,none": 0.026587432487268498 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.268, "acc_norm_stderr,none": 0.02806876238252672 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.4438202247191011, "acc_norm_stderr,none": 0.03734431584194247 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.58, "acc_norm_stderr,none": 0.03127799950463661 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.24, "acc_norm_stderr,none": 0.027065293652238982 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.184, "acc_norm_stderr,none": 0.02455581299422255 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.144, "acc_norm_stderr,none": 0.022249407735450245 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.3, "acc_norm_stderr,none": 0.029040893477575783 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.488, "acc_norm_stderr,none": 0.03167708558254714 }, "leaderboard_gpqa": { "acc_norm,none": 0.2684563758389262, "acc_norm_stderr,none": 0.01284985608403652, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.2727272727272727, "acc_norm_stderr,none": 0.03173071239071728 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.2600732600732601, "acc_norm_stderr,none": 0.018790743352015988 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.2767857142857143, "acc_norm_stderr,none": 0.021161749643954904 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.08502772643253234, "prompt_level_strict_acc_stderr,none": 0.012002932679468982, "inst_level_strict_acc,none": 0.17865707434052758, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.09242144177449169, "prompt_level_loose_acc_stderr,none": 0.012463258511767319, "inst_level_loose_acc,none": 0.19664268585131894, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.006797583081570997, "exact_match_stderr,none": 0.0022605202818857095, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.009771986970684038, "exact_match_stderr,none": 0.005623391633915856 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.0035714285714285713, "exact_match_stderr,none": 0.0035714285714285713 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.006493506493506494, "exact_match_stderr,none": 0.006493506493506494 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.010362694300518135, "exact_match_stderr,none": 0.007308424386792209 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.014814814814814815, "exact_match_stderr,none": 0.010436494549594376 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.2562333776595745, "acc_stderr,none": 0.003980020690139148 }, "leaderboard_musr": { "acc_norm,none": 0.4298941798941799, "acc_norm_stderr,none": 0.017856543173457542, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.52, "acc_norm_stderr,none": 0.03166085340849512 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.34765625, "acc_norm_stderr,none": 0.029822440677853123 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.424, "acc_norm_stderr,none": 0.03131803437491622 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - 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It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). 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YangZhoumill/factor_medium_64k

YangZhoumill

--- dataset_info: features: - name: problem dtype: string - name: question dtype: string - name: solution dtype: string - name: op dtype: int64 - name: id dtype: int64 - name: template dtype: string - name: mode dtype: string - name: length dtype: string - name: d dtype: int64 splits: - name: ops_2 num_bytes: 138096441 num_examples: 480 - name: ops_3 num_bytes: 136357405 num_examples: 480 - name: ops_4 num_bytes: 132803657 num_examples: 480 - name: ops_5 num_bytes: 128621002 num_examples: 480 - name: ops_6 num_bytes: 317065486 num_examples: 1159 - name: ops_7 num_bytes: 127027447 num_examples: 492 - name: ops_8 num_bytes: 258260134 num_examples: 1003 - name: ops_9 num_bytes: 215930558 num_examples: 826 - name: ops_10 num_bytes: 186097498 num_examples: 759 - name: ops_11 num_bytes: 183021458 num_examples: 711 - name: ops_12 num_bytes: 180492543 num_examples: 692 - name: ops_13 num_bytes: 168159764 num_examples: 646 - name: ops_14 num_bytes: 151505080 num_examples: 571 - name: ops_15 num_bytes: 172408278 num_examples: 719 - name: ops_16 num_bytes: 164422053 num_examples: 678 - name: ops_17 num_bytes: 164290988 num_examples: 649 - name: ops_18 num_bytes: 156514421 num_examples: 610 - name: ops_19 num_bytes: 133023791 num_examples: 586 - name: ops_20 num_bytes: 125417848 num_examples: 538 download_size: 862021789 dataset_size: 3239515852 configs: - config_name: default data_files: - split: ops_2 path: data/ops_2-* - split: ops_3 path: data/ops_3-* - split: ops_4 path: data/ops_4-* - split: ops_5 path: data/ops_5-* - split: ops_6 path: data/ops_6-* - split: ops_7 path: data/ops_7-* - split: ops_8 path: data/ops_8-* - split: ops_9 path: data/ops_9-* - split: ops_10 path: data/ops_10-* - split: ops_11 path: data/ops_11-* - split: ops_12 path: data/ops_12-* - split: ops_13 path: data/ops_13-* - split: ops_14 path: data/ops_14-* - split: ops_15 path: data/ops_15-* - split: ops_16 path: data/ops_16-* - split: ops_17 path: data/ops_17-* - split: ops_18 path: data/ops_18-* - split: ops_19 path: data/ops_19-* - split: ops_20 path: data/ops_20-* ---

open-llm-leaderboard/icefog72__Ice0.37-18.11-RP-details

open-llm-leaderboard

--- pretty_name: Evaluation run of icefog72/Ice0.37-18.11-RP dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [icefog72/Ice0.37-18.11-RP](https://huggingface.co/icefog72/Ice0.37-18.11-RP)\n\ The dataset is composed of 38 configuration(s), each one corresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can\ \ be found as a specific split in each configuration, the split being named using\ \ the timestamp of the run.The \"train\" split is always pointing to the latest\ \ results.\n\nAn additional configuration \"results\" store all the aggregated results\ \ of the run.\n\nTo load the details from a run, you can for instance do the following:\n\ ```python\nfrom datasets import load_dataset\ndata = load_dataset(\n\t\"open-llm-leaderboard/icefog72__Ice0.37-18.11-RP-details\"\ ,\n\tname=\"icefog72__Ice0.37-18.11-RP__leaderboard_bbh_boolean_expressions\",\n\ \tsplit=\"latest\"\n)\n```\n\n## Latest results\n\nThese are the [latest results\ \ from run 2024-11-19T23-56-46.205594](https://huggingface.co/datasets/open-llm-leaderboard/icefog72__Ice0.37-18.11-RP-details/blob/main/icefog72__Ice0.37-18.11-RP/results_2024-11-19T23-56-46.205594.json)\ \ (note that there might be results for other tasks in the repos if successive evals\ \ didn't cover the same tasks. You find each in the results and the \"latest\" split\ \ for each eval):\n\n```python\n{\n \"all\": {\n \"leaderboard\": {\n\ \ \"acc,none\": 0.3143284574468085,\n \"acc_stderr,none\"\ : 0.004232520301337217,\n \"exact_match,none\": 0.06419939577039276,\n\ \ \"exact_match_stderr,none\": 0.0066144283283954206,\n \"\ prompt_level_strict_acc,none\": 0.43807763401109057,\n \"prompt_level_strict_acc_stderr,none\"\ : 0.021350931135490865,\n \"acc_norm,none\": 0.46984044623167726,\n \ \ \"acc_norm_stderr,none\": 0.005333659102069773,\n \"prompt_level_loose_acc,none\"\ : 0.4713493530499076,\n \"prompt_level_loose_acc_stderr,none\": 0.02148122093008049,\n\ \ \"inst_level_strict_acc,none\": 0.5563549160671463,\n \"\ inst_level_strict_acc_stderr,none\": \"N/A\",\n \"inst_level_loose_acc,none\"\ : 0.5911270983213429,\n \"inst_level_loose_acc_stderr,none\": \"N/A\"\ ,\n \"alias\": \"leaderboard\"\n },\n \"leaderboard_bbh\"\ : {\n \"acc_norm,none\": 0.507203610484291,\n \"acc_norm_stderr,none\"\ : 0.0061443884276944525,\n \"alias\": \" - leaderboard_bbh\"\n \ \ },\n \"leaderboard_bbh_boolean_expressions\": {\n \"alias\"\ : \" - leaderboard_bbh_boolean_expressions\",\n \"acc_norm,none\": 0.828,\n\ \ \"acc_norm_stderr,none\": 0.02391551394448624\n },\n \ \ \"leaderboard_bbh_causal_judgement\": {\n \"alias\": \" - leaderboard_bbh_causal_judgement\"\ ,\n \"acc_norm,none\": 0.6096256684491979,\n \"acc_norm_stderr,none\"\ : 0.03576973947986408\n },\n \"leaderboard_bbh_date_understanding\"\ : {\n \"alias\": \" - leaderboard_bbh_date_understanding\",\n \ \ \"acc_norm,none\": 0.408,\n \"acc_norm_stderr,none\": 0.031145209846548512\n\ \ },\n \"leaderboard_bbh_disambiguation_qa\": {\n \"alias\"\ : \" - leaderboard_bbh_disambiguation_qa\",\n \"acc_norm,none\": 0.692,\n\ \ \"acc_norm_stderr,none\": 0.02925692860650181\n },\n \ \ \"leaderboard_bbh_formal_fallacies\": {\n \"alias\": \" - leaderboard_bbh_formal_fallacies\"\ ,\n \"acc_norm,none\": 0.56,\n \"acc_norm_stderr,none\": 0.03145724452223569\n\ \ },\n \"leaderboard_bbh_geometric_shapes\": {\n \"alias\"\ : \" - leaderboard_bbh_geometric_shapes\",\n \"acc_norm,none\": 0.464,\n\ \ \"acc_norm_stderr,none\": 0.03160397514522374\n },\n \ \ \"leaderboard_bbh_hyperbaton\": {\n \"alias\": \" - leaderboard_bbh_hyperbaton\"\ ,\n \"acc_norm,none\": 0.792,\n \"acc_norm_stderr,none\":\ \ 0.025721398901416368\n },\n \"leaderboard_bbh_logical_deduction_five_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_logical_deduction_five_objects\"\ ,\n \"acc_norm,none\": 0.452,\n \"acc_norm_stderr,none\":\ \ 0.03153986449255664\n },\n \"leaderboard_bbh_logical_deduction_seven_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\"\ ,\n \"acc_norm,none\": 0.44,\n \"acc_norm_stderr,none\": 0.03145724452223569\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n\ \ \"acc_norm,none\": 0.668,\n \"acc_norm_stderr,none\": 0.029844039047465857\n\ \ },\n \"leaderboard_bbh_movie_recommendation\": {\n \"\ alias\": \" - leaderboard_bbh_movie_recommendation\",\n \"acc_norm,none\"\ : 0.616,\n \"acc_norm_stderr,none\": 0.030821679117375447\n },\n\ \ \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.576,\n \"acc_norm_stderr,none\":\ \ 0.03131803437491622\n },\n \"leaderboard_bbh_object_counting\":\ \ {\n \"alias\": \" - leaderboard_bbh_object_counting\",\n \ \ \"acc_norm,none\": 0.356,\n \"acc_norm_stderr,none\": 0.0303436806571532\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"\ alias\": \" - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\"\ : 0.4315068493150685,\n \"acc_norm_stderr,none\": 0.041131302645371945\n\ \ },\n \"leaderboard_bbh_reasoning_about_colored_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\",\n\ \ \"acc_norm,none\": 0.524,\n \"acc_norm_stderr,none\": 0.03164968895968774\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \"\ \ - leaderboard_bbh_ruin_names\",\n \"acc_norm,none\": 0.504,\n \ \ \"acc_norm_stderr,none\": 0.0316851985511992\n },\n \"leaderboard_bbh_salient_translation_error_detection\"\ : {\n \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\"\ ,\n \"acc_norm,none\": 0.44,\n \"acc_norm_stderr,none\": 0.03145724452223569\n\ \ },\n \"leaderboard_bbh_snarks\": {\n \"alias\": \" -\ \ leaderboard_bbh_snarks\",\n \"acc_norm,none\": 0.6292134831460674,\n\ \ \"acc_norm_stderr,none\": 0.03630566062020178\n },\n \ \ \"leaderboard_bbh_sports_understanding\": {\n \"alias\": \" - leaderboard_bbh_sports_understanding\"\ ,\n \"acc_norm,none\": 0.828,\n \"acc_norm_stderr,none\":\ \ 0.02391551394448624\n },\n \"leaderboard_bbh_temporal_sequences\"\ : {\n \"alias\": \" - leaderboard_bbh_temporal_sequences\",\n \ \ \"acc_norm,none\": 0.268,\n \"acc_norm_stderr,none\": 0.02806876238252672\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_five_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\"\ ,\n \"acc_norm,none\": 0.192,\n \"acc_norm_stderr,none\":\ \ 0.024960691989171963\n },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.144,\n \"acc_norm_stderr,none\":\ \ 0.022249407735450245\n },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.296,\n \"acc_norm_stderr,none\":\ \ 0.028928939388379694\n },\n \"leaderboard_bbh_web_of_lies\": {\n\ \ \"alias\": \" - leaderboard_bbh_web_of_lies\",\n \"acc_norm,none\"\ : 0.484,\n \"acc_norm_stderr,none\": 0.03166998503010743\n },\n\ \ \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.31208053691275167,\n\ \ \"acc_norm_stderr,none\": 0.013434267788620318,\n \"alias\"\ : \" - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n\ \ \"alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\"\ : 0.29797979797979796,\n \"acc_norm_stderr,none\": 0.03258630383836556\n\ \ },\n \"leaderboard_gpqa_extended\": {\n \"alias\": \"\ \ - leaderboard_gpqa_extended\",\n \"acc_norm,none\": 0.32234432234432236,\n\ \ \"acc_norm_stderr,none\": 0.020020102750045735\n },\n \ \ \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.30580357142857145,\n \"acc_norm_stderr,none\"\ : 0.021792582688757046\n },\n \"leaderboard_ifeval\": {\n \ \ \"alias\": \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\"\ : 0.43807763401109057,\n \"prompt_level_strict_acc_stderr,none\": 0.021350931135490865,\n\ \ \"inst_level_strict_acc,none\": 0.5563549160671463,\n \"\ inst_level_strict_acc_stderr,none\": \"N/A\",\n \"prompt_level_loose_acc,none\"\ : 0.4713493530499076,\n \"prompt_level_loose_acc_stderr,none\": 0.02148122093008049,\n\ \ \"inst_level_loose_acc,none\": 0.5911270983213429,\n \"\ inst_level_loose_acc_stderr,none\": \"N/A\"\n },\n \"leaderboard_math_hard\"\ : {\n \"exact_match,none\": 0.06419939577039276,\n \"exact_match_stderr,none\"\ : 0.0066144283283954206,\n \"alias\": \" - leaderboard_math_hard\"\n\ \ },\n \"leaderboard_math_algebra_hard\": {\n \"alias\"\ : \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\": 0.09120521172638436,\n\ \ \"exact_match_stderr,none\": 0.016458188366731106\n },\n \ \ \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\": \"\ \ - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.024390243902439025,\n \"exact_match_stderr,none\": 0.013965813032045565\n\ \ },\n \"leaderboard_math_geometry_hard\": {\n \"alias\"\ : \" - leaderboard_math_geometry_hard\",\n \"exact_match,none\": 0.022727272727272728,\n\ \ \"exact_match_stderr,none\": 0.0130210469090637\n },\n \ \ \"leaderboard_math_intermediate_algebra_hard\": {\n \"alias\": \"\ \ - leaderboard_math_intermediate_algebra_hard\",\n \"exact_match,none\"\ : 0.02857142857142857,\n \"exact_match_stderr,none\": 0.009973998820736053\n\ \ },\n \"leaderboard_math_num_theory_hard\": {\n \"alias\"\ : \" - leaderboard_math_num_theory_hard\",\n \"exact_match,none\": 0.05194805194805195,\n\ \ \"exact_match_stderr,none\": 0.017941344490765\n },\n \ \ \"leaderboard_math_prealgebra_hard\": {\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\ ,\n \"exact_match,none\": 0.16580310880829016,\n \"exact_match_stderr,none\"\ : 0.026839845022314426\n },\n \"leaderboard_math_precalculus_hard\"\ : {\n \"alias\": \" - leaderboard_math_precalculus_hard\",\n \ \ \"exact_match,none\": 0.022222222222222223,\n \"exact_match_stderr,none\"\ : 0.01273389971505968\n },\n \"leaderboard_mmlu_pro\": {\n \ \ \"alias\": \" - leaderboard_mmlu_pro\",\n \"acc,none\": 0.3143284574468085,\n\ \ \"acc_stderr,none\": 0.004232520301337217\n },\n \"leaderboard_musr\"\ : {\n \"acc_norm,none\": 0.43386243386243384,\n \"acc_norm_stderr,none\"\ : 0.017804882924407392,\n \"alias\": \" - leaderboard_musr\"\n \ \ },\n \"leaderboard_musr_murder_mysteries\": {\n \"alias\":\ \ \" - leaderboard_musr_murder_mysteries\",\n \"acc_norm,none\": 0.54,\n\ \ \"acc_norm_stderr,none\": 0.031584653891499004\n },\n \ \ \"leaderboard_musr_object_placements\": {\n \"alias\": \" - leaderboard_musr_object_placements\"\ ,\n \"acc_norm,none\": 0.42578125,\n \"acc_norm_stderr,none\"\ : 0.030964342373467638\n },\n \"leaderboard_musr_team_allocation\"\ : {\n \"alias\": \" - leaderboard_musr_team_allocation\",\n \ \ \"acc_norm,none\": 0.336,\n \"acc_norm_stderr,none\": 0.02993325909419153\n\ \ }\n },\n \"leaderboard\": {\n \"acc,none\": 0.3143284574468085,\n\ \ \"acc_stderr,none\": 0.004232520301337217,\n \"exact_match,none\"\ : 0.06419939577039276,\n \"exact_match_stderr,none\": 0.0066144283283954206,\n\ \ \"prompt_level_strict_acc,none\": 0.43807763401109057,\n \"prompt_level_strict_acc_stderr,none\"\ : 0.021350931135490865,\n \"acc_norm,none\": 0.46984044623167726,\n \ \ \"acc_norm_stderr,none\": 0.005333659102069773,\n \"prompt_level_loose_acc,none\"\ : 0.4713493530499076,\n \"prompt_level_loose_acc_stderr,none\": 0.02148122093008049,\n\ \ \"inst_level_strict_acc,none\": 0.5563549160671463,\n \"inst_level_strict_acc_stderr,none\"\ : \"N/A\",\n \"inst_level_loose_acc,none\": 0.5911270983213429,\n \ \ \"inst_level_loose_acc_stderr,none\": \"N/A\",\n \"alias\": \"leaderboard\"\ \n },\n \"leaderboard_bbh\": {\n \"acc_norm,none\": 0.507203610484291,\n\ \ \"acc_norm_stderr,none\": 0.0061443884276944525,\n \"alias\": \"\ \ - leaderboard_bbh\"\n },\n \"leaderboard_bbh_boolean_expressions\": {\n\ \ \"alias\": \" - leaderboard_bbh_boolean_expressions\",\n \"acc_norm,none\"\ : 0.828,\n \"acc_norm_stderr,none\": 0.02391551394448624\n },\n \"\ leaderboard_bbh_causal_judgement\": {\n \"alias\": \" - leaderboard_bbh_causal_judgement\"\ ,\n \"acc_norm,none\": 0.6096256684491979,\n \"acc_norm_stderr,none\"\ : 0.03576973947986408\n },\n \"leaderboard_bbh_date_understanding\": {\n \ \ \"alias\": \" - leaderboard_bbh_date_understanding\",\n \"acc_norm,none\"\ : 0.408,\n \"acc_norm_stderr,none\": 0.031145209846548512\n },\n \"\ leaderboard_bbh_disambiguation_qa\": {\n \"alias\": \" - leaderboard_bbh_disambiguation_qa\"\ ,\n \"acc_norm,none\": 0.692,\n \"acc_norm_stderr,none\": 0.02925692860650181\n\ \ },\n \"leaderboard_bbh_formal_fallacies\": {\n \"alias\": \" - leaderboard_bbh_formal_fallacies\"\ ,\n \"acc_norm,none\": 0.56,\n \"acc_norm_stderr,none\": 0.03145724452223569\n\ \ },\n \"leaderboard_bbh_geometric_shapes\": {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\"\ ,\n \"acc_norm,none\": 0.464,\n \"acc_norm_stderr,none\": 0.03160397514522374\n\ \ },\n \"leaderboard_bbh_hyperbaton\": {\n \"alias\": \" - leaderboard_bbh_hyperbaton\"\ ,\n \"acc_norm,none\": 0.792,\n \"acc_norm_stderr,none\": 0.025721398901416368\n\ \ },\n \"leaderboard_bbh_logical_deduction_five_objects\": {\n \"alias\"\ : \" - leaderboard_bbh_logical_deduction_five_objects\",\n \"acc_norm,none\"\ : 0.452,\n \"acc_norm_stderr,none\": 0.03153986449255664\n },\n \"\ leaderboard_bbh_logical_deduction_seven_objects\": {\n \"alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\"\ ,\n \"acc_norm,none\": 0.44,\n \"acc_norm_stderr,none\": 0.03145724452223569\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \"\ alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n \"acc_norm,none\"\ : 0.668,\n \"acc_norm_stderr,none\": 0.029844039047465857\n },\n \"\ leaderboard_bbh_movie_recommendation\": {\n \"alias\": \" - leaderboard_bbh_movie_recommendation\"\ ,\n \"acc_norm,none\": 0.616,\n \"acc_norm_stderr,none\": 0.030821679117375447\n\ \ },\n \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.576,\n \"acc_norm_stderr,none\": 0.03131803437491622\n\ \ },\n \"leaderboard_bbh_object_counting\": {\n \"alias\": \" - leaderboard_bbh_object_counting\"\ ,\n \"acc_norm,none\": 0.356,\n \"acc_norm_stderr,none\": 0.0303436806571532\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"alias\": \" \ \ - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\": 0.4315068493150685,\n\ \ \"acc_norm_stderr,none\": 0.041131302645371945\n },\n \"leaderboard_bbh_reasoning_about_colored_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\"\ ,\n \"acc_norm,none\": 0.524,\n \"acc_norm_stderr,none\": 0.03164968895968774\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \" - leaderboard_bbh_ruin_names\"\ ,\n \"acc_norm,none\": 0.504,\n \"acc_norm_stderr,none\": 0.0316851985511992\n\ \ },\n \"leaderboard_bbh_salient_translation_error_detection\": {\n \ \ \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\",\n \ \ \"acc_norm,none\": 0.44,\n \"acc_norm_stderr,none\": 0.03145724452223569\n\ \ },\n \"leaderboard_bbh_snarks\": {\n \"alias\": \" - leaderboard_bbh_snarks\"\ ,\n \"acc_norm,none\": 0.6292134831460674,\n \"acc_norm_stderr,none\"\ : 0.03630566062020178\n },\n \"leaderboard_bbh_sports_understanding\": {\n\ \ \"alias\": \" - leaderboard_bbh_sports_understanding\",\n \"acc_norm,none\"\ : 0.828,\n \"acc_norm_stderr,none\": 0.02391551394448624\n },\n \"\ leaderboard_bbh_temporal_sequences\": {\n \"alias\": \" - leaderboard_bbh_temporal_sequences\"\ ,\n \"acc_norm,none\": 0.268,\n \"acc_norm_stderr,none\": 0.02806876238252672\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\"\ ,\n \"acc_norm,none\": 0.192,\n \"acc_norm_stderr,none\": 0.024960691989171963\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.144,\n \"acc_norm_stderr,none\": 0.022249407735450245\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.296,\n \"acc_norm_stderr,none\": 0.028928939388379694\n\ \ },\n \"leaderboard_bbh_web_of_lies\": {\n \"alias\": \" - leaderboard_bbh_web_of_lies\"\ ,\n \"acc_norm,none\": 0.484,\n \"acc_norm_stderr,none\": 0.03166998503010743\n\ \ },\n \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.31208053691275167,\n\ \ \"acc_norm_stderr,none\": 0.013434267788620318,\n \"alias\": \"\ \ - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n \"\ alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\": 0.29797979797979796,\n\ \ \"acc_norm_stderr,none\": 0.03258630383836556\n },\n \"leaderboard_gpqa_extended\"\ : {\n \"alias\": \" - leaderboard_gpqa_extended\",\n \"acc_norm,none\"\ : 0.32234432234432236,\n \"acc_norm_stderr,none\": 0.020020102750045735\n\ \ },\n \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.30580357142857145,\n \"acc_norm_stderr,none\"\ : 0.021792582688757046\n },\n \"leaderboard_ifeval\": {\n \"alias\"\ : \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\": 0.43807763401109057,\n\ \ \"prompt_level_strict_acc_stderr,none\": 0.021350931135490865,\n \ \ \"inst_level_strict_acc,none\": 0.5563549160671463,\n \"inst_level_strict_acc_stderr,none\"\ : \"N/A\",\n \"prompt_level_loose_acc,none\": 0.4713493530499076,\n \ \ \"prompt_level_loose_acc_stderr,none\": 0.02148122093008049,\n \"inst_level_loose_acc,none\"\ : 0.5911270983213429,\n \"inst_level_loose_acc_stderr,none\": \"N/A\"\n \ \ },\n \"leaderboard_math_hard\": {\n \"exact_match,none\": 0.06419939577039276,\n\ \ \"exact_match_stderr,none\": 0.0066144283283954206,\n \"alias\"\ : \" - leaderboard_math_hard\"\n },\n \"leaderboard_math_algebra_hard\": {\n\ \ \"alias\": \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\"\ : 0.09120521172638436,\n \"exact_match_stderr,none\": 0.016458188366731106\n\ \ },\n \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\":\ \ \" - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.024390243902439025,\n \"exact_match_stderr,none\": 0.013965813032045565\n\ \ },\n \"leaderboard_math_geometry_hard\": {\n \"alias\": \" - leaderboard_math_geometry_hard\"\ ,\n \"exact_match,none\": 0.022727272727272728,\n \"exact_match_stderr,none\"\ : 0.0130210469090637\n },\n \"leaderboard_math_intermediate_algebra_hard\"\ : {\n \"alias\": \" - leaderboard_math_intermediate_algebra_hard\",\n \ \ \"exact_match,none\": 0.02857142857142857,\n \"exact_match_stderr,none\"\ : 0.009973998820736053\n },\n \"leaderboard_math_num_theory_hard\": {\n \ \ \"alias\": \" - leaderboard_math_num_theory_hard\",\n \"exact_match,none\"\ : 0.05194805194805195,\n \"exact_match_stderr,none\": 0.017941344490765\n\ \ },\n \"leaderboard_math_prealgebra_hard\": {\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\ ,\n \"exact_match,none\": 0.16580310880829016,\n \"exact_match_stderr,none\"\ : 0.026839845022314426\n },\n \"leaderboard_math_precalculus_hard\": {\n \ \ \"alias\": \" - leaderboard_math_precalculus_hard\",\n \"exact_match,none\"\ : 0.022222222222222223,\n \"exact_match_stderr,none\": 0.01273389971505968\n\ \ },\n \"leaderboard_mmlu_pro\": {\n \"alias\": \" - leaderboard_mmlu_pro\"\ ,\n \"acc,none\": 0.3143284574468085,\n \"acc_stderr,none\": 0.004232520301337217\n\ \ },\n \"leaderboard_musr\": {\n \"acc_norm,none\": 0.43386243386243384,\n\ \ \"acc_norm_stderr,none\": 0.017804882924407392,\n \"alias\": \"\ \ - leaderboard_musr\"\n },\n \"leaderboard_musr_murder_mysteries\": {\n \ \ \"alias\": \" - leaderboard_musr_murder_mysteries\",\n \"acc_norm,none\"\ : 0.54,\n \"acc_norm_stderr,none\": 0.031584653891499004\n },\n \"\ leaderboard_musr_object_placements\": {\n \"alias\": \" - leaderboard_musr_object_placements\"\ ,\n \"acc_norm,none\": 0.42578125,\n \"acc_norm_stderr,none\": 0.030964342373467638\n\ \ },\n \"leaderboard_musr_team_allocation\": {\n \"alias\": \" - leaderboard_musr_team_allocation\"\ ,\n \"acc_norm,none\": 0.336,\n \"acc_norm_stderr,none\": 0.02993325909419153\n\ \ }\n}\n```" repo_url: https://huggingface.co/icefog72/Ice0.37-18.11-RP leaderboard_url: '' point_of_contact: '' configs: - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_bbh_boolean_expressions data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_bbh_causal_judgement data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_bbh_date_understanding data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_bbh_disambiguation_qa data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_bbh_formal_fallacies data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_bbh_geometric_shapes data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_bbh_hyperbaton data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_bbh_logical_deduction_five_objects data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_bbh_logical_deduction_seven_objects data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_bbh_logical_deduction_three_objects data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_bbh_movie_recommendation data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_bbh_navigate data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_bbh_navigate_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_navigate_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_bbh_object_counting data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_bbh_object_counting_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_object_counting_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_bbh_penguins_in_a_table data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_bbh_reasoning_about_colored_objects data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_bbh_ruin_names data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_bbh_salient_translation_error_detection data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_bbh_snarks data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_bbh_snarks_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_snarks_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_bbh_sports_understanding data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_bbh_temporal_sequences data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_bbh_tracking_shuffled_objects_five_objects data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_bbh_tracking_shuffled_objects_seven_objects data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_bbh_tracking_shuffled_objects_three_objects data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_bbh_web_of_lies data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_gpqa_diamond data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_gpqa_diamond_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_diamond_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_gpqa_extended data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_gpqa_extended_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_extended_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_gpqa_main data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_gpqa_main_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_main_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_ifeval data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_ifeval_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_ifeval_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_math_algebra_hard data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_math_algebra_hard_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_math_algebra_hard_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_math_counting_and_prob_hard data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_math_geometry_hard data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_math_geometry_hard_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_math_geometry_hard_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_math_intermediate_algebra_hard data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_math_num_theory_hard data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_math_prealgebra_hard data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_math_precalculus_hard data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_mmlu_pro data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_mmlu_pro_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_mmlu_pro_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_musr_murder_mysteries data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_musr_object_placements data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_musr_object_placements_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_musr_object_placements_2024-11-19T23-56-46.205594.jsonl' - config_name: icefog72__Ice0.37-18.11-RP__leaderboard_musr_team_allocation data_files: - split: 2024_11_19T23_56_46.205594 path: - '**/samples_leaderboard_musr_team_allocation_2024-11-19T23-56-46.205594.jsonl' - split: latest path: - '**/samples_leaderboard_musr_team_allocation_2024-11-19T23-56-46.205594.jsonl' --- # Dataset Card for Evaluation run of icefog72/Ice0.37-18.11-RP <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [icefog72/Ice0.37-18.11-RP](https://huggingface.co/icefog72/Ice0.37-18.11-RP) The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run. To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset( "open-llm-leaderboard/icefog72__Ice0.37-18.11-RP-details", name="icefog72__Ice0.37-18.11-RP__leaderboard_bbh_boolean_expressions", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-11-19T23-56-46.205594](https://huggingface.co/datasets/open-llm-leaderboard/icefog72__Ice0.37-18.11-RP-details/blob/main/icefog72__Ice0.37-18.11-RP/results_2024-11-19T23-56-46.205594.json) (note that there might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "leaderboard": { "acc,none": 0.3143284574468085, "acc_stderr,none": 0.004232520301337217, "exact_match,none": 0.06419939577039276, "exact_match_stderr,none": 0.0066144283283954206, "prompt_level_strict_acc,none": 0.43807763401109057, "prompt_level_strict_acc_stderr,none": 0.021350931135490865, "acc_norm,none": 0.46984044623167726, "acc_norm_stderr,none": 0.005333659102069773, "prompt_level_loose_acc,none": 0.4713493530499076, "prompt_level_loose_acc_stderr,none": 0.02148122093008049, "inst_level_strict_acc,none": 0.5563549160671463, "inst_level_strict_acc_stderr,none": "N/A", "inst_level_loose_acc,none": 0.5911270983213429, "inst_level_loose_acc_stderr,none": "N/A", "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.507203610484291, "acc_norm_stderr,none": 0.0061443884276944525, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.828, "acc_norm_stderr,none": 0.02391551394448624 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.6096256684491979, "acc_norm_stderr,none": 0.03576973947986408 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.408, "acc_norm_stderr,none": 0.031145209846548512 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.692, "acc_norm_stderr,none": 0.02925692860650181 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.56, "acc_norm_stderr,none": 0.03145724452223569 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.464, "acc_norm_stderr,none": 0.03160397514522374 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.792, "acc_norm_stderr,none": 0.025721398901416368 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.452, "acc_norm_stderr,none": 0.03153986449255664 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.44, "acc_norm_stderr,none": 0.03145724452223569 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.668, "acc_norm_stderr,none": 0.029844039047465857 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.616, "acc_norm_stderr,none": 0.030821679117375447 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.576, "acc_norm_stderr,none": 0.03131803437491622 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.356, "acc_norm_stderr,none": 0.0303436806571532 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.4315068493150685, "acc_norm_stderr,none": 0.041131302645371945 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.524, "acc_norm_stderr,none": 0.03164968895968774 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.504, "acc_norm_stderr,none": 0.0316851985511992 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.44, "acc_norm_stderr,none": 0.03145724452223569 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.6292134831460674, "acc_norm_stderr,none": 0.03630566062020178 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.828, "acc_norm_stderr,none": 0.02391551394448624 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.268, "acc_norm_stderr,none": 0.02806876238252672 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.192, "acc_norm_stderr,none": 0.024960691989171963 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.144, "acc_norm_stderr,none": 0.022249407735450245 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.296, "acc_norm_stderr,none": 0.028928939388379694 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.484, "acc_norm_stderr,none": 0.03166998503010743 }, "leaderboard_gpqa": { "acc_norm,none": 0.31208053691275167, "acc_norm_stderr,none": 0.013434267788620318, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.29797979797979796, "acc_norm_stderr,none": 0.03258630383836556 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.32234432234432236, "acc_norm_stderr,none": 0.020020102750045735 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.30580357142857145, "acc_norm_stderr,none": 0.021792582688757046 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.43807763401109057, "prompt_level_strict_acc_stderr,none": 0.021350931135490865, "inst_level_strict_acc,none": 0.5563549160671463, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.4713493530499076, "prompt_level_loose_acc_stderr,none": 0.02148122093008049, "inst_level_loose_acc,none": 0.5911270983213429, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.06419939577039276, "exact_match_stderr,none": 0.0066144283283954206, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.09120521172638436, "exact_match_stderr,none": 0.016458188366731106 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.024390243902439025, "exact_match_stderr,none": 0.013965813032045565 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.022727272727272728, "exact_match_stderr,none": 0.0130210469090637 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.02857142857142857, "exact_match_stderr,none": 0.009973998820736053 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.05194805194805195, "exact_match_stderr,none": 0.017941344490765 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.16580310880829016, "exact_match_stderr,none": 0.026839845022314426 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.022222222222222223, "exact_match_stderr,none": 0.01273389971505968 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.3143284574468085, "acc_stderr,none": 0.004232520301337217 }, "leaderboard_musr": { "acc_norm,none": 0.43386243386243384, "acc_norm_stderr,none": 0.017804882924407392, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.54, "acc_norm_stderr,none": 0.031584653891499004 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.42578125, "acc_norm_stderr,none": 0.030964342373467638 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.336, "acc_norm_stderr,none": 0.02993325909419153 } }, "leaderboard": { "acc,none": 0.3143284574468085, "acc_stderr,none": 0.004232520301337217, "exact_match,none": 0.06419939577039276, "exact_match_stderr,none": 0.0066144283283954206, "prompt_level_strict_acc,none": 0.43807763401109057, "prompt_level_strict_acc_stderr,none": 0.021350931135490865, "acc_norm,none": 0.46984044623167726, "acc_norm_stderr,none": 0.005333659102069773, "prompt_level_loose_acc,none": 0.4713493530499076, "prompt_level_loose_acc_stderr,none": 0.02148122093008049, "inst_level_strict_acc,none": 0.5563549160671463, "inst_level_strict_acc_stderr,none": "N/A", "inst_level_loose_acc,none": 0.5911270983213429, "inst_level_loose_acc_stderr,none": "N/A", "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.507203610484291, "acc_norm_stderr,none": 0.0061443884276944525, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.828, "acc_norm_stderr,none": 0.02391551394448624 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.6096256684491979, "acc_norm_stderr,none": 0.03576973947986408 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.408, "acc_norm_stderr,none": 0.031145209846548512 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.692, "acc_norm_stderr,none": 0.02925692860650181 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.56, "acc_norm_stderr,none": 0.03145724452223569 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.464, "acc_norm_stderr,none": 0.03160397514522374 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.792, "acc_norm_stderr,none": 0.025721398901416368 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.452, "acc_norm_stderr,none": 0.03153986449255664 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.44, "acc_norm_stderr,none": 0.03145724452223569 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.668, "acc_norm_stderr,none": 0.029844039047465857 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.616, "acc_norm_stderr,none": 0.030821679117375447 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.576, "acc_norm_stderr,none": 0.03131803437491622 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.356, "acc_norm_stderr,none": 0.0303436806571532 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.4315068493150685, "acc_norm_stderr,none": 0.041131302645371945 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.524, "acc_norm_stderr,none": 0.03164968895968774 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.504, "acc_norm_stderr,none": 0.0316851985511992 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.44, "acc_norm_stderr,none": 0.03145724452223569 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.6292134831460674, "acc_norm_stderr,none": 0.03630566062020178 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.828, "acc_norm_stderr,none": 0.02391551394448624 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.268, "acc_norm_stderr,none": 0.02806876238252672 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.192, "acc_norm_stderr,none": 0.024960691989171963 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.144, "acc_norm_stderr,none": 0.022249407735450245 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.296, "acc_norm_stderr,none": 0.028928939388379694 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.484, "acc_norm_stderr,none": 0.03166998503010743 }, "leaderboard_gpqa": { "acc_norm,none": 0.31208053691275167, "acc_norm_stderr,none": 0.013434267788620318, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.29797979797979796, "acc_norm_stderr,none": 0.03258630383836556 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.32234432234432236, "acc_norm_stderr,none": 0.020020102750045735 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.30580357142857145, "acc_norm_stderr,none": 0.021792582688757046 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.43807763401109057, "prompt_level_strict_acc_stderr,none": 0.021350931135490865, "inst_level_strict_acc,none": 0.5563549160671463, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.4713493530499076, "prompt_level_loose_acc_stderr,none": 0.02148122093008049, "inst_level_loose_acc,none": 0.5911270983213429, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.06419939577039276, "exact_match_stderr,none": 0.0066144283283954206, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.09120521172638436, "exact_match_stderr,none": 0.016458188366731106 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.024390243902439025, "exact_match_stderr,none": 0.013965813032045565 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.022727272727272728, "exact_match_stderr,none": 0.0130210469090637 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.02857142857142857, "exact_match_stderr,none": 0.009973998820736053 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.05194805194805195, "exact_match_stderr,none": 0.017941344490765 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.16580310880829016, "exact_match_stderr,none": 0.026839845022314426 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.022222222222222223, "exact_match_stderr,none": 0.01273389971505968 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.3143284574468085, "acc_stderr,none": 0.004232520301337217 }, "leaderboard_musr": { "acc_norm,none": 0.43386243386243384, "acc_norm_stderr,none": 0.017804882924407392, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.54, "acc_norm_stderr,none": 0.031584653891499004 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.42578125, "acc_norm_stderr,none": 0.030964342373467638 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.336, "acc_norm_stderr,none": 0.02993325909419153 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. 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If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

eliasfiz/rlhf-raw

eliasfiz

--- dataset_info: features: - name: audioId dtype: string - name: audio_bits dtype: audio - name: rank dtype: int64 - name: batchId dtype: string - name: prompt dtype: string - name: emotion dtype: string splits: - name: train num_bytes: 96468490.0 num_examples: 750 download_size: 87971561 dataset_size: 96468490.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

MahmoudIbrahim/Arabic_NVIDIA

MahmoudIbrahim

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 63028674 num_examples: 20300 download_size: 22579566 dataset_size: 63028674 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - question-answering - text-generation language: - ar tags: - finance pretty_name: 'n' size_categories: - 10K<n<100K --- ## Dataset Overview This dataset contains texts translated into Arabic, where the NVIDIA data, specifically the nvidia/HelpSteer2 dataset, was translated using Google Translate to achieve accurate translation. This data is specifically designed for use in question answering and text generation tasks. ## Dataset Details - **Original Dataset**: NVIDIA's **HelpSteer2** dataset. - **Language**: Translated into **Arabic**. - **Use Case**: question answering and text generation tasks.

amuvarma/mls-train-500

amuvarma

--- dataset_info: features: - name: audio_values sequence: float64 - name: transcript_ids sequence: int64 - name: labels sequence: int64 - name: input_ids sequence: int32 splits: - name: train num_bytes: 1200822000000 num_examples: 500000 download_size: 738443026079 dataset_size: 1200822000000 configs: - config_name: default data_files: - split: train path: data/train-* ---

nuprl-staging/training_classifier_intermediate_depth1

nuprl-staging

--- dataset_info: features: - name: id dtype: int64 - name: prompt dtype: string - name: answer dtype: string - name: pythoncode dtype: string - name: depth0 dtype: string - name: depth1 dtype: string - name: depth2 dtype: string - name: depth3 dtype: string - name: depth4 dtype: string - name: depth5 dtype: string - name: depth6 dtype: string - name: depth7 dtype: string - name: depthn0 dtype: string - name: depthn1 dtype: string - name: depthn2 dtype: string - name: depthn3 dtype: string - name: depthn4 dtype: string - name: depthn5 dtype: string - name: depthn6 dtype: string - name: depthn7 dtype: string - name: num_nodes dtype: int64 - name: num_edges dtype: int64 - name: num_classes dtype: int64 - name: path_length dtype: int64 - name: num_cycle dtype: int64 - name: correctness dtype: bool - name: one_correct dtype: bool splits: - name: train num_bytes: 4201926 num_examples: 3000 - name: test num_bytes: 703852 num_examples: 500 download_size: 1085393 dataset_size: 4905778 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

StormblessedKal/torgo_imperative_train

StormblessedKal

--- dataset_info: features: - name: Filename dtype: string - name: Transcription dtype: string - name: Pathname dtype: string - name: Audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 165075014.0 num_examples: 854 download_size: 164460754 dataset_size: 165075014.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-sk-unrevised_NoQuant_32_16_0.05_32_BestF1

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 140757 num_examples: 97 - name: test num_bytes: 1213941 num_examples: 743 download_size: 277939 dataset_size: 1354698 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

paolordls/crosslg-sft-qa-sm

paolordls

--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: scenario_id dtype: int64 - name: example_id dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 106775 num_examples: 200 download_size: 52100 dataset_size: 106775 configs: - config_name: default data_files: - split: train path: data/train-* ---

AmitIsraeli/pops_10k_depth

AmitIsraeli

--- dataset_info: features: - name: image dtype: image - name: text dtype: string - name: depth_predicted dtype: image splits: - name: train num_bytes: 3197717112.0 num_examples: 10000 download_size: 3196879201 dataset_size: 3197717112.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

youseon/pakdd_table_merge_data_task_1

youseon

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 103509254 num_examples: 9196 download_size: 20719831 dataset_size: 103509254 configs: - config_name: default data_files: - split: train path: data/train-* ---

CNX-PathLLM/ECCV-2024-close

CNX-PathLLM

--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: slide_id dtype: string - name: project dtype: string - name: f1024 dtype: string - name: f2048 dtype: string - name: f4096 dtype: string splits: - name: train num_bytes: 1863837.034026465 num_examples: 2947 - name: test num_bytes: 396033.3316892725 num_examples: 632 download_size: 429899 dataset_size: 2259870.3657157375 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

Hotpot-Killer/instructpg-dataset

Hotpot-Killer

--- license: mit ---

PbOE1209/IndicPublicSpeech_kn

PbOE1209

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: original_text dtype: string - name: translation dtype: string - name: language dtype: string splits: - name: train num_bytes: 34591589576.048 num_examples: 11504 download_size: 30860056732 dataset_size: 34591589576.048 configs: - config_name: default data_files: - split: train path: data/train-* ---

asadfgglie/BanBan_2024-10-17-facial_expressions-nli

asadfgglie

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': not_entailment splits: - name: train num_bytes: 645894 num_examples: 4726 download_size: 94897 dataset_size: 645894 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "BanBan_2024-10-17-facial_expressions-nli" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

rashid0784/common_voice_audio_quality_enhancement_v3

rashid0784

--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: original file dtype: audio - name: generated noisy audio dtype: audio - name: accent dtype: string splits: - name: train num_bytes: 9932306058.536 num_examples: 115104 - name: test num_bytes: 1236121698.6 num_examples: 15980 download_size: 6803617592 dataset_size: 11168427757.136 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

neurograce/SubstationDataset

neurograce

--- license: apache-2.0 ---

Jhonatan321/Datasets_Jhona

Jhonatan321

--- license: mit task_categories: - text-generation language: - es tags: - code ---

allenai/tulu-3-sft-reused-off-policy

allenai

--- dataset_info: features: - name: id dtype: string - name: prompt dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 584556391 num_examples: 96911 download_size: 301118275 dataset_size: 584556391 configs: - config_name: default data_files: - split: train path: data/train-* --- <img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu-3/Tulu3-logo.png" alt="Tulu3 banner" width="400" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Llama 3.1 Tulu 3 SFT reused (off-policy) *Note that this collection is licensed under ODC-BY-1.0 license; different licenses apply to subsets of the data. Some portions of the dataset are non-commercial. We present the mixture as a research artifact.* This preference dataset is part of our Tulu 3 preference mixture: it contains prompts from our [SFT mixture](https://huggingface.co/datasets/allenai/tulu-3-sft-mixture) and it contains 96,911 generation pairs obtained using the following models: - [Mistral 7B Instruct v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) (Apache 2.0) - [Mistral Nemo Instruct 2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407) (Apache 2.0) - [Tulu 2 7B](https://huggingface.co/allenai/tulu-2-7b) (Ai2 ImpACT Low Risk License) - [Tulu 2 13B](https://huggingface.co/allenai/tulu-2-13b) (Ai2 ImpACT Low Risk License) - [Yi-34B-Chat](https://huggingface.co/01-ai/Yi-34B-Chat) (Apache 2.0) - [Yi-6B-Chat](https://huggingface.co/01-ai/Yi-6B-Chat) (Apache 2.0) - [MPT 30B Chat](https://huggingface.co/mosaicml/mpt-30b-chat) (CC-BY-SA-4.0) - [MPT 7B 8k Chat](https://huggingface.co/mosaicml/mpt-7b-8k-chat) (CC-BY-SA-4.0) - [Google Gemma 2 27B it](https://huggingface.co/google/gemma-2-27b-it) (Gemma is provided under and subject to the Gemma Terms of Use found at [ai.google.dev/gemma/terms](https://ai.google.dev/gemma/terms)) - [Google Gemma 2 9B it](https://huggingface.co/google/gemma-2-9b-it) (Gemma is provided under and subject to the Gemma Terms of Use found at [ai.google.dev/gemma/terms](https://ai.google.dev/gemma/terms)) - [InternLM2.5 20B](https://huggingface.co/internlm/internlm2_5-20b-chat) (InternLM weights are fully open for academic research and also allow free commercial usage. A commercial license can be obtained as instructed in the model card.) - [InternLM2.5 7B](https://huggingface.co/internlm/internlm2_5-7b-chat) (InternLM weights are fully open for academic research and also allow free commercial usage. A commercial license can be obtained as instructed in the model card.) - [InternLM2.5 1.8B](https://huggingface.co/internlm/internlm2_5-1_8b-chat) (InternLM weights are fully open for academic research and also allow free commercial usage. A commercial license can be obtained as instructed in the model card.) - [Falcon 7B](https://huggingface.co/tiiuae/falcon-7b-instruct) (Apache 2.0) - [Qwen2.5 72B Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) (Qwen is licensed under the Qwen LICENSE AGREEMENT, Copyright (c) Alibaba Cloud. All Rights Reserved.) - [Qwen2.5 32B Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) (Apache 2.0) - [Qwen2.5 14B Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) (Apache 2.0) - [Qwen2.5 7B Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) (Apache 2.0) - [Llama 3.1 8B Instruct ](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) (this dataset was partially "Built with Llama" and is thus subject to the Llama 3.1 License) - [Llama 3.1 70B Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) (this dataset was partially "Built with Llama" and is thus subject to the Llama 3.1 License) - [Llama 3 8B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B) (this dataset was partially "Built with Meta Llama 3" and is thus subject to the Llama 3 License) - [GPT-4 Turbo](https://openai.com/index/new-models-and-developer-products-announced-at-devday/) and [GPT-4o](https://openai.com/index/hello-gpt-4o/) (Outputs produced by GPT-4 are subject to OpenAI's [terms of use](https://openai.com/policies/row-terms-of-use)) - [Claude 3.5 Sonnet](https://www.anthropic.com/news/claude-3-5-sonnet) (Outputs produced by Claude are subject to Anthropic [terms of service](https://www.anthropic.com/legal/commercial-terms) and [usage policy](https://www.anthropic.com/legal/aup)) ## License This dataset is licensed under ODC-BY. It is intended for research and educational use in accordance with Ai2's [Responsible Use Guidelines](https://allenai.org/responsible-use). This dataset includes output data generated from third party models that are subject to separate terms governing their use.

allenai/tulu-3-wildchat-reused-on-policy-8b

allenai

--- dataset_info: features: - name: id dtype: string - name: prompt dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 141797370 num_examples: 17207 download_size: 84322720 dataset_size: 141797370 configs: - config_name: default data_files: - split: train path: data/train-* --- <img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu-3/Tulu3-logo.png" alt="Tulu3 banner" width="400" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Llama 3.1 Tulu 3 Wildchat reused (on-policy 8B) *Note that this collection is licensed under ODC-BY-1.0 license; different licenses apply to subsets of the data. Some portions of the dataset are non-commercial. We present the mixture as a research artifact.* This preference dataset is part of our Tulu 3 preference mixture: it contains prompts from [WildChat](allenai/WildChat-1M) and it contains 17,207 generation pairs (some of which on-policy completions from https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B) obtained using the following models: - [Mistral 7B Instruct v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) (Apache 2.0) - [Mistral Nemo Instruct 2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407) (Apache 2.0) - [Tulu 2 7B](https://huggingface.co/allenai/tulu-2-7b) (Ai2 ImpACT Low Risk License) - [Tulu 2 13B](https://huggingface.co/allenai/tulu-2-13b) (Ai2 ImpACT Low Risk License) - [Yi-34B-Chat](https://huggingface.co/01-ai/Yi-34B-Chat) (Apache 2.0) - [Yi-6B-Chat](https://huggingface.co/01-ai/Yi-6B-Chat) (Apache 2.0) - [MPT 30B Chat](https://huggingface.co/mosaicml/mpt-30b-chat) (CC-BY-SA-4.0) - [MPT 7B 8k Chat](https://huggingface.co/mosaicml/mpt-7b-8k-chat) (CC-BY-SA-4.0) - [Google Gemma 2 27B it](https://huggingface.co/google/gemma-2-27b-it) (Gemma is provided under and subject to the Gemma Terms of Use found at [ai.google.dev/gemma/terms](https://ai.google.dev/gemma/terms)) - [Google Gemma 2 9B it](https://huggingface.co/google/gemma-2-9b-it) (Gemma is provided under and subject to the Gemma Terms of Use found at [ai.google.dev/gemma/terms](https://ai.google.dev/gemma/terms)) - [InternLM2.5 20B](https://huggingface.co/internlm/internlm2_5-20b-chat) (InternLM weights are fully open for academic research and also allow free commercial usage. A commercial license can be obtained as instructed in the model card.) - [InternLM2.5 7B](https://huggingface.co/internlm/internlm2_5-7b-chat) (InternLM weights are fully open for academic research and also allow free commercial usage. A commercial license can be obtained as instructed in the model card.) - [InternLM2.5 1.8B](https://huggingface.co/internlm/internlm2_5-1_8b-chat) (InternLM weights are fully open for academic research and also allow free commercial usage. A commercial license can be obtained as instructed in the model card.) - [Falcon 7B](https://huggingface.co/tiiuae/falcon-7b-instruct) (Apache 2.0) - [Qwen2.5 72B Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) (Qwen is licensed under the Qwen LICENSE AGREEMENT, Copyright (c) Alibaba Cloud. All Rights Reserved.) - [Qwen2.5 32B Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) (Apache 2.0) - [Qwen2.5 14B Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) (Apache 2.0) - [Qwen2.5 7B Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) (Apache 2.0) - [Llama 3.1 8B Instruct ](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) (this dataset was partially "Built with Llama" and is thus subject to the Llama 3.1 License) - [Llama 3.1 70B Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) (this dataset was partially "Built with Llama" and is thus subject to the Llama 3.1 License) - [Llama 3 8B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B) (this dataset was partially "Built with Meta Llama 3" and is thus subject to the Llama 3 License) - [GPT-4 Turbo](https://openai.com/index/new-models-and-developer-products-announced-at-devday/) and [GPT-4o](https://openai.com/index/hello-gpt-4o/) (Outputs produced by GPT-4 are subject to OpenAI's [terms of use](https://openai.com/policies/row-terms-of-use)) - [Claude 3.5 Sonnet](https://www.anthropic.com/news/claude-3-5-sonnet) (Outputs produced by Claude are subject to Anthropic [terms of service](https://www.anthropic.com/legal/commercial-terms) and [usage policy](https://www.anthropic.com/legal/aup)) ## Completion Generation Approach: Given a set of prompts, we generated the completions and preferences using a synthetic pipeline that combines both on-policy and off-policy data, and obtained the preference annotations on four different aspects using the Ultrafeedback template and an LLM judge. The code for the synthetic generation pipeline is found in the scripts/synth_pref directory of [open-instruct](https://github.com/allenai/open-instruct/) ## License This dataset is licensed under ODC-BY. It is intended for research and educational use in accordance with Ai2's [Responsible Use Guidelines](https://allenai.org/responsible-use). This dataset includes output data generated from third party models that are subject to separate terms governing their use.

allenai/llama-3.1-tulu-3-70b-preference-mixture

allenai

--- dataset_info: features: - name: id dtype: string - name: prompt dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: source dtype: string splits: - name: train num_bytes: 2282537650 num_examples: 334367 download_size: 1229520687 dataset_size: 2282537650 configs: - config_name: default data_files: - split: train path: data/train-* license: odc-by size_categories: - 100K<n<1M --- <img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu-3/Tulu3-logo.png" alt="Tulu3 banner" width="400" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Llama 3.1 Tulu 3 70B Preference Mixture *Note that this collection is licensed under ODC-BY-1.0 license; different licenses apply to subsets of the data. Some portions of the dataset are non-commercial. We present the mixture as a research artifact.* This preference mixture used for DPO on our the [Llama 3.1 Tulu 3 70B SFT](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B-SFT) checkpoint to obtain [Llama 3.1 Tulu 3 70B DPO](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B-DPO). This mix is made up from the following preference datasets: - https://huggingface.co/datasets/allenai/tulu-3-sft-reused-off-policy - https://huggingface.co/datasets/allenai/tulu-3-sft-reused-on-policy-70b - https://huggingface.co/datasets/allenai/tulu-3-wildchat-if-on-policy-70b - https://huggingface.co/datasets/allenai/tulu-3-IF-augmented-on-policy-70b - https://huggingface.co/datasets/allenai/tulu-3-wildchat-unused - https://huggingface.co/datasets/allenai/tulu-3-wildchat-reused-on-policy-70b - https://huggingface.co/datasets/allenai/tulu-3-ultrafeedback-cleaned-on-policy-70b It contains 334,367 generation pairs obtained using the following models: - [Mistral 7B Instruct v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) (Apache 2.0) - [Mistral Nemo Instruct 2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407) (Apache 2.0) - [Tulu 2 7B](https://huggingface.co/allenai/tulu-2-7b) (Ai2 ImpACT Low Risk License) - [Tulu 2 13B](https://huggingface.co/allenai/tulu-2-13b) (Ai2 ImpACT Low Risk License) - [Yi-34B-Chat](https://huggingface.co/01-ai/Yi-34B-Chat) (Apache 2.0) - [Yi-6B-Chat](https://huggingface.co/01-ai/Yi-6B-Chat) (Apache 2.0) - [MPT 30B Chat](https://huggingface.co/mosaicml/mpt-30b-chat) (CC-BY-SA-4.0) - [MPT 7B 8k Chat](https://huggingface.co/mosaicml/mpt-7b-8k-chat) (CC-BY-SA-4.0) - [Google Gemma 2 27B it](https://huggingface.co/google/gemma-2-27b-it) (Gemma is provided under and subject to the Gemma Terms of Use found at [ai.google.dev/gemma/terms](https://ai.google.dev/gemma/terms)) - [Google Gemma 2 9B it](https://huggingface.co/google/gemma-2-9b-it) (Gemma is provided under and subject to the Gemma Terms of Use found at [ai.google.dev/gemma/terms](https://ai.google.dev/gemma/terms)) - [InternLM2.5 20B](https://huggingface.co/internlm/internlm2_5-20b-chat) (InternLM weights are fully open for academic research and also allow free commercial usage. A commercial license can be obtained as instructed in the model card.) - [InternLM2.5 7B](https://huggingface.co/internlm/internlm2_5-7b-chat) (InternLM weights are fully open for academic research and also allow free commercial usage. A commercial license can be obtained as instructed in the model card.) - [InternLM2.5 1.8B](https://huggingface.co/internlm/internlm2_5-1_8b-chat) (InternLM weights are fully open for academic research and also allow free commercial usage. A commercial license can be obtained as instructed in the model card.) - [Falcon 7B](https://huggingface.co/tiiuae/falcon-7b-instruct) (Apache 2.0) - [Qwen2.5 72B Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) (Qwen is licensed under the Qwen LICENSE AGREEMENT, Copyright (c) Alibaba Cloud. All Rights Reserved.) - [Qwen2.5 32B Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) (Apache 2.0) - [Qwen2.5 14B Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) (Apache 2.0) - [Qwen2.5 7B Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) (Apache 2.0) - [Llama 3.1 8B Instruct ](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) (this dataset was partially "Built with Llama" and is thus subject to the Llama 3.1 License) - [Llama 3.1 70B Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) (this dataset was partially "Built with Llama" and is thus subject to the Llama 3.1 License) - [Llama 3 8B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B) (this dataset was partially "Built with Meta Llama 3" and is thus subject to the Llama 3 License) - [GPT-4 Turbo](https://openai.com/index/new-models-and-developer-products-announced-at-devday/) and [GPT-4o](https://openai.com/index/hello-gpt-4o/) (Outputs produced by GPT-4 are subject to OpenAI's [terms of use](https://openai.com/policies/row-terms-of-use)) - [Claude 3.5 Sonnet](https://www.anthropic.com/news/claude-3-5-sonnet) (Outputs produced by Claude are subject to Anthropic [terms of service](https://www.anthropic.com/legal/commercial-terms) and [usage policy](https://www.anthropic.com/legal/aup)) ### Model Family | **Stage** | **Llama 3.1 8B** | **Llama 3.1 70B** | |----------------------|----------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------| | **Base Model** | [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) | [meta-llama/Llama-3.1-70B](https://huggingface.co/meta-llama/Llama-3.1-70B) | | **SFT** | [allenai/Llama-3.1-Tulu-3-8B-SFT](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-SFT) | [allenai/Llama-3.1-Tulu-3-70B-SFT](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B-SFT) | | **DPO** | [allenai/Llama-3.1-Tulu-3-8B-DPO](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-DPO) | [allenai/Llama-3.1-Tulu-3-70B-DPO](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B-DPO) | | **Final Models (RLVR)** | [allenai/Llama-3.1-Tulu-3-8B](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B) | [allenai/Llama-3.1-Tulu-3-70B](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B) | | **Reward Model (RM)**| [allenai/Llama-3.1-Tulu-3-8B-RM](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-RM) | (Same as 8B) | ## License This dataset is licensed under ODC-BY. It is intended for research and educational use in accordance with Ai2's [Responsible Use Guidelines](https://allenai.org/responsible-use). This dataset includes output data generated from third party models that are subject to separate terms governing their use. ## Citation If Tülu3 or any of the related materials were helpful to your work, please cite: ``` @article{lambert2024tulu3, title = {Tülu 3: Pushing Frontiers in Open Language Model Post-Training}, author = { Nathan Lambert and Jacob Morrison and Valentina Pyatkin and Shengyi Huang and Hamish Ivison and Faeze Brahman and Lester James V. Miranda and Alisa Liu and Nouha Dziri and Shane Lyu and Yuling Gu and Saumya Malik and Victoria Graf and Jena D. Hwang and Jiangjiang Yang and Ronan Le Bras and Oyvind Tafjord and Chris Wilhelm and Luca Soldaini and Noah A. Smith and Yizhong Wang and Pradeep Dasigi and Hannaneh Hajishirzi }, year = {2024}, email = {tulu@allenai.org} } ```

lamblamb/from_news_to_forecast

lamblamb

--- license: mit --- ``` @inproceedings{wang2024newsforecast, title={From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflection}, author={Wang, Xinlei and Feng, Maike and Qiu, Jing and Gu, Jinjin and Zhao, Junhua}, booktitle={Neural Information Processing Systems}, year={2024} } ```

jamesnatulan/cuelang

jamesnatulan

--- dataset_info: features: - name: repo_path dtype: string - name: repo_name dtype: string - name: url dtype: string - name: content dtype: string splits: - name: train num_bytes: 88645676 num_examples: 13235 download_size: 20801533 dataset_size: 88645676 configs: - config_name: default data_files: - split: train path: data/train-* ---

STL-Yonsei/SWIR-Fruit_and_Vegetable_Dataset

STL-Yonsei

--- license: cc-by-nc-4.0 ---

1231czx/test_ver2_rebuttal_af_rm_bon64_005

1231czx

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string - name: generator dtype: string splits: - name: train num_bytes: 1288815 num_examples: 805 download_size: 775756 dataset_size: 1288815 configs: - config_name: default data_files: - split: train path: data/train-* ---

1231czx/test_ver2_rebuttal_af_rrm_bon8_005

1231czx

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string - name: generator dtype: string splits: - name: train num_bytes: 1476291 num_examples: 805 download_size: 879535 dataset_size: 1476291 configs: - config_name: default data_files: - split: train path: data/train-* ---

spow12/llava_instruct_mix_jp

spow12

--- dataset_info: features: - name: image dtype: image - name: conversations list: - name: content dtype: string - name: role dtype: string - name: en_conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 11259562724.229902 num_examples: 258968 download_size: 10767717705 dataset_size: 11259562724.229902 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - visual-question-answering language: - ja --- Translated using ChatWaifu_12B_v2.2(private) # Prompt ```python prompt = [ { 'role': 'user', 'content': [ { 'type': 'text', 'text': f"""Translate the sentece to japanese. If there is any structure like markdown table or chart, using original format. Here is the sentence to translate: 36.76""" }, ] }, { 'role': 'assistant', 'content': [ { 'type': 'text', 'text': f"""36.76""" }, ] }, { 'role': 'user', 'content': [ { 'type': 'text', 'text': f"""Translate the sentece to japanese. If there is any structure like markdown table or chart, using original format. Here is the sentence to translate: Sure! Here's the extracted results written in markdown\n| Characteristic | Forecast spend in billion GBP |\n|:----------------------|--------------------------------:|\n| Food and grocery | 148.3 |\n| Clothing and footwear | 46.4 |\n| Home | 37.4 |\n| Electricals | 21.2 |\n| Health and beauty | 18.5 |""" }, ] }, { 'role': 'assistant', 'content': [ { 'type': 'text', 'text': f"""以下はマークダウン形式で書かれた抽出された結果です。 特徴 | 予測支出（十億GBP） -------------------|----------------------------- 食品と日用雑貨 | 148.3 衣服と靴 | 46.4 住宅 | 37.4 電気製品 | 21.2 健康と美容 | 18.5""" }, ] }, { 'role': 'user', 'content': [ { 'type': 'text', 'text': f"""Translate the sentece to japanese. If there is any structure like markdown table or chart, using original format. Here is the sentence to translate: Yes""" }, ] }, { 'role': 'assistant', 'content': [ { 'type': 'text', 'text': f"""はい""" }, ] }, { 'role': 'user', 'content': [ { 'type': 'text', 'text': f"""Translate the sentece to japanese. If there is any structure like markdown table or chart, using original format. Here is the sentence to translate: Maxime Barbier""" }, ] }, { 'role': 'assistant', 'content': [ { 'type': 'text', 'text': f"""マキシム・バルビエ""" }, ] }, { 'role': 'user', 'content': [ { 'type': 'text', 'text': f"""Translate the sentece to japanese. If there is any structure like markdown table or chart, using original format. Here is the sentence to translate: Can you extract the full data and reformat it as a markdown table?""" }, ] }, { 'role': 'assistant', 'content': [ { 'type': 'text', 'text': f"""全データを抽出して、マークダウン・テーブルとして再フォーマットできますか？""" }, ] }, { 'role': 'user', 'content': [ { 'type': 'text', 'text': f"""Translate the sentece to japanese. If there is any structure like markdown table or chart, using original format. Here is the sentence to translate {text}""" }, ] }, ] ``` # Caution! This dataset may contain errors or incorrect translations, even after filtering. # Original Dataset [theblackcat102/llava-instruct-mix](https://huggingface.co/datasets/theblackcat102/llava-instruct-mix)

deepklarity/top_chrome_extensions

deepklarity

--- license: apache-2.0 --- ### Top Chrome extensions dataset This dataset contains data of around 4000 rows the top chrome extensions available on [Chrome web store](https://chromewebstore.google.com/?pli=1) The dataset was scraped in `October-2024`. We aim to use this dataset to perform analysis and identify trends and get a bird's eye view of Chrome extension ecosystem and build a extension recommender. #### Mantainers - [Nishritha Damera](https://www.linkedin.com/in/nishrithadamera/)

JayKr/Dog_Eye_Research_Dataset

JayKr

--- dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 2106105 num_examples: 10410 download_size: 844470 dataset_size: 2106105 configs: - config_name: default data_files: - split: train path: data/train-* ---

SHASWATSINGH3101/key_info_bail_para_prompts_TRAIN_V1

SHASWATSINGH3101

--- license: cc-by-nc-nd-4.0 ---

cyberorigin/alita

cyberorigin

--- license: mit --- ## CyberOrigin Dataset Our data includes information from home services, the logistics industry, and laboratory scenarios. For more details, please refer to our [Offical Data Website](https://cyberorigin2077.github.io/) contents of the dataset: ``` alita # dataset root path └── data/ ├── metadata_Traj01.json ├── segment_ids_Traj01.bin # for each frame segment_ids uniquely points to the segment index that frame i came from. You may want to use this to separate non-contiguous frames from different videos (transitions). ├── videos_Traj01.bin # 16x16 image patches at 15hz, each patch is vector-quantized into 2^18 possible integer values. These can be decoded into 256x256 RGB images using the provided magvit2.ckpt weights. ├── ... └── ... ``` ```json { "task": "ALITA", "total_episodes": , "total_frames": , "token_dtype": "uint32", "vocab_size": 262144, "fps": 15, "language_annotation": "None", } ```

bkk21/triper-gemma-10-dataset

bkk21

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: korean_v1 num_bytes: 54303611 num_examples: 53070 download_size: 1252931 dataset_size: 54303611 configs: - config_name: default data_files: - split: korean_v1 path: data/korean_v1-* ---

hyunsoow/custom_drug_dataset

hyunsoow

--- dataset_info: features: - name: patient_id dtype: int64 - name: drugName dtype: string - name: condition dtype: string - name: review dtype: string - name: rating dtype: float64 - name: date dtype: string - name: usefulCount dtype: int64 - name: review_length dtype: int64 splits: - name: train num_bytes: 65975578 num_examples: 110811 - name: validation num_bytes: 16422578 num_examples: 27703 - name: test num_bytes: 27430466 num_examples: 46108 download_size: 63886981 dataset_size: 109828622 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

piporica/custom_drug_dataset

piporica

--- dataset_info: features: - name: patient_id dtype: int64 - name: drugName dtype: string - name: condition dtype: string - name: review dtype: string - name: rating dtype: float64 - name: date dtype: string - name: usefulCount dtype: int64 - name: review_length dtype: int64 splits: - name: train num_bytes: 65975578 num_examples: 110811 - name: validation num_bytes: 16422578 num_examples: 27703 - name: test num_bytes: 27430466 num_examples: 46108 download_size: 63886981 dataset_size: 109828622 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

Lancelot53/temp_qgen_dataset

Lancelot53

--- dataset_info: features: - name: instruction dtype: string - name: question dtype: string - name: output dtype: string - name: generator dtype: string - name: text dtype: string splits: - name: train num_bytes: 219083 num_examples: 195 - name: validation num_bytes: 52562 num_examples: 49 - name: test num_bytes: 51947 num_examples: 49 download_size: 131936 dataset_size: 323592 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

bizb0630/hamza_1.0

bizb0630

--- license: mit task_categories: - translation language: - uz - ru pretty_name: uzbek-russian_parallel_corpora --- # Hamza - Uzbek-Russian parallel corpora. ## Overview **Hamza** is a parallel corpus containing over 15,000 aligned sentences in Uzbek and Russian. ## Creation Created using [lingtrain](https://github.com/averkij/lingtrain-aligner). Text mined from different websites and telegram channels. ### Format The dataset is presented in TMX (Translation Memory eXchange).

Tristepin/quakec-v1

Tristepin

--- license: mit ---

bew/medium-chillhop-tokenized

bew

--- dataset_info: features: - name: tokens sequence: sequence: int16 - name: mask sequence: sequence: bool splits: - name: train num_bytes: 579449352 num_examples: 8313 download_size: 375690299 dataset_size: 579449352 configs: - config_name: default data_files: - split: train path: data/train-* ---

data-is-better-together/10k-prompt-collective-argilla-format

data-is-better-together

--- language: - en size_categories: 10K<n<100K tags: - rlfh - argilla - human-feedback --- # Dataset Card for 10k-prompt-collective-argilla This dataset has been created with [Argilla](https://docs.argilla.io). As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets). ## Dataset Description - **Homepage:** https://argilla.io - **Repository:** https://github.com/argilla-io/argilla - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset contains: * A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla. * Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`. * The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla. ### Load with Argilla To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code: ```python import argilla as rg ds = rg.FeedbackDataset.from_huggingface("DIBT/10k-prompt-collective-argilla") ``` ### Load with `datasets` To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset("DIBT/10k-prompt-collective-argilla") ``` ### Supported Tasks and Leaderboards This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure). There are no leaderboards associated with this dataset. ### Languages [More Information Needed] ## Dataset Structure ### Data in Argilla The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**. The **fields** are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. | Field Name | Title | Type | Required | Markdown | | ---------- | ----- | ---- | -------- | -------- | | prompt | Prompt | text | True | True | The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking. | Question Name | Title | Type | Required | Description | Values/Labels | | ------------- | ----- | ---- | -------- | ----------- | ------------- | | quality | Rate the quality of the prompt | label_selection | True | N/A | ['0', '1', '2', '3', '4'] | The **suggestions** are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata". The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. | Metadata Name | Title | Type | Values | Visible for Annotators | | ------------- | ----- | ---- | ------ | ---------------------- | The **guidelines**, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section. ### Data Instances An example of a dataset instance in Argilla looks as follows: ```json { "external_id": null, "fields": { "prompt": "Provide step-by-step instructions on how to make a safe and effective homemade all-purpose cleaner from common household ingredients. The guide should include measurements, tips for storing the cleaner, and additional variations or scents that can be added. Additionally, the guide should be written in clear and concise language, with helpful visuals or photographs to aid in the process." }, "metadata": { "evolved_from": null, "kind": "synthetic", "source": "ultrachat" }, "responses": [ { "status": "submitted", "user_id": "d23b12c2-b601-490e-b5b3-2040eb393a00", "values": { "quality": { "value": "4" } } }, { "status": "submitted", "user_id": "e2bdd868-f28e-46fc-9254-a6ec1e291889", "values": { "quality": { "value": "4" } } } ], "suggestions": [], "vectors": {} } ``` While the same record in HuggingFace `datasets` looks as follows: ```json { "external_id": null, "metadata": "{\"source\": \"ultrachat\", \"kind\": \"synthetic\", \"evolved_from\": null}", "prompt": "Provide step-by-step instructions on how to make a safe and effective homemade all-purpose cleaner from common household ingredients. The guide should include measurements, tips for storing the cleaner, and additional variations or scents that can be added. Additionally, the guide should be written in clear and concise language, with helpful visuals or photographs to aid in the process.", "quality": [ { "status": "submitted", "user_id": "d23b12c2-b601-490e-b5b3-2040eb393a00", "value": "4" }, { "status": "submitted", "user_id": "e2bdd868-f28e-46fc-9254-a6ec1e291889", "value": "4" } ], "quality-suggestion": null, "quality-suggestion-metadata": { "agent": null, "score": null, "type": null } } ``` ### Data Fields Among the dataset fields, we differentiate between the following: * **Fields:** These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. * **prompt** is of type `text`. * **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`. * **quality** is of type `label_selection` with the following allowed values ['0', '1', '2', '3', '4']. * **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable. * (optional) **quality-suggestion** is of type `label_selection` with the following allowed values ['0', '1', '2', '3', '4']. Additionally, we also have two more fields that are optional and are the following: * **metadata:** This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. * **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file. ### Data Splits The dataset contains a single split, which is `train`. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation guidelines # Task We are collaboratively creating a database of prompts in English. The annotation guidelines below should help you get started but you can also ask questions in the [Discord Channel](https://discord.com/channels/879548962464493619/1205128865735770142). Our aim is to identify effective prompts and understand the interaction between AI-generated and human-generated prompts. The focus is on rating prompts that are clear, interesting and complex for fine-tuning open source LLMs. What makes a prompt good? That's a difficult question but here are some aspects: - The intent of the user is clear. - The question, instruction or task for the assistant is challenging or interesting because it involves solving a complex problem, reasoning, involving being creative, etc. In other words, first of all the intent (what the user asks) should be clear. Then we can look into how interesting and complex is the task. The most interesting the prompt is the higher rating should be. ## Guidelines You need to assign a rating to each prompt thinking about the complexity for an assistant and if the intent is clear. A very good prompt is one that is challenging but also very clear in the intent of the user. You can use keyboard shortcuts (the numbers) to quickly rate the examples. If you find some pattern, you can also use the search box and filters as well as the bulk labelling mode, please use this with care and only when you find a clear pattern (e.g., prompts that are completely incorrect and share a common issue). If you are unsure about your answer, you can click on the tag and then “Save as a draft” to save if for later. In the case that you feel unequipped of rating a specific prompt, you can use the “Discard” button. ## Ratings ### 1. Very Bad: The prompt doesn't communicate its purpose, is non-sensical or is in a language other than English. The prompt assumes the usage of tools or capabilities that don’t apply to this model, like generating an image or scraping a website. *Examples:* >"Do the thing." >“Hello!” >"asdajflajfada” >“Quiero que redactes una entrada de blog.” >"Extract data from a website.” >“Tell me how you feel when someone insults you.” ### 2. Bad: Suggests a goal but lacks clarity and coherence. *Examples:* >"Find me stuff about that thing, you know?" >“Write something.” >"Tell me about this thing." >"Can you help with this?" >"I need to know more." ### 3. Ok: The intent is understandable, but it's missing information to complete the task. *Examples:* >"I need information on something important." >“Write a blogpost.” ### 4. Good: Presents a clear goal and necessary information, effectively directing the AI, but the prompt could be more specific. *Examples:* >"Provide a summary of renewable energy sources." >“Tell me about Sean Connery.” >"Explain global warming." ### 5. Very Good: Comprehensive and explicit, leaving no room for ambiguity. Perfectly guides the AI and includes details. *Examples:* >"Compare the efficiency and environmental impact of solar and wind energy, including recent advancements and case studies from 2023." >“Make a list of 5 plant-based recipes that I can try that don’t have red peppers as an ingredient.” #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

data-is-better-together/dibt-prompt-translation-for-arabic

data-is-better-together

--- dataset_info: features: - name: target dtype: string - name: quality list: - name: status dtype: string - name: user_id dtype: string - name: value dtype: string - name: metadata dtype: string - name: avg_rating dtype: float64 - name: num_responses dtype: int64 - name: agreement_ratio dtype: float64 - name: raw_responses sequence: int64 - name: kind dtype: string - name: source dtype: string splits: - name: train num_bytes: 824861 num_examples: 501 download_size: 391793 dataset_size: 824861 configs: - config_name: default data_files: - split: train path: data/train-* ---

data-is-better-together/MPEP_DUTCH

data-is-better-together

--- size_categories: n<1K tags: - rlfh - argilla - human-feedback --- # Dataset Card for MPEP_DUTCH This dataset has been created with [Argilla](https://docs.argilla.io). As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets). ## Dataset Description - **Homepage:** https://argilla.io - **Repository:** https://github.com/argilla-io/argilla - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset contains: * A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla. * Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`. * The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla. ### Load with Argilla To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code: ```python import argilla as rg ds = rg.FeedbackDataset.from_huggingface("DIBT/MPEP_DUTCH") ``` ### Load with `datasets` To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset("DIBT/MPEP_DUTCH") ``` ### Supported Tasks and Leaderboards This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure). There are no leaderboards associated with this dataset. ### Languages [More Information Needed] ## Dataset Structure ### Data in Argilla The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**. The **fields** are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. | Field Name | Title | Type | Required | Markdown | | ---------- | ----- | ---- | -------- | -------- | | source | Source | text | True | True | The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking. | Question Name | Title | Type | Required | Description | Values/Labels | | ------------- | ----- | ---- | -------- | ----------- | ------------- | | target | Vertaling | text | True | Vertaal de prompt naar het Nederlands. | N/A | The **suggestions** are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata". The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. | Metadata Name | Title | Type | Values | Visible for Annotators | | ------------- | ----- | ---- | ------ | ---------------------- | The **guidelines**, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section. ### Data Instances An example of a dataset instance in Argilla looks as follows: ```json { "external_id": "1788", "fields": { "source": "As an AI enthusiast, you love creating programs that can understand human language. Your latest project involves creating a program that can identify and replace words with their antonyms in a given text. \nTo demonstrate the effectiveness of your program, you decide to test it on a news article about a recent political event. However, to make it more challenging, you also want your program to differentiate between homonyms and use context clues to correctly replace them.\nHere\u0027s a step-by-step explanation of how your program works:\n1. The program reads the input text and identifies all the words that have antonyms.\n2. For each of these words, the program identifies the context in which it appears to determine the correct antonym to use.\n3. The program then replaces the original word with its antonym in the text.\n4. If the original word has multiple meanings, the program uses the context to determine which meaning is intended and replace it with the appropriate antonym.\n5. Finally, the program outputs the modified text with the replaced words.\nCan you put your natural language processing skills to the test and try to identify the antonyms used in the modified text?" }, "metadata": { "evolved_from": null, "kind": "synthetic", "source": "evol_instruct" }, "responses": [ { "status": "submitted", "user_id": "315be0ef-8df8-48df-8ba1-ff3339ef016a", "values": { "target": { "value": "Als een AI-enthousiasteling, houd je ervan om programma\u0027s te maken die de menselijke taal begrijpen. Je nieuwste project is een programma dat woorden kan herkennen en vervangen door hun antoniemen in een stuk tekst.\nOm de effectiviteit van je programma aan te tonen, besluit je het te testen op een nieuwsartikel over een recent politiek evenement. Om het uitdagender te maken, wil je ook dat je programma onderscheid maakt tussen homoniemen, en daarnaast contextuele aanwijzingen gebruikt woorden correct te vervangen.\nHier is een stapsgewijze uitleg van hoe je programma werkt:\n1. Het programma leest de inputtekst en identificeert alle woorden die antoniemen hebben.\n2. Voor elk van deze woorden identificeert het programma de context waarin het verschijnt om het juiste antoniem te bepalen.\n3. Het programma vervangt vervolgens het originele woord door zijn antoniem in de tekst.\n4. Als het originele woord meerdere betekenissen heeft, gebruikt het programma de context om te bepalen welke betekenis bedoeld is en vervangt het door het passende antoniem.\n5. Uiteindelijk geeft het programma de aangepaste tekst uit met de vervangen woorden.\nKun je je vaardigheden in natuurlijke taalverwerking op de proef stellen en proberen de antoniemen te identificeren die in de gewijzigde tekst worden gebruikt?" } } } ], "suggestions": [ { "agent": null, "question_name": "target", "score": null, "type": null, "value": "Als een AI-enthousiasteling, houd je ervan om programma\u0027s te maken die de menselijke taal begrijpen. Je nieuwste project houdt in dat je een programma maakt dat woorden kan identificeren en vervangen door hun antoniemen in een gegeven tekst.\nOm de effectiviteit van je programma aan te tonen, besluit je het te testen op een nieuwsartikel over een recent politiek evenement. Om het echter uitdagender te maken, wil je ook dat je programma onderscheid maakt tussen homoniemen en contextuele aanwijzingen gebruikt om ze correct te vervangen.\nHier is een stapsgewijze uitleg van hoe je programma werkt:\n1. Het programma leest de inputtekst en identificeert alle woorden die antoniemen hebben.\n2. Voor elk van deze woorden identificeert het programma de context waarin het verschijnt om het juiste antoniem te bepalen.\n3. Het programma vervangt vervolgens het originele woord door zijn antoniem in de tekst.\n4. Als het originele woord meerdere betekenissen heeft, gebruikt het programma de context om te bepalen welke betekenis bedoeld is en vervangt het door het passende antoniem.\n5. Uiteindelijk geeft het programma de aangepaste tekst uit met de vervangen woorden.\nKun je je vaardigheden in natuurlijke taalverwerking op de proef stellen en proberen de antoniemen te identificeren die in de gewijzigde tekst worden gebruikt?" } ], "vectors": {} } ``` While the same record in HuggingFace `datasets` looks as follows: ```json { "external_id": "1788", "metadata": "{\"evolved_from\": null, \"kind\": \"synthetic\", \"source\": \"evol_instruct\"}", "source": "As an AI enthusiast, you love creating programs that can understand human language. Your latest project involves creating a program that can identify and replace words with their antonyms in a given text. \nTo demonstrate the effectiveness of your program, you decide to test it on a news article about a recent political event. However, to make it more challenging, you also want your program to differentiate between homonyms and use context clues to correctly replace them.\nHere\u0027s a step-by-step explanation of how your program works:\n1. The program reads the input text and identifies all the words that have antonyms.\n2. For each of these words, the program identifies the context in which it appears to determine the correct antonym to use.\n3. The program then replaces the original word with its antonym in the text.\n4. If the original word has multiple meanings, the program uses the context to determine which meaning is intended and replace it with the appropriate antonym.\n5. Finally, the program outputs the modified text with the replaced words.\nCan you put your natural language processing skills to the test and try to identify the antonyms used in the modified text?", "target": [ { "status": "submitted", "user_id": "315be0ef-8df8-48df-8ba1-ff3339ef016a", "value": "Als een AI-enthousiasteling, houd je ervan om programma\u0027s te maken die de menselijke taal begrijpen. Je nieuwste project is een programma dat woorden kan herkennen en vervangen door hun antoniemen in een stuk tekst.\nOm de effectiviteit van je programma aan te tonen, besluit je het te testen op een nieuwsartikel over een recent politiek evenement. Om het uitdagender te maken, wil je ook dat je programma onderscheid maakt tussen homoniemen, en daarnaast contextuele aanwijzingen gebruikt woorden correct te vervangen.\nHier is een stapsgewijze uitleg van hoe je programma werkt:\n1. Het programma leest de inputtekst en identificeert alle woorden die antoniemen hebben.\n2. Voor elk van deze woorden identificeert het programma de context waarin het verschijnt om het juiste antoniem te bepalen.\n3. Het programma vervangt vervolgens het originele woord door zijn antoniem in de tekst.\n4. Als het originele woord meerdere betekenissen heeft, gebruikt het programma de context om te bepalen welke betekenis bedoeld is en vervangt het door het passende antoniem.\n5. Uiteindelijk geeft het programma de aangepaste tekst uit met de vervangen woorden.\nKun je je vaardigheden in natuurlijke taalverwerking op de proef stellen en proberen de antoniemen te identificeren die in de gewijzigde tekst worden gebruikt?" } ], "target-suggestion": "Als een AI-enthousiasteling, houd je ervan om programma\u0027s te maken die de menselijke taal begrijpen. Je nieuwste project houdt in dat je een programma maakt dat woorden kan identificeren en vervangen door hun antoniemen in een gegeven tekst.\nOm de effectiviteit van je programma aan te tonen, besluit je het te testen op een nieuwsartikel over een recent politiek evenement. Om het echter uitdagender te maken, wil je ook dat je programma onderscheid maakt tussen homoniemen en contextuele aanwijzingen gebruikt om ze correct te vervangen.\nHier is een stapsgewijze uitleg van hoe je programma werkt:\n1. Het programma leest de inputtekst en identificeert alle woorden die antoniemen hebben.\n2. Voor elk van deze woorden identificeert het programma de context waarin het verschijnt om het juiste antoniem te bepalen.\n3. Het programma vervangt vervolgens het originele woord door zijn antoniem in de tekst.\n4. Als het originele woord meerdere betekenissen heeft, gebruikt het programma de context om te bepalen welke betekenis bedoeld is en vervangt het door het passende antoniem.\n5. Uiteindelijk geeft het programma de aangepaste tekst uit met de vervangen woorden.\nKun je je vaardigheden in natuurlijke taalverwerking op de proef stellen en proberen de antoniemen te identificeren die in de gewijzigde tekst worden gebruikt?", "target-suggestion-metadata": { "agent": null, "score": null, "type": null } } ``` ### Data Fields Among the dataset fields, we differentiate between the following: * **Fields:** These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. * **source** is of type `text`. * **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`. * **target** is of type `text`, and description "Vertaal de prompt naar het Nederlands.". * **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable. * (optional) **target-suggestion** is of type `text`. Additionally, we also have two more fields that are optional and are the following: * **metadata:** This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. * **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file. ### Data Splits The dataset contains a single split, which is `train`. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation guidelines ## Leaderboard Zie de voortgang op het [Leaderboard](https://huggingface.co/spaces/DIBT-Dutch/prompt-translation). ## Hulp bij het vertalen - Er staat een vertaalsuggestie klaar die door GPT-4 gemaakt is. Deze mag je aanpassen zoveel als je wilt, of je mag hem verwijderen en helemaal zelf vertalen. - Je wordt aangemoedigd om het taalgebruik aan te passen om de prompt voor jou natuurlijker te laten klinken - Prompts die erg specifieke vragen stellen over niet-Nederlandstalige gebieden mag je vernederlandsen. Bijvoorbeeld: in plaats van Amerikaanse staten kun je Nederlandse of Belgische provincies gebruiken. - Maak zelf een inschatting of en hoe je bepaalde technische termen (bijvoorbeeld wetenschappelijke termen of programmeertermen) wilt vertalen. Wetenschappelijke termen in het Nederlands kan je vaak het makkelijkst terugvinden door de Engelstalige Wikipedia-pagina op te zoeken, en vanaf daar door te klikken naar de Nederlandstalige variant. - Soms heeft GPT-4 de prompt ook uitgevoerd in plaats van alleen vertaald. In dat geval mag je de uitvoer gewoon verwijderen. ## Discord Vragen of onduidelijkheden? Kom ons opzoeken in het [Discord-kanaal](https://discord.com/channels/879548962464493619/1217585262595408034)! #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

data-is-better-together/MPEP_GERMAN

data-is-better-together

--- size_categories: n<1K tags: - rlfh - argilla - human-feedback --- # Dataset Card for MPEP_GERMAN This dataset has been created with [Argilla](https://docs.argilla.io). As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets). ## Dataset Description - **Homepage:** https://argilla.io - **Repository:** https://github.com/argilla-io/argilla - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset contains: * A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla. * Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`. * The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla. ### Load with Argilla To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code: ```python import argilla as rg ds = rg.FeedbackDataset.from_huggingface("DIBT/MPEP_GERMAN") ``` ### Load with `datasets` To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset("DIBT/MPEP_GERMAN") ``` ### Supported Tasks and Leaderboards This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure). There are no leaderboards associated with this dataset. ### Languages [More Information Needed] ## Dataset Structure ### Data in Argilla The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**. The **fields** are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. | Field Name | Title | Type | Required | Markdown | | ---------- | ----- | ---- | -------- | -------- | | source | Prompt | text | True | True | The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking. | Question Name | Title | Type | Required | Description | Values/Labels | | ------------- | ----- | ---- | -------- | ----------- | ------------- | | target | Target | text | True | Überprüfe die Übersetzung und passe sie an, falls notwendig. Weitere Hinweise in den Annotation Guidelines. | N/A | The **suggestions** are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata". The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. | Metadata Name | Title | Type | Values | Visible for Annotators | | ------------- | ----- | ---- | ------ | ---------------------- | The **guidelines**, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section. ### Data Instances An example of a dataset instance in Argilla looks as follows: ```json { "external_id": "5996", "fields": { "source": "Can you provide two examples of metaphors using an Excel table format? \nHere is an Excel table with two examples of metaphors:\n| Metaphor | Meaning |\n| ------------- |:-------------:|\n| Life is a journey | Life can be compared to a journey with ups and downs |\n| Love is a rose | Love can be compared to a delicate and beautiful rose |\nCan you provide two more examples of metaphors using a MATLAB code format?" }, "metadata": { "evolved_from": null, "kind": "synthetic", "source": "evol_instruct" }, "responses": [ { "status": "submitted", "user_id": "a26ea56e-a953-4474-ad7c-5d66cd0d35f8", "values": { "target": { "value": "Kannst du zwei Beispiele f\u00fcr Metaphern im Excel-Tabellenformat liefern?\n\nHier ist eine Excel-Tabelle mit zwei Beispielen f\u00fcr Metaphern:\n\n| Metapher | Bedeutung |\n\n| ------------- |:-------------:|\n\n| Das Leben ist eine Reise | Das Leben kann mit einer Reise mit H\u00f6hen und Tiefen verglichen werden |\n\n| Liebe ist wie eine Rose | Liebe kann mit einer zarten und sch\u00f6nen Rose verglichen werden |\n\nKannst du zwei weitere Beispiele f\u00fcr Metaphern in einem MATLAB-Codeformat liefern?" } } } ], "suggestions": [ { "agent": null, "question_name": "target", "score": null, "type": null, "value": "Kannst du zwei Beispiele f\u00fcr Metaphern in einem Excel-Tabellenformat liefern?\n\nHier ist eine Excel-Tabelle mit zwei Beispielen f\u00fcr Metaphern:\n\n| Metapher | Bedeutung |\n\n| ------------- |:-------------:|\n\n| Das Leben ist eine Reise | Das Leben kann mit einer Reise mit H\u00f6hen und Tiefen verglichen werden |\n\n| Liebe ist eine Rose | Liebe kann mit einer zarten und sch\u00f6nen Rose verglichen werden |\n\nKannst du zwei weitere Beispiele f\u00fcr Metaphern in einem MATLAB-Codeformat liefern?" } ], "vectors": {} } ``` While the same record in HuggingFace `datasets` looks as follows: ```json { "external_id": "5996", "metadata": "{\"source\": \"evol_instruct\", \"kind\": \"synthetic\", \"evolved_from\": null}", "source": "Can you provide two examples of metaphors using an Excel table format? \nHere is an Excel table with two examples of metaphors:\n| Metaphor | Meaning |\n| ------------- |:-------------:|\n| Life is a journey | Life can be compared to a journey with ups and downs |\n| Love is a rose | Love can be compared to a delicate and beautiful rose |\nCan you provide two more examples of metaphors using a MATLAB code format?", "target": [ { "status": "submitted", "user_id": "a26ea56e-a953-4474-ad7c-5d66cd0d35f8", "value": "Kannst du zwei Beispiele f\u00fcr Metaphern im Excel-Tabellenformat liefern?\n\nHier ist eine Excel-Tabelle mit zwei Beispielen f\u00fcr Metaphern:\n\n| Metapher | Bedeutung |\n\n| ------------- |:-------------:|\n\n| Das Leben ist eine Reise | Das Leben kann mit einer Reise mit H\u00f6hen und Tiefen verglichen werden |\n\n| Liebe ist wie eine Rose | Liebe kann mit einer zarten und sch\u00f6nen Rose verglichen werden |\n\nKannst du zwei weitere Beispiele f\u00fcr Metaphern in einem MATLAB-Codeformat liefern?" } ], "target-suggestion": "Kannst du zwei Beispiele f\u00fcr Metaphern in einem Excel-Tabellenformat liefern?\n\nHier ist eine Excel-Tabelle mit zwei Beispielen f\u00fcr Metaphern:\n\n| Metapher | Bedeutung |\n\n| ------------- |:-------------:|\n\n| Das Leben ist eine Reise | Das Leben kann mit einer Reise mit H\u00f6hen und Tiefen verglichen werden |\n\n| Liebe ist eine Rose | Liebe kann mit einer zarten und sch\u00f6nen Rose verglichen werden |\n\nKannst du zwei weitere Beispiele f\u00fcr Metaphern in einem MATLAB-Codeformat liefern?", "target-suggestion-metadata": { "agent": null, "score": null, "type": null } } ``` ### Data Fields Among the dataset fields, we differentiate between the following: * **Fields:** These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. * **source** is of type `text`. * **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`. * **target** is of type `text`, and description "Überprüfe die Übersetzung und passe sie an, falls notwendig. Weitere Hinweise in den Annotation Guidelines.". * **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable. * (optional) **target-suggestion** is of type `text`. Additionally, we also have two more fields that are optional and are the following: * **metadata:** This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. * **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file. ### Data Splits The dataset contains a single split, which is `train`. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation guidelines # Leaderboard Verfolge den Fortschritt auf unserem [Leaderboard](https://huggingface.co/spaces/DIBT-German/DIBT-German-Dashboard). # Hinweise zur Übersetzung - Für jeden Prompt gibt es einen Übersetzungsvorschlag, der von GPT-4 generiert wurde. Du kannst diesen Vorschlag bearbeiten und anpassen, oder ihn entfernen und selbst übersetzen. - Du kannst die Ausdrucksweise anpassen, damit der Prompt natürlicher klingt. - Prompts, die sehr spezifische Angaben zu nicht deutschsprachigen Gebieten machen, können übertragen statt wörtlich übersetzt werden. Anstelle von US-Bundesstaaten kannst Du z.B. deutsche Bundesländer verwenden. - Entscheide selbst, ob und wie Du bestimmte Fachbegriffe übersetzen möchtest (z.B. wissenschaftliche oder Programmierbegriffe). Wissenschaftliche Begriffe auf Deutsch lassen sich oft am einfachsten finden, indem man die englische Wikipedia-Seite aufruft und sich von dort zur deutschen Version durchklickt. - Manchmal enthalten die Übersetzungsvorschläge von GPT-4 auch Antworten auf die englischsprachigen Prompts, anstatt sie nur zu übersetzen. In diesem Fall kannst Du die über die Übersetzung hinausgehende Ausgabe einfach löschen. # Mehr erfahren Du möchtest mehr über das Multilingual Prompt Evaluation Project erfahren? Besuche das Data is Better Together Repository auf [Github](https://github.com/huggingface/data-is-better-together/tree/main). Dort findest Du auch Informationen zu anderen Sprachen, an denen bereits gearbeitet wird. # Kontakt Hast Du Fragen oder Wünsche? Sprich uns gerne auf [Discord](https://discord.com/channels/879548962464493619/1217179426002047076) an. #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

data-is-better-together/MPEP_ARABIC

data-is-better-together

--- size_categories: n<1K tags: - rlfh - argilla - human-feedback --- # Dataset Card for MPEP_ARABIC This dataset has been created with [Argilla](https://docs.argilla.io). As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets). ## Dataset Description - **Homepage:** https://argilla.io - **Repository:** https://github.com/argilla-io/argilla - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset contains: * A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla. * Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`. * The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla. ### Load with Argilla To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code: ```python import argilla as rg ds = rg.FeedbackDataset.from_huggingface("DIBT/MPEP_ARABIC") ``` ### Load with `datasets` To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset("DIBT/MPEP_ARABIC") ``` ### Supported Tasks and Leaderboards This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure). There are no leaderboards associated with this dataset. ### Languages [More Information Needed] ## Dataset Structure ### Data in Argilla The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**. The **fields** are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. | Field Name | Title | Type | Required | Markdown | | ---------- | ----- | ---- | -------- | -------- | | source | Source | text | True | True | The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking. | Question Name | Title | Type | Required | Description | Values/Labels | | ------------- | ----- | ---- | -------- | ----------- | ------------- | | target | Target | text | True | Translate the text. | N/A | The **suggestions** are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata". The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. | Metadata Name | Title | Type | Values | Visible for Annotators | | ------------- | ----- | ---- | ------ | ---------------------- | The **guidelines**, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section. ### Data Instances An example of a dataset instance in Argilla looks as follows: ```json { "external_id": null, "fields": { "source": "If a recipe calls for 2 1/2 cups of sugar and you want to make a half portion of it, calculate the exact amount of sugar needed." }, "metadata": { "evolved_from": null, "kind": "synthetic", "source": "argilla/distilabel-reasoning-prompts" }, "responses": [ { "status": "submitted", "user_id": "6e3edb87-0ccc-47ef-bd61-3ed0e68b20de", "values": { "target": { "value": "\u0625\u0630\u0627 \u0643\u0627\u0646\u062a \u0627\u0644\u0648\u0635\u0641\u0629 \u062a\u062a\u0637\u0644\u0628 \u0643\u0648\u0628\u064a\u0646 \u0648\u0646\u0635\u0641 \u0645\u0646 \u0627\u0644\u0633\u0643\u0631 \u0648\u062a\u0631\u064a\u062f \u062a\u062d\u0636\u064a\u0631 \u0646\u0635\u0641 \u0647\u0630\u0647 \u0627\u0644\u0643\u0645\u064a\u0629\u060c \u0641\u0627\u062d\u0633\u0628 \u0643\u0645\u064a\u0629 \u0627\u0644\u0633\u0643\u0631 \u0627\u0644\u0645\u0637\u0644\u0648\u0628\u0629 \u0628\u0627\u0644\u0636\u0628\u0637." } } } ], "suggestions": [ { "agent": null, "question_name": "target", "score": null, "type": null, "value": "\u0625\u0630\u0627 \u0643\u0627\u0646\u062a \u0627\u0644\u0648\u0635\u0641\u0629 \u062a\u062a\u0637\u0644\u0628 \u0643\u0648\u0628\u064a\u0646 \u0648\u0646\u0635\u0641 \u0645\u0646 \u0627\u0644\u0633\u0643\u0631 \u0648\u062a\u0631\u064a\u062f \u062a\u062d\u0636\u064a\u0631 \u0646\u0635\u0641 \u0627\u0644\u0643\u0645\u064a\u0629\u060c \u0641\u0627\u062d\u0633\u0628 \u0627\u0644\u0643\u0645\u064a\u0629 \u0627\u0644\u062f\u0642\u064a\u0642\u0629 \u0645\u0646 \u0627\u0644\u0633\u0643\u0631 \u0627\u0644\u0645\u0637\u0644\u0648\u0628\u0629." } ], "vectors": {} } ``` While the same record in HuggingFace `datasets` looks as follows: ```json { "external_id": null, "metadata": "{\"source\": \"argilla/distilabel-reasoning-prompts\", \"kind\": \"synthetic\", \"evolved_from\": null}", "source": "If a recipe calls for 2 1/2 cups of sugar and you want to make a half portion of it, calculate the exact amount of sugar needed.", "target": [ { "status": "submitted", "user_id": "6e3edb87-0ccc-47ef-bd61-3ed0e68b20de", "value": "\u0625\u0630\u0627 \u0643\u0627\u0646\u062a \u0627\u0644\u0648\u0635\u0641\u0629 \u062a\u062a\u0637\u0644\u0628 \u0643\u0648\u0628\u064a\u0646 \u0648\u0646\u0635\u0641 \u0645\u0646 \u0627\u0644\u0633\u0643\u0631 \u0648\u062a\u0631\u064a\u062f \u062a\u062d\u0636\u064a\u0631 \u0646\u0635\u0641 \u0647\u0630\u0647 \u0627\u0644\u0643\u0645\u064a\u0629\u060c \u0641\u0627\u062d\u0633\u0628 \u0643\u0645\u064a\u0629 \u0627\u0644\u0633\u0643\u0631 \u0627\u0644\u0645\u0637\u0644\u0648\u0628\u0629 \u0628\u0627\u0644\u0636\u0628\u0637." } ], "target-suggestion": "\u0625\u0630\u0627 \u0643\u0627\u0646\u062a \u0627\u0644\u0648\u0635\u0641\u0629 \u062a\u062a\u0637\u0644\u0628 \u0643\u0648\u0628\u064a\u0646 \u0648\u0646\u0635\u0641 \u0645\u0646 \u0627\u0644\u0633\u0643\u0631 \u0648\u062a\u0631\u064a\u062f \u062a\u062d\u0636\u064a\u0631 \u0646\u0635\u0641 \u0627\u0644\u0643\u0645\u064a\u0629\u060c \u0641\u0627\u062d\u0633\u0628 \u0627\u0644\u0643\u0645\u064a\u0629 \u0627\u0644\u062f\u0642\u064a\u0642\u0629 \u0645\u0646 \u0627\u0644\u0633\u0643\u0631 \u0627\u0644\u0645\u0637\u0644\u0648\u0628\u0629.", "target-suggestion-metadata": { "agent": null, "score": null, "type": null } } ``` ### Data Fields Among the dataset fields, we differentiate between the following: * **Fields:** These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. * **source** is of type `text`. * **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`. * **target** is of type `text`, and description "Translate the text.". * **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable. * (optional) **target-suggestion** is of type `text`. Additionally, we also have two more fields that are optional and are the following: * **metadata:** This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. * **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file. ### Data Splits The dataset contains a single split, which is `train`. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation guidelines This is a translation dataset that contains texts. Please translate the text in the text field. #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

data-is-better-together/MPEP_CZECH

data-is-better-together

--- size_categories: n<1K tags: - rlfh - argilla - human-feedback --- # Dataset Card for MPEP_CZECH This dataset has been created with [Argilla](https://docs.argilla.io). As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets). ## Dataset Description - **Homepage:** https://argilla.io - **Repository:** https://github.com/argilla-io/argilla - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset contains: * A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla. * Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`. * The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla. ### Load with Argilla To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code: ```python import argilla as rg ds = rg.FeedbackDataset.from_huggingface("DIBT/MPEP_CZECH") ``` ### Load with `datasets` To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset("DIBT/MPEP_CZECH") ``` ### Supported Tasks and Leaderboards This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure). There are no leaderboards associated with this dataset. ### Languages [More Information Needed] ## Dataset Structure ### Data in Argilla The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**. The **fields** are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. | Field Name | Title | Type | Required | Markdown | | ---------- | ----- | ---- | -------- | -------- | | source | Source | text | True | True | The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking. | Question Name | Title | Type | Required | Description | Values/Labels | | ------------- | ----- | ---- | -------- | ----------- | ------------- | | target | Target | text | True | Translate the text. | N/A | The **suggestions** are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata". The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. | Metadata Name | Title | Type | Values | Visible for Annotators | | ------------- | ----- | ---- | ------ | ---------------------- | The **guidelines**, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section. ### Data Instances An example of a dataset instance in Argilla looks as follows: ```json { "external_id": "1804", "fields": { "source": "What is the effect of climate change on the polar ice caps and how is it impacting global sea levels?" }, "metadata": { "evolved_from": null, "kind": "human", "source": "OpenAssistant/oasst2" }, "responses": [ { "status": "submitted", "user_id": "818c9e76-9b4f-40c6-8efb-2dcd6d5d5ddb", "values": { "target": { "value": "Jak\u00fd je vliv zm\u011bny klimatu na pol\u00e1rn\u00ed ledov\u00e9 \u010depice a jak ovliv\u0148uje glob\u00e1ln\u00ed hladinu mo\u0159\u00ed?" } } } ], "suggestions": [ { "agent": null, "question_name": "target", "score": null, "type": null, "value": "Jak\u00fd je vliv zm\u011bny klimatu na pol\u00e1rn\u00ed ledov\u00e9 \u010depice a jak ovliv\u0148uje glob\u00e1ln\u00ed hladinu mo\u0159\u00ed?" } ], "vectors": {} } ``` While the same record in HuggingFace `datasets` looks as follows: ```json { "external_id": "1804", "metadata": "{\"evolved_from\": null, \"kind\": \"human\", \"source\": \"OpenAssistant/oasst2\"}", "source": "What is the effect of climate change on the polar ice caps and how is it impacting global sea levels?", "target": [ { "status": "submitted", "user_id": "818c9e76-9b4f-40c6-8efb-2dcd6d5d5ddb", "value": "Jak\u00fd je vliv zm\u011bny klimatu na pol\u00e1rn\u00ed ledov\u00e9 \u010depice a jak ovliv\u0148uje glob\u00e1ln\u00ed hladinu mo\u0159\u00ed?" } ], "target-suggestion": "Jak\u00fd je vliv zm\u011bny klimatu na pol\u00e1rn\u00ed ledov\u00e9 \u010depice a jak ovliv\u0148uje glob\u00e1ln\u00ed hladinu mo\u0159\u00ed?", "target-suggestion-metadata": { "agent": null, "score": null, "type": null } } ``` ### Data Fields Among the dataset fields, we differentiate between the following: * **Fields:** These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. * **source** is of type `text`. * **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`. * **target** is of type `text`, and description "Translate the text.". * **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable. * (optional) **target-suggestion** is of type `text`. Additionally, we also have two more fields that are optional and are the following: * **metadata:** This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. * **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file. ### Data Splits The dataset contains a single split, which is `train`. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation guidelines This is a translation dataset that contains texts. Please translate the text in the text field. #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

data-is-better-together/MPEP_HUNGARIAN

data-is-better-together

--- size_categories: n<1K tags: - rlfh - argilla - human-feedback --- # Dataset Card for MPEP_HUNGARIAN This dataset has been created with [Argilla](https://docs.argilla.io). As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets). ## Dataset Description - **Homepage:** https://argilla.io - **Repository:** https://github.com/argilla-io/argilla - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset contains: * A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla. * Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`. * The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla. ### Load with Argilla To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code: ```python import argilla as rg ds = rg.FeedbackDataset.from_huggingface("DIBT/MPEP_HUNGARIAN") ``` ### Load with `datasets` To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset("DIBT/MPEP_HUNGARIAN") ``` ### Supported Tasks and Leaderboards This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure). There are no leaderboards associated with this dataset. ### Languages [More Information Needed] ## Dataset Structure ### Data in Argilla The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**. The **fields** are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. | Field Name | Title | Type | Required | Markdown | | ---------- | ----- | ---- | -------- | -------- | | source | Source | text | True | True | The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking. | Question Name | Title | Type | Required | Description | Values/Labels | | ------------- | ----- | ---- | -------- | ----------- | ------------- | | target | Target | text | True | Translate the text. | N/A | The **suggestions** are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata". The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. | Metadata Name | Title | Type | Values | Visible for Annotators | | ------------- | ----- | ---- | ------ | ---------------------- | The **guidelines**, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section. ### Data Instances An example of a dataset instance in Argilla looks as follows: ```json { "external_id": "9999", "fields": { "source": "As an AI enthusiast, you love creating programs that can understand human language. Your latest project involves creating a program that can identify and replace words with their antonyms in a given text. \nTo demonstrate the effectiveness of your program, you decide to test it on a news article about a recent political event. However, to make it more challenging, you also want your program to differentiate between homonyms and use context clues to correctly replace them.\nHere\u0027s a step-by-step explanation of how your program works:\n1. The program reads the input text and identifies all the words that have antonyms.\n2. For each of these words, the program identifies the context in which it appears to determine the correct antonym to use.\n3. The program then replaces the original word with its antonym in the text.\n4. If the original word has multiple meanings, the program uses the context to determine which meaning is intended and replace it with the appropriate antonym.\n5. Finally, the program outputs the modified text with the replaced words.\nCan you put your natural language processing skills to the test and try to identify the antonyms used in the modified text?" }, "metadata": { "evolved_from": null, "kind": "synthetic", "source": "evol_instruct" }, "responses": [ { "status": "submitted", "user_id": "d2d0ad53-12ef-4d3f-84c5-df3afb2b2699", "values": { "target": { "value": "Mint mesters\u00e9ges intelligencia rajong\u00f3, szeretsz olyan programokat k\u00e9sz\u00edteni, amelyek k\u00e9pesek meg\u00e9rteni az emberi nyelvet. Leg\u00fajabb projekted egy olyan program fejleszt\u00e9se, amely k\u00e9pes felismerni \u00e9s kicser\u00e9lni a szavakat azok ellent\u00e9teire egy adott sz\u00f6vegben.\nAnnak \u00e9rdek\u00e9ben, hogy bemutasd a program hat\u00e9konys\u00e1g\u00e1t, \u00fagy d\u00f6ntesz, hogy teszteled azt egy \u00fajs\u00e1gcikken, amely egy nemr\u00e9giben t\u00f6rt\u00e9nt politikai esem\u00e9nyr\u0151l sz\u00f3l. Azonban, hogy m\u00e9g nagyobb kih\u00edv\u00e1st jelentsen, azt is szeretn\u00e9d, ha a program megk\u00fcl\u00f6nb\u00f6ztetn\u00e9 a homonim\u00e1kat \u00e9s a kontextus alapj\u00e1n helyesen cser\u00e9ln\u00e9 ki azokat.\n\u00cdme, egy l\u00e9p\u00e9sr\u0151l-l\u00e9p\u00e9sre le\u00edr\u00e1s a program m\u0171k\u00f6d\u00e9s\u00e9r\u0151l:\n1. A program beolvassa a bemeneti sz\u00f6veget \u00e9s azonos\u00edt minden olyan sz\u00f3t, amelynek van ellent\u00e9te.\n2. Minden ilyen sz\u00f3 eset\u00e9n a program azonos\u00edtja a kontextust, amelyben megjelennek, hogy meghat\u00e1rozza a helyes ellent\u00e9tes sz\u00f3t, amit haszn\u00e1lni kell.\n3. A program ezut\u00e1n kicser\u00e9li az eredeti sz\u00f3t annak ellentetj\u00e9re a sz\u00f6vegben.\n4. Ha az eredeti sz\u00f3nak t\u00f6bb jelent\u00e9se is van, a program a kontextust haszn\u00e1lja annak meghat\u00e1roz\u00e1s\u00e1ra, hogy melyik jelent\u00e9s \u00e9rtend\u0151, \u00e9s kicser\u00e9li a megfelel\u0151 ellent\u00e9tes sz\u00f3ra.\n5. V\u00e9g\u00fcl a program visszaadja a m\u00f3dos\u00edtott sz\u00f6veget a kicser\u00e9lt szavakkal.\nPr\u00f3b\u00e1ra tenn\u00e9d a term\u00e9szetes nyelvfeldolgoz\u00e1si k\u00e9pess\u00e9geidet, hogy azonos\u00edtsd a m\u00f3dos\u00edtott sz\u00f6vegben haszn\u00e1lt ellent\u00e9teket?" } } } ], "suggestions": [ { "agent": null, "question_name": "target", "score": null, "type": null, "value": "A programot az emberi nyelv meg\u00e9rt\u00e9s\u00e9re alkalmas programok l\u00e9trehoz\u00e1sa szereti. Az utols\u00f3 projektje olyan program l\u00e9trehoz\u00e1sa, amely k\u00e9pes azonos\u00edtani \u00e9s helyettes\u00edteni a szavakat az ant\u00f3nimusaival egy adott sz\u00f6vegben. A program hat\u00e9konys\u00e1g\u00e1nak bemutat\u00e1sa \u00e9rdek\u00e9ben \u00fagy d\u00f6nt, hogy egy k\u00f6zelm\u00faltbeli politikai esem\u00e9nyr\u0151l sz\u00f3l\u00f3 h\u00edr cikkben tesztelj\u00fck. De hogy kih\u00edv\u00e1st jelentse, azt is szeretn\u00e9, hogy a program hom\u00f3nimokat k\u00fcl\u00f6nb\u00f6ztesse meg \u00e9s \u00f6sszef\u00fcgg\u00e9si nyomokat haszn\u00e1ljon, hogy helyesen helyettes\u00edts\u00e9k \u0151ket. Itt egy l\u00e9p\u00e9sr\u0151l l\u00e9p\u00e9sre magyar\u00e1zata annak, hogyan m\u0171k\u00f6dik a program: 1. A program olvasja a beutazott sz\u00f6veget \u00e9s azonos\u00edtja az \u00f6sszes ant\u00f3nimusaival rendelkez\u0151 sz\u00f3t. 2. Mindegyik sz\u00f3 eset\u00e9ben a program azonos\u00edtja azt a kontextust, amelyben \u00fagy t\u0171nik, hogy meghat\u00e1rozza az eredeti ant\u00f3nim\u00e1t, amelyet haszn\u00e1lni kell. 3. A program a sz\u00f6vegben" } ], "vectors": {} } ``` While the same record in HuggingFace `datasets` looks as follows: ```json { "external_id": "9999", "metadata": "{\"evolved_from\": null, \"kind\": \"synthetic\", \"source\": \"evol_instruct\"}", "source": "As an AI enthusiast, you love creating programs that can understand human language. Your latest project involves creating a program that can identify and replace words with their antonyms in a given text. \nTo demonstrate the effectiveness of your program, you decide to test it on a news article about a recent political event. However, to make it more challenging, you also want your program to differentiate between homonyms and use context clues to correctly replace them.\nHere\u0027s a step-by-step explanation of how your program works:\n1. The program reads the input text and identifies all the words that have antonyms.\n2. For each of these words, the program identifies the context in which it appears to determine the correct antonym to use.\n3. The program then replaces the original word with its antonym in the text.\n4. If the original word has multiple meanings, the program uses the context to determine which meaning is intended and replace it with the appropriate antonym.\n5. Finally, the program outputs the modified text with the replaced words.\nCan you put your natural language processing skills to the test and try to identify the antonyms used in the modified text?", "target": [ { "status": "submitted", "user_id": "d2d0ad53-12ef-4d3f-84c5-df3afb2b2699", "value": "Mint mesters\u00e9ges intelligencia rajong\u00f3, szeretsz olyan programokat k\u00e9sz\u00edteni, amelyek k\u00e9pesek meg\u00e9rteni az emberi nyelvet. Leg\u00fajabb projekted egy olyan program fejleszt\u00e9se, amely k\u00e9pes felismerni \u00e9s kicser\u00e9lni a szavakat azok ellent\u00e9teire egy adott sz\u00f6vegben.\nAnnak \u00e9rdek\u00e9ben, hogy bemutasd a program hat\u00e9konys\u00e1g\u00e1t, \u00fagy d\u00f6ntesz, hogy teszteled azt egy \u00fajs\u00e1gcikken, amely egy nemr\u00e9giben t\u00f6rt\u00e9nt politikai esem\u00e9nyr\u0151l sz\u00f3l. Azonban, hogy m\u00e9g nagyobb kih\u00edv\u00e1st jelentsen, azt is szeretn\u00e9d, ha a program megk\u00fcl\u00f6nb\u00f6ztetn\u00e9 a homonim\u00e1kat \u00e9s a kontextus alapj\u00e1n helyesen cser\u00e9ln\u00e9 ki azokat.\n\u00cdme, egy l\u00e9p\u00e9sr\u0151l-l\u00e9p\u00e9sre le\u00edr\u00e1s a program m\u0171k\u00f6d\u00e9s\u00e9r\u0151l:\n1. A program beolvassa a bemeneti sz\u00f6veget \u00e9s azonos\u00edt minden olyan sz\u00f3t, amelynek van ellent\u00e9te.\n2. Minden ilyen sz\u00f3 eset\u00e9n a program azonos\u00edtja a kontextust, amelyben megjelennek, hogy meghat\u00e1rozza a helyes ellent\u00e9tes sz\u00f3t, amit haszn\u00e1lni kell.\n3. A program ezut\u00e1n kicser\u00e9li az eredeti sz\u00f3t annak ellentetj\u00e9re a sz\u00f6vegben.\n4. Ha az eredeti sz\u00f3nak t\u00f6bb jelent\u00e9se is van, a program a kontextust haszn\u00e1lja annak meghat\u00e1roz\u00e1s\u00e1ra, hogy melyik jelent\u00e9s \u00e9rtend\u0151, \u00e9s kicser\u00e9li a megfelel\u0151 ellent\u00e9tes sz\u00f3ra.\n5. V\u00e9g\u00fcl a program visszaadja a m\u00f3dos\u00edtott sz\u00f6veget a kicser\u00e9lt szavakkal.\nPr\u00f3b\u00e1ra tenn\u00e9d a term\u00e9szetes nyelvfeldolgoz\u00e1si k\u00e9pess\u00e9geidet, hogy azonos\u00edtsd a m\u00f3dos\u00edtott sz\u00f6vegben haszn\u00e1lt ellent\u00e9teket?" } ], "target-suggestion": "A programot az emberi nyelv meg\u00e9rt\u00e9s\u00e9re alkalmas programok l\u00e9trehoz\u00e1sa szereti. Az utols\u00f3 projektje olyan program l\u00e9trehoz\u00e1sa, amely k\u00e9pes azonos\u00edtani \u00e9s helyettes\u00edteni a szavakat az ant\u00f3nimusaival egy adott sz\u00f6vegben. A program hat\u00e9konys\u00e1g\u00e1nak bemutat\u00e1sa \u00e9rdek\u00e9ben \u00fagy d\u00f6nt, hogy egy k\u00f6zelm\u00faltbeli politikai esem\u00e9nyr\u0151l sz\u00f3l\u00f3 h\u00edr cikkben tesztelj\u00fck. De hogy kih\u00edv\u00e1st jelentse, azt is szeretn\u00e9, hogy a program hom\u00f3nimokat k\u00fcl\u00f6nb\u00f6ztesse meg \u00e9s \u00f6sszef\u00fcgg\u00e9si nyomokat haszn\u00e1ljon, hogy helyesen helyettes\u00edts\u00e9k \u0151ket. Itt egy l\u00e9p\u00e9sr\u0151l l\u00e9p\u00e9sre magyar\u00e1zata annak, hogyan m\u0171k\u00f6dik a program: 1. A program olvasja a beutazott sz\u00f6veget \u00e9s azonos\u00edtja az \u00f6sszes ant\u00f3nimusaival rendelkez\u0151 sz\u00f3t. 2. Mindegyik sz\u00f3 eset\u00e9ben a program azonos\u00edtja azt a kontextust, amelyben \u00fagy t\u0171nik, hogy meghat\u00e1rozza az eredeti ant\u00f3nim\u00e1t, amelyet haszn\u00e1lni kell. 3. A program a sz\u00f6vegben", "target-suggestion-metadata": { "agent": null, "score": null, "type": null } } ``` ### Data Fields Among the dataset fields, we differentiate between the following: * **Fields:** These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. * **source** is of type `text`. * **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`. * **target** is of type `text`, and description "Translate the text.". * **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable. * (optional) **target-suggestion** is of type `text`. Additionally, we also have two more fields that are optional and are the following: * **metadata:** This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. * **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file. ### Data Splits The dataset contains a single split, which is `train`. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation guidelines This is a translation dataset that contains texts. Please translate the text in the text field. #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

data-is-better-together/aya_dataset_dutch_example

data-is-better-together

--- dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: language dtype: string - name: language_code dtype: string - name: annotation_type dtype: string - name: user_id dtype: string splits: - name: train num_bytes: 2054479.0656496773 num_examples: 1633 - name: test num_bytes: 125810.10812306659 num_examples: 100 download_size: 672315 dataset_size: 2180289.173772744 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* license: apache-2.0 task_categories: - text-generation language: - nl tags: - sft size_categories: - 1K<n<10K ---

Tristepin/quakec-raw1

Tristepin

--- license: mit ---

data-is-better-together/aya_dutch_dpo_raw

data-is-better-together

--- size_categories: n<1K dataset_info: features: - name: instruction dtype: string - name: targets dtype: string - name: language dtype: string - name: language_code dtype: string - name: annotation_type dtype: string - name: user_id dtype: string - name: generation_model dtype: string - name: generation dtype: string - name: predicted_generation_language dtype: string - name: predicted_generation_language_score dtype: float64 - name: generations sequence: string - name: generation_models sequence: string - name: model_name dtype: string - name: ratings sequence: int64 - name: rationales sequence: string splits: - name: train num_bytes: 3530439 num_examples: 1200 download_size: 1847668 dataset_size: 3530439 configs: - config_name: default data_files: - split: train path: data/train-* tags: - synthetic - distilabel - rlaif --- <p align="left"> <a href="https://github.com/argilla-io/distilabel"> <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> </a> </p> # Dataset Card for aya_dutch_dpo_raw This dataset has been created with [distilabel](https://distilabel.argilla.io/). ## Dataset Summary This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI: ```console distilabel pipeline run --config "https://huggingface.co/datasets/DIBT/aya_dutch_dpo_raw/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/DIBT/aya_dutch_dpo_raw/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "annotation_type": "re-annotations", "generation": "De constructie van het fort, dat werd opgetrokken in de haven van Lahaina met behulp van massive koraalblokken die uit de omringende riffen waren verwijderd, markeerde een keerpunt in de politieke en economische ontwikkeling van het eiland Maui in de eerste helft van de negentiende eeuw.", "generation_model": "meta-llama/Meta-Llama-3-70B-Instruct", "generation_models": [ "aya", "meta-llama/Meta-Llama-3-70B-Instruct" ], "generations": [ "Een meer complexe versie van de zin is natuurlijk: \"Het fort werd gebouwd in het kadegebied bij Lahaina Harbor van koraalblokken met muren bekroond met 47 kanonnen die van schepen zijn verkregen\".", "De constructie van het fort, dat werd opgetrokken in de haven van Lahaina met behulp van massive koraalblokken die uit de omringende riffen waren verwijderd, markeerde een keerpunt in de politieke en economische ontwikkeling van het eiland Maui in de eerste helft van de negentiende eeuw." ], "instruction": "Maak een complexere zin: Het fort werd gebouwd in de haven van Lahaina met koraalblokken.", "language": "Dutch", "language_code": "nld", "model_name": "meta-llama/Meta-Llama-3-70B-Instruct", "predicted_generation_language": "nld_Latn", "predicted_generation_language_score": 0.9995737671852112, "ratings": [ 4, 5 ], "rationales": [ "The text accurately provides a more complex sentence as requested, and the added details are correct (e.g., \"muren bekroond met 47 kanonnen\"). The sentence is well-structured and easy to follow. However, the model could be more confident in its language, and some parts feel slightly redundant (e.g., \"kadegebied bij Lahaina Harbor\").", "The text provides a sophisticated and accurate sentence that not only meets the request but also adds valuable context about the significance of the fort\u0027s construction. The language is confident and precise, and the sentence is well-structured and engaging. The model demonstrates a thorough understanding of the topic and effectively conveys its knowledge without any noticeable errors or hallucinations." ], "targets": "Een meer complexe versie van de zin is natuurlijk: \"Het fort werd gebouwd in het kadegebied bij Lahaina Harbor van koraalblokken met muren bekroond met 47 kanonnen die van schepen zijn verkregen\".", "user_id": "ca908e583236b208e473e89dae5c7b7d3daf3662e2bbf6606f0702c718bb5c06" } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("DIBT/aya_dutch_dpo_raw", "default") ``` Or simply as it follows, since there's only one configuration and is named `default`: ```python from datasets import load_dataset ds = load_dataset("DIBT/aya_dutch_dpo_raw") ``` </details>

data-is-better-together/aya_dataset_english_example

data-is-better-together

--- dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: language dtype: string - name: language_code dtype: string - name: annotation_type dtype: string - name: user_id dtype: string splits: - name: train num_bytes: 4836140.556250679 num_examples: 3844 - name: test num_bytes: 125810.10812306659 num_examples: 100 download_size: 1606520 dataset_size: 4961950.664373746 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* license: apache-2.0 task_categories: - text-generation language: - en tags: - sft size_categories: - 1K<n<10K ---

data-is-better-together/aya_english_dpo_raw

data-is-better-together

--- size_categories: n<1K dataset_info: features: - name: instruction dtype: string - name: targets dtype: string - name: language dtype: string - name: language_code dtype: string - name: annotation_type dtype: string - name: user_id dtype: string - name: generation_model dtype: string - name: generation dtype: string - name: predicted_generation_language dtype: string - name: predicted_generation_language_score dtype: float64 - name: generations sequence: string - name: generation_models sequence: string - name: model_name dtype: string - name: ratings sequence: int64 - name: rationales sequence: string splits: - name: train num_bytes: 567777 num_examples: 100 download_size: 320089 dataset_size: 567777 configs: - config_name: default data_files: - split: train path: data/train-* tags: - synthetic - distilabel - rlaif --- <p align="left"> <a href="https://github.com/argilla-io/distilabel"> <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> </a> </p> # Dataset Card for aya_english_dpo_raw This dataset has been created with [distilabel](https://distilabel.argilla.io/). ## Dataset Summary This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI: ```console distilabel pipeline run --config "https://huggingface.co/datasets/DIBT/aya_english_dpo_raw/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/DIBT/aya_english_dpo_raw/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "annotation_type": "original-annotations", "generation": "The largest organ in the human body is the skin. It covers the entire surface of the body and has a total area of about 22 square feet (2 square meters) in adults. The skin performs many important functions, such as:\n\n* Protecting the body from external damage, such as injury, infection, and UV radiation\n* Regulating body temperature\n* Aiding in the production of vitamin D\n* Assisting in the sensation of touch and feel\n* Helping to maintain fluid balance and hydration\n\nThe skin is composed of several layers, with the outermost layer being the epidermis, followed by the dermis, and then the hypodermis. It is estimated that the skin accounts for about 15% of an adult\u0027s total body weight.\n\nIt\u0027s worth noting that while the skin is the largest organ by surface area, the liver is often considered the largest organ by mass, weighing around 3 pounds (1.4 kilograms) in adults.", "generation_model": "meta-llama/Meta-Llama-3-70B-Instruct", "generation_models": [ "aya", "meta-llama/Meta-Llama-3-70B-Instruct" ], "generations": [ "The largest organ in the human body is the skin. It is the body\u0027s largest organ by surface area and weight. The skin serves many essential functions, including protecting the body from physical damage, pathogens, and dehydration, as well as helping to regulate body temperature and producing vitamin D in response to sunlight. It\u0027s a complex organ with multiple layers, cells, and structures, each serving a unique purpose in maintaining the body\u0027s overall health and well-being.", "The largest organ in the human body is the skin. It covers the entire surface of the body and has a total area of about 22 square feet (2 square meters) in adults. The skin performs many important functions, such as:\n\n* Protecting the body from external damage, such as injury, infection, and UV radiation\n* Regulating body temperature\n* Aiding in the production of vitamin D\n* Assisting in the sensation of touch and feel\n* Helping to maintain fluid balance and hydration\n\nThe skin is composed of several layers, with the outermost layer being the epidermis, followed by the dermis, and then the hypodermis. It is estimated that the skin accounts for about 15% of an adult\u0027s total body weight.\n\nIt\u0027s worth noting that while the skin is the largest organ by surface area, the liver is often considered the largest organ by mass, weighing around 3 pounds (1.4 kilograms) in adults." ], "instruction": "What is the largest organ in the human body?", "language": "English", "language_code": "eng", "model_name": "meta-llama/Meta-Llama-3-70B-Instruct", "predicted_generation_language": "eng_Latn", "predicted_generation_language_score": 0.9590950012207031, "ratings": [ 5, 5 ], "rationales": [ "The text is accurate, informative, and comprehensive in describing the skin as the largest organ in the human body. It provides specific details about its functions, structure, and importance, aligning perfectly with the instruction.", "This text is equally excellent, providing a clear and concise answer to the question. It lists the skin\u0027s functions, describes its composition, and offers additional interesting facts, such as the comparison with the liver\u0027s mass. The text is well-structured, accurate, and confident in its information, making it an excellent response." ], "targets": "The largest organ in the human body is the skin. It is the body\u0027s largest organ by surface area and weight. The skin serves many essential functions, including protecting the body from physical damage, pathogens, and dehydration, as well as helping to regulate body temperature and producing vitamin D in response to sunlight. It\u0027s a complex organ with multiple layers, cells, and structures, each serving a unique purpose in maintaining the body\u0027s overall health and well-being.", "user_id": "29f22cf193a81e1a5c47d76af453a91b3cd19aa348995c7add1df15fe24e8801" } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("DIBT/aya_english_dpo_raw", "default") ``` Or simply as it follows, since there's only one configuration and is named `default`: ```python from datasets import load_dataset ds = load_dataset("DIBT/aya_english_dpo_raw") ``` </details>