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98
your hugging face profile now has your recent activities ๐ค
Nicolay Rusnachenko
nicolay-r
AI & ML interests
Information RetrievalใปMedical Multimodal NLP (๐ผ+๐) Research Fellow @BU_Researchใปsoftware developer http://arekit.ioใปPhD in NLP
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posted
an
update
6 days ago
Post
426
๐ข For those who are interested in extracting information about โ๏ธ authors from texts, happy to share personal ๐น on Reading Between the lines: adapting ChatGPT-related systems ๐ค for Implicit Information Retrieval National
Youtube: https://youtu.be/nXClX7EDYbE
๐ In this talk, we refer to IIR as such information that is indirectly expressed by โ๏ธ author / ๐จ character / patient / any other entity.
๐ I cover the 1๏ธโฃ pre-processing and 2๏ธโฃ reasoning techniques, aimed at enhancing gen AI capabilities in IIR. To showcase the effectiveness of the proposed techniques, we experiment with such IIR tasks as Sentiment Analysis, Emotion Extraction / Causes Prediction.
In pictures below, sharing the quick takeaways on the pipeline construction and experiment results ๐งช
Related paper cards:
๐ emotion-extraction: https://nicolay-r.github.io/#semeval2024-nicolay
๐ sentiment-analysis: https://nicolay-r.github.io/#ljom2024
Models:
nicolay-r/flan-t5-tsa-thor-base
nicolay-r/flan-t5-emotion-cause-thor-base
๐ PS: I got a hoppy for advetising HPMoR โจ ๐
Youtube: https://youtu.be/nXClX7EDYbE
๐ In this talk, we refer to IIR as such information that is indirectly expressed by โ๏ธ author / ๐จ character / patient / any other entity.
๐ I cover the 1๏ธโฃ pre-processing and 2๏ธโฃ reasoning techniques, aimed at enhancing gen AI capabilities in IIR. To showcase the effectiveness of the proposed techniques, we experiment with such IIR tasks as Sentiment Analysis, Emotion Extraction / Causes Prediction.
In pictures below, sharing the quick takeaways on the pipeline construction and experiment results ๐งช
Related paper cards:
๐ emotion-extraction: https://nicolay-r.github.io/#semeval2024-nicolay
๐ sentiment-analysis: https://nicolay-r.github.io/#ljom2024
Models:
nicolay-r/flan-t5-tsa-thor-base
nicolay-r/flan-t5-emotion-cause-thor-base
๐ PS: I got a hoppy for advetising HPMoR โจ ๐
posted
an
update
12 days ago
Post
703
๐ข Have you ever been wondered how specifically Transformers were capable for handling long input contexts?
I got a chance to tackle this through long document texts summarization problem, and delighted to share the related survey and diagram for a quick skimming below:
Preprint ๐ https://nicolay-r.github.io/website/data/preprint-AINL_2023_longt5_summarization.pdf
Springer ๐ https://link.springer.com/article/10.1007/s10958-024-07435-z
๐ฏ The aim of the survey was the development of the long-document summarizer for mass-media news in Vietnamese language. ๐ป๐ณ
Sharing for a quick skimming of the methods performance overview of various LM-based solution across several datasets, covering domain-oriented advances in Vietnamese language (see attached screenshots)
As for solution we consider:
โ๏ธ 1. Adapt existed google/pegasus-cnn_dailymail for summarizing large dataset for arranging training
โ๏ธ 2. Tuning google/long-t5-tglobal-large suitable for performing generative summarization.
Implementation details:
๐ https://github.com/nicolay-r/ViLongT5
(Simplier to go with huggingface rather flaxformer that so far become a legacy engine)
I got a chance to tackle this through long document texts summarization problem, and delighted to share the related survey and diagram for a quick skimming below:
Preprint ๐ https://nicolay-r.github.io/website/data/preprint-AINL_2023_longt5_summarization.pdf
Springer ๐ https://link.springer.com/article/10.1007/s10958-024-07435-z
๐ฏ The aim of the survey was the development of the long-document summarizer for mass-media news in Vietnamese language. ๐ป๐ณ
Sharing for a quick skimming of the methods performance overview of various LM-based solution across several datasets, covering domain-oriented advances in Vietnamese language (see attached screenshots)
As for solution we consider:
โ๏ธ 1. Adapt existed google/pegasus-cnn_dailymail for summarizing large dataset for arranging training
โ๏ธ 2. Tuning google/long-t5-tglobal-large suitable for performing generative summarization.
Implementation details:
๐ https://github.com/nicolay-r/ViLongT5
(Simplier to go with huggingface rather flaxformer that so far become a legacy engine)
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19 days ago
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2336
> Oasis: First Real-Time Video Game Without a Game Engine! ๐ฎ
DecartAI & Etched just released Oasis - a fully AI-generated video game running at 20 FPS (frames per second). The model takes keyboard inputs and generates everything - physics, rules, graphics - on the fly, without any game engine.
โก๏ธ What makes this special? Current text-to-video models (Mochi-1, Sora, Kling) generate about 1 frame every 10-20 seconds (that's the kind of device I had to play LoL back in the day, thus my low rankings). Oasis is 200 times faster, making it the first playable AI-generated game.
โ๏ธ Under the hood, it uses a vision transformer to encode space and a diffusion model to generate frames. The secret sauce is "dynamic noising" - a technique that keeps the video stable between frames.
Key insights:
โก๏ธ Generates 20 FPS, vs 0.2 FPS for other DIT-based video models
โฃ The specialized hardware Sohu developed by Etched allows to handle 10x more player than H100
๐ฎ Features real game mechanics
โฃ Movement, jumping, item management
โฃ Physics and lighting
โฃ Procedurally generated worlds
โ ๏ธ Current limitations
โฃ Blurry graphics at a distance
โฃ Objects sometimes change appearance
โฃ Memory issues in long sessions
Try it yourself, the playable demo is impressive! ๐ https://oasis.decart.ai/welcome
Code ๐ https://github.com/etched-ai/open-oasis
Read it in full ๐ https://oasis-model.github.io/
DecartAI & Etched just released Oasis - a fully AI-generated video game running at 20 FPS (frames per second). The model takes keyboard inputs and generates everything - physics, rules, graphics - on the fly, without any game engine.
โก๏ธ What makes this special? Current text-to-video models (Mochi-1, Sora, Kling) generate about 1 frame every 10-20 seconds (that's the kind of device I had to play LoL back in the day, thus my low rankings). Oasis is 200 times faster, making it the first playable AI-generated game.
โ๏ธ Under the hood, it uses a vision transformer to encode space and a diffusion model to generate frames. The secret sauce is "dynamic noising" - a technique that keeps the video stable between frames.
Key insights:
โก๏ธ Generates 20 FPS, vs 0.2 FPS for other DIT-based video models
โฃ The specialized hardware Sohu developed by Etched allows to handle 10x more player than H100
๐ฎ Features real game mechanics
โฃ Movement, jumping, item management
โฃ Physics and lighting
โฃ Procedurally generated worlds
โ ๏ธ Current limitations
โฃ Blurry graphics at a distance
โฃ Objects sometimes change appearance
โฃ Memory issues in long sessions
Try it yourself, the playable demo is impressive! ๐ https://oasis.decart.ai/welcome
Code ๐ https://github.com/etched-ai/open-oasis
Read it in full ๐ https://oasis-model.github.io/
posted
an
update
19 days ago
Post
1830
๐ข If you're aimed at processing complex dependencies in spreadsheet data with LLM Chain-of-Thought technique, then this update might be valuable for you ๐
The updated ๐ฆ bulk-chain-0.24.1 which is aimed at iterative processing of CSV/JSONL data with no-string dependencies from third party LLM frameworks is out ๐
๐ฆ: https://pypi.org/project/bulk-chain/0.24.1/
๐: https://github.com/nicolay-r/bulk-chain
๐: https://github.com/nicolay-r/bulk-chain/issues/26
The key feature of bulk-chain is SQLite caching that saves your time โฐ๏ธ and money ๐ต by guarantee no-data-lost, which is important once using the remote LLM providers such as OpenAI, ReplicateIO, OpenRouter, etc.
๐ง This release has the following updates:
โ Improved stability for various header conditions and the related support from SQLite
โ Manual setup for ID column / assigning the ID
โ Make CSV-related setups dynamic, that refers to the related Python ๐ฆ csv package.
Quick start on GoogleColab:
๐: https://colab.research.google.com/github/nicolay-r/bulk-chain/blob/master/bulk_chain_tutorial.ipynb
Below is an example of the three simple steps in pictures:
1. โฌ๏ธ Package installation
2. โ๏ธ Declaring schema
3. ๐ Launching inference for your data with Replicate and ๐ค meta-llama/Llama-3.1-405B
The updated ๐ฆ bulk-chain-0.24.1 which is aimed at iterative processing of CSV/JSONL data with no-string dependencies from third party LLM frameworks is out ๐
๐ฆ: https://pypi.org/project/bulk-chain/0.24.1/
๐: https://github.com/nicolay-r/bulk-chain
๐: https://github.com/nicolay-r/bulk-chain/issues/26
The key feature of bulk-chain is SQLite caching that saves your time โฐ๏ธ and money ๐ต by guarantee no-data-lost, which is important once using the remote LLM providers such as OpenAI, ReplicateIO, OpenRouter, etc.
๐ง This release has the following updates:
โ Improved stability for various header conditions and the related support from SQLite
โ Manual setup for ID column / assigning the ID
โ Make CSV-related setups dynamic, that refers to the related Python ๐ฆ csv package.
Quick start on GoogleColab:
๐: https://colab.research.google.com/github/nicolay-r/bulk-chain/blob/master/bulk_chain_tutorial.ipynb
Below is an example of the three simple steps in pictures:
1. โฌ๏ธ Package installation
2. โ๏ธ Declaring schema
3. ๐ Launching inference for your data with Replicate and ๐ค meta-llama/Llama-3.1-405B
posted
an
update
about 1 month ago
Post
681
๐ข Excited to share that our studies ๐ "Large Language Models in Targeted Sentiment Analysis for Russian" has recently become in ๐ Springer Lobachevskii Journal of Mathematics ๐ฅณโจ ...
๐ https://link.springer.com/article/10.1134/S1995080224603758
In this studies we provide such a diverse look and experiments over various ๐ค LLM models ๐ค scaled from 7B in two different modes: โ๏ธ zero-shot and ๐ฅ fine-tuned (Flan-T5 only) using Three-Hop reasoning technique.
We showcase the importance of performing:
๐ text translation into English
๐ application on Chain-of-Thought for Implicit Sentiment Analysis
More:
๐ Arxiv: https://arxiv.org/abs/2404.12342
๐งโ๐ป๏ธ Code: https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework
๐ค Models: Large Language Models in Targeted Sentiment Analysis (2404.12342)
๐ฅ Video @NLPSummit : https://www.youtube.com/watch?v=qawLJsRHzB4
THOR: https://github.com/scofield7419/THOR-ISA
๐ https://link.springer.com/article/10.1134/S1995080224603758
In this studies we provide such a diverse look and experiments over various ๐ค LLM models ๐ค scaled from 7B in two different modes: โ๏ธ zero-shot and ๐ฅ fine-tuned (Flan-T5 only) using Three-Hop reasoning technique.
We showcase the importance of performing:
๐ text translation into English
๐ application on Chain-of-Thought for Implicit Sentiment Analysis
More:
๐ Arxiv: https://arxiv.org/abs/2404.12342
๐งโ๐ป๏ธ Code: https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework
๐ค Models: Large Language Models in Targeted Sentiment Analysis (2404.12342)
๐ฅ Video @NLPSummit : https://www.youtube.com/watch?v=qawLJsRHzB4
THOR: https://github.com/scofield7419/THOR-ISA
posted
an
update
about 1 month ago
Post
640
๐ข We are giving extra two weeks before switching to the final stage of RuOpinionNE-2024.
โฐ The final stage starts since 1-st of November 2024.
We have already first baseline submission by ๐จโ๐ป RefalMachine that showcase F1 = 0.17 based on Qwen2 model series.
For those who wish to attend:
๐ Codalab: https://codalab.lisn.upsaclay.fr/competitions/20244
๐ Task: https://codalab.lisn.upsaclay.fr/competitions/20244#learn_the_details-overview
๐ Updates: https://t.me/RuOpinionNE2024
๐ Questions: https://nicolay-r.github.io/
๐งช Past experiments: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
โฐ The final stage starts since 1-st of November 2024.
We have already first baseline submission by ๐จโ๐ป RefalMachine that showcase F1 = 0.17 based on Qwen2 model series.
For those who wish to attend:
๐ Codalab: https://codalab.lisn.upsaclay.fr/competitions/20244
๐ Task: https://codalab.lisn.upsaclay.fr/competitions/20244#learn_the_details-overview
๐ Updates: https://t.me/RuOpinionNE2024
๐ Questions: https://nicolay-r.github.io/
๐งช Past experiments: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
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3037
The Nobel Prize background for Hopfield and Hinton's work on neural networks is pure gold. It's a masterclass in explaining AI basics.
Key takeaways from the conclusion:
- ML applications are expanding rapidly. We're still figuring out which will stick.
- Ethical discussions are crucial as the tech develops.
- Physics ๐ค AI: A two-way street of innovation.
Some mind-blowing AI applications in physics:
- Discovering the Higgs particle
- Cleaning up gravitational wave data
- Hunting exoplanets
- Predicting molecular structures
- Designing better solar cells
We're just scratching the surface. The interplay between AI and physics is reshaping both fields.
Bonus: The illustrations accompanying the background document are really neat. (Credit: Johan Jarnestad/The Royal Swedish Academy of Sciences)
#AI #MachineLearning #Physics #Ethics #Innovation
Key takeaways from the conclusion:
- ML applications are expanding rapidly. We're still figuring out which will stick.
- Ethical discussions are crucial as the tech develops.
- Physics ๐ค AI: A two-way street of innovation.
Some mind-blowing AI applications in physics:
- Discovering the Higgs particle
- Cleaning up gravitational wave data
- Hunting exoplanets
- Predicting molecular structures
- Designing better solar cells
We're just scratching the surface. The interplay between AI and physics is reshaping both fields.
Bonus: The illustrations accompanying the background document are really neat. (Credit: Johan Jarnestad/The Royal Swedish Academy of Sciences)
#AI #MachineLearning #Physics #Ethics #Innovation
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reacted to
Jaward's
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about 1 month ago
Post
1133
Lightweight implementation of newly introduced โDifferential Transformerโ:
Proposes differential attention mechanism which computes attention scores as a difference between two separate softmax attention maps thereby reducing noise in attention blocks. [[[Differential nanoGPT]]] :)
Code: https://github.com/Jaykef/ai-algorithms/blob/main/DIFF_Transformer.ipynb
YT Video: https://youtu.be/9V4mJA5y7dg
Proposes differential attention mechanism which computes attention scores as a difference between two separate softmax attention maps thereby reducing noise in attention blocks. [[[Differential nanoGPT]]] :)
Code: https://github.com/Jaykef/ai-algorithms/blob/main/DIFF_Transformer.ipynb
YT Video: https://youtu.be/9V4mJA5y7dg
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fantaxy's
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about 1 month ago
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3463
NSFW Erotic Novel AI Generation
-NSFW Text (Data) Generator for Detecting 'NSFW' Text: Multilingual Experience
The multilingual NSFW text (data) auto-generator is a tool designed to automatically generate and analyze adult content in various languages. This service uses AI-based text generation to produce various types of NSFW content, which can then be used as training data to build effective filtering models. It supports multiple languages, including English, and allows users to input the desired language through the system prompt in the on-screen options to generate content in the specified language. Users can create datasets from the generated data, train machine learning models, and improve the accuracy of text analysis systems. Furthermore, content generation can be customized according to user specifications, allowing for the creation of tailored data. This maximizes the performance of NSFW text detection models.
Web: https://fantaxy-erotica.hf.space
API: https://replicate.com/aitechtree/nsfw-novel-generation
Usage Warnings and Notices: This tool is intended for research and development purposes only, and the generated NSFW content must adhere to appropriate legal and ethical guidelines. Proper monitoring is required to prevent the misuse of inappropriate content, and legal responsibility lies with the user. Users must comply with local laws and regulations when using the data, and the service provider is not liable for any issues arising from the misuse of the data.
-NSFW Text (Data) Generator for Detecting 'NSFW' Text: Multilingual Experience
The multilingual NSFW text (data) auto-generator is a tool designed to automatically generate and analyze adult content in various languages. This service uses AI-based text generation to produce various types of NSFW content, which can then be used as training data to build effective filtering models. It supports multiple languages, including English, and allows users to input the desired language through the system prompt in the on-screen options to generate content in the specified language. Users can create datasets from the generated data, train machine learning models, and improve the accuracy of text analysis systems. Furthermore, content generation can be customized according to user specifications, allowing for the creation of tailored data. This maximizes the performance of NSFW text detection models.
Web: https://fantaxy-erotica.hf.space
API: https://replicate.com/aitechtree/nsfw-novel-generation
Usage Warnings and Notices: This tool is intended for research and development purposes only, and the generated NSFW content must adhere to appropriate legal and ethical guidelines. Proper monitoring is required to prevent the misuse of inappropriate content, and legal responsibility lies with the user. Users must comply with local laws and regulations when using the data, and the service provider is not liable for any issues arising from the misuse of the data.
- 2 replies
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reach-vb's
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about 1 month ago
Post
2050
On-device AI framework ecosystem is blooming these days:
1. llama.cpp - All things Whisper, LLMs & VLMs - run across Metal, CUDA and other backends (AMD/ NPU etc)
https://github.com/ggerganov/llama.cpp
2. MLC - Deploy LLMs across platforms especially WebGPU (fastest WebGPU LLM implementation out there)
https://github.com/mlc-ai/web-llm
3. MLX - Arguably the fastest general purpose framework (Mac only) - Supports all major Image Generation (Flux, SDXL, etc), Transcription (Whisper), LLMs
https://github.com/ml-explore/mlx-examples
4. Candle - Cross-platform general purpose framework written in Rust - wide coverage across model categories
https://github.com/huggingface/candle
Honorable mentions:
1. Transformers.js - Javascript (WebGPU) implementation built on top of ONNXruntimeweb
https://github.com/xenova/transformers.js
2. Mistral rs - Rust implementation for LLMs & VLMs, built on top of Candle
https://github.com/EricLBuehler/mistral.rs
3. Ratchet - Cross platform, rust based WebGPU framework built for battle-tested deployments
https://github.com/huggingface/ratchet
4. Zml - Cross platform, Zig based ML framework
https://github.com/zml/zml
Looking forward to how the ecosystem would look 1 year from now - Quite bullish on the top 4 atm - but open source ecosystem changes quite a bit! ๐ค
Also, which frameworks did I miss?
1. llama.cpp - All things Whisper, LLMs & VLMs - run across Metal, CUDA and other backends (AMD/ NPU etc)
https://github.com/ggerganov/llama.cpp
2. MLC - Deploy LLMs across platforms especially WebGPU (fastest WebGPU LLM implementation out there)
https://github.com/mlc-ai/web-llm
3. MLX - Arguably the fastest general purpose framework (Mac only) - Supports all major Image Generation (Flux, SDXL, etc), Transcription (Whisper), LLMs
https://github.com/ml-explore/mlx-examples
4. Candle - Cross-platform general purpose framework written in Rust - wide coverage across model categories
https://github.com/huggingface/candle
Honorable mentions:
1. Transformers.js - Javascript (WebGPU) implementation built on top of ONNXruntimeweb
https://github.com/xenova/transformers.js
2. Mistral rs - Rust implementation for LLMs & VLMs, built on top of Candle
https://github.com/EricLBuehler/mistral.rs
3. Ratchet - Cross platform, rust based WebGPU framework built for battle-tested deployments
https://github.com/huggingface/ratchet
4. Zml - Cross platform, Zig based ML framework
https://github.com/zml/zml
Looking forward to how the ecosystem would look 1 year from now - Quite bullish on the top 4 atm - but open source ecosystem changes quite a bit! ๐ค
Also, which frameworks did I miss?
- 1 reply
posted
an
update
about 1 month ago
Post
1004
๐ข Two weeks ago I got a chance to share the most recent reasoning ๐ง capabilities of Large Language models in Sentiment Analysis NLPSummit-2024.
For those who missed and still wish to find out the advances of GenAI in that field, the recording is now available:
https://www.youtube.com/watch?v=qawLJsRHzB4
You will be aware of:
โ๏ธ how well LLMs reasoning can be used for reasoning in sentiment analysis as in Zero-shot-Learning,
โ๏ธ how to improve reasoning by applying and leaving step-by-step chains (Chain-of-Thought)
โ๏ธ how to prepare the most advanced model in sentiment analysis using Chain-of-Thought.
Links:
๐ Paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)
โญ Code: https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework
For those who missed and still wish to find out the advances of GenAI in that field, the recording is now available:
https://www.youtube.com/watch?v=qawLJsRHzB4
You will be aware of:
โ๏ธ how well LLMs reasoning can be used for reasoning in sentiment analysis as in Zero-shot-Learning,
โ๏ธ how to improve reasoning by applying and leaving step-by-step chains (Chain-of-Thought)
โ๏ธ how to prepare the most advanced model in sentiment analysis using Chain-of-Thought.
Links:
๐ Paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)
โญ Code: https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework
reacted to
qnguyen3's
post with ๐ฅ
about 1 month ago
Post
3740
nanoLLaVA-1.5 is here! Same size (1B), better performance ๐ฅ๐ฅ๐ฅ
And it is much more powerful than v1.0
Try it out now on HF Spaces: qnguyen3/nanoLLaVA
Model: qnguyen3/nanoLLaVA-1.5
And it is much more powerful than v1.0
Try it out now on HF Spaces: qnguyen3/nanoLLaVA
Model: qnguyen3/nanoLLaVA-1.5
- 3 replies
reacted to
mmhamdy's
post with ๐
about 1 month ago
Post
1829
๐ Evaluating Long Context #1: Long Range Arena (LRA)
Accurately evaluating how well language models handle long contexts is crucial, but it's also quite challenging to do well. In this series of posts, we're going to examine the various benchmarks that were proposed to assess long context understanding, starting with Long Range Arens (LRA)
Introduced in 2020, Long Range Arens (LRA) is one of the earliest benchmarks designed to tackle the challenge of long context evaluation.
๐ Key Features of LRA
1๏ธโฃ Diverse Tasks: The LRA benchmark consists of a suite of tasks designed to evaluate model performance on long sequences ranging from 1,000 to 16,000 tokens. These tasks encompass different data types and modalities: Text, Natural and Synthetic Images, and Mathematical Expressions.
2๏ธโฃ Synthetic and Real-world Tasks: LRA is comprised of both synthetic probing tasks and real-world tasks.
3๏ธโฃ Open-Source and Extensible: Implemented in Python using Jax and Flax, the LRA benchmark code is publicly available, making it easy to extend.
๐ Tasks
1๏ธโฃ Long ListOps
2๏ธโฃ Byte-level Text Classification and Document Retrieval
3๏ธโฃ Image Classification
4๏ธโฃ Pathfinder and Pathfinder-X (Long-range spatial dependency)
๐จโ๐ป Long Range Arena (LRA) Github Repository: https://github.com/google-research/long-range-arena
๐ Long Range Arena (LRA) paper: Long Range Arena: A Benchmark for Efficient Transformers (2011.04006)
Accurately evaluating how well language models handle long contexts is crucial, but it's also quite challenging to do well. In this series of posts, we're going to examine the various benchmarks that were proposed to assess long context understanding, starting with Long Range Arens (LRA)
Introduced in 2020, Long Range Arens (LRA) is one of the earliest benchmarks designed to tackle the challenge of long context evaluation.
๐ Key Features of LRA
1๏ธโฃ Diverse Tasks: The LRA benchmark consists of a suite of tasks designed to evaluate model performance on long sequences ranging from 1,000 to 16,000 tokens. These tasks encompass different data types and modalities: Text, Natural and Synthetic Images, and Mathematical Expressions.
2๏ธโฃ Synthetic and Real-world Tasks: LRA is comprised of both synthetic probing tasks and real-world tasks.
3๏ธโฃ Open-Source and Extensible: Implemented in Python using Jax and Flax, the LRA benchmark code is publicly available, making it easy to extend.
๐ Tasks
1๏ธโฃ Long ListOps
2๏ธโฃ Byte-level Text Classification and Document Retrieval
3๏ธโฃ Image Classification
4๏ธโฃ Pathfinder and Pathfinder-X (Long-range spatial dependency)
๐จโ๐ป Long Range Arena (LRA) Github Repository: https://github.com/google-research/long-range-arena
๐ Long Range Arena (LRA) paper: Long Range Arena: A Benchmark for Efficient Transformers (2011.04006)
reacted to
merve's
post with ๐ฅ
about 1 month ago
Post
3740
Meta AI vision has been cooking
@facebook
They shipped multiple models and demos for their papers at @ECCV ๐ค
Here's a compilation of my top picks:
- Sapiens is family of foundation models for human-centric depth estimation, segmentation and more, all models have open weights and demos ๐
All models have their demos and even torchscript checkpoints!
A collection of models and demos: facebook/sapiens-66d22047daa6402d565cb2fc
- VFusion3D is state-of-the-art consistent 3D generation model from images
Model: facebook/vfusion3d
Demo: facebook/VFusion3D
- CoTracker is the state-of-the-art point (pixel) tracking model
Demo: facebook/cotracker
Model: facebook/cotracker
They shipped multiple models and demos for their papers at @ECCV ๐ค
Here's a compilation of my top picks:
- Sapiens is family of foundation models for human-centric depth estimation, segmentation and more, all models have open weights and demos ๐
All models have their demos and even torchscript checkpoints!
A collection of models and demos: facebook/sapiens-66d22047daa6402d565cb2fc
- VFusion3D is state-of-the-art consistent 3D generation model from images
Model: facebook/vfusion3d
Demo: facebook/VFusion3D
- CoTracker is the state-of-the-art point (pixel) tracking model
Demo: facebook/cotracker
Model: facebook/cotracker
posted
an
update
about 2 months ago
Post
643
๐ข This year I made decent amout of experiments on LLM reasoning capabilities in author opinion extraction.
However, they did not go further with:
โ๏ธ annoation of other sources of opinion causes: entities, out-of-context object (None).
๐ evaluation of factual statements that support the extracted sentiment.
To address these limitations, so far we launch ๐ RuOpinionNE-2024 competition on the Codalab platform:
๐ https://codalab.lisn.upsaclay.fr/competitions/20244
The competition is aimed at extraction of opinion tuples (see attached images) from texts written in Russian.
It proceeds the past RuSentNE-2023 codalab competition findings:
๐ Past year competition: https://www.dialog-21.ru/media/5896/golubevaplusetal118.pdf
๐ LLM reasoning ๐ง : https://arxiv.org/abs/2404.12342
For those who interested to adopt Generative AI, the complete information about competition is below:
๐ RuOpinionNE-2024: https://codalab.lisn.upsaclay.fr/competitions/20244
๐ Task description: https://codalab.lisn.upsaclay.fr/competitions/20244#learn_the_details-overview
๐ To follow updates: https://t.me/RuOpinionNE2024
โฐ Stages Deadlines (might be extended)
๐ฆ Submission details (bottom of the competition page)
๐ For questions you can contact @nicolay-r : https://nicolay-r.github.io/
๐งช Most recent findings on LLM application: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
However, they did not go further with:
โ๏ธ annoation of other sources of opinion causes: entities, out-of-context object (None).
๐ evaluation of factual statements that support the extracted sentiment.
To address these limitations, so far we launch ๐ RuOpinionNE-2024 competition on the Codalab platform:
๐ https://codalab.lisn.upsaclay.fr/competitions/20244
The competition is aimed at extraction of opinion tuples (see attached images) from texts written in Russian.
It proceeds the past RuSentNE-2023 codalab competition findings:
๐ Past year competition: https://www.dialog-21.ru/media/5896/golubevaplusetal118.pdf
๐ LLM reasoning ๐ง : https://arxiv.org/abs/2404.12342
For those who interested to adopt Generative AI, the complete information about competition is below:
๐ RuOpinionNE-2024: https://codalab.lisn.upsaclay.fr/competitions/20244
๐ Task description: https://codalab.lisn.upsaclay.fr/competitions/20244#learn_the_details-overview
๐ To follow updates: https://t.me/RuOpinionNE2024
โฐ Stages Deadlines (might be extended)
๐ฆ Submission details (bottom of the competition page)
๐ For questions you can contact @nicolay-r : https://nicolay-r.github.io/
๐งช Most recent findings on LLM application: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
reacted to
Tonic's
post with ๐
about 2 months ago
Post
2730
๐๐ปโโ๏ธhey there folks ,
did you know that https://huggingface.co/lmms-lab released a new version of ๐๐Llava on thursday ? Now it has ๐ฅvideo understanding !
check it out ๐๐ป
collection : lmms-lab/llava-video-661e86f5e8dabc3ff793c944
demo : Tonic/Llava-Video
did you know that https://huggingface.co/lmms-lab released a new version of ๐๐Llava on thursday ? Now it has ๐ฅvideo understanding !
check it out ๐๐ป
collection : lmms-lab/llava-video-661e86f5e8dabc3ff793c944
demo : Tonic/Llava-Video
reacted to
nroggendorff's
post with ๐
about 2 months ago
Post
1517
pretty much all of the values in the llama training post are placeholders, so if you dont get a desireable result tweak and tweak and tweak. it took months to get smallama to do anything
reacted to
clem's
post with ๐
about 2 months ago
Post
2056
What are we thinking about MovieGen from Meta? Are the researchers on Hugging Face to be able to ask them questions?
The paper is here: https://ai.meta.com/static-resource/movie-gen-research-paper
The paper is here: https://ai.meta.com/static-resource/movie-gen-research-paper