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--- |
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base_model: BAAI/bge-small-en-v1.5 |
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datasets: [] |
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language: [] |
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library_name: sentence-transformers |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:1500 |
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- loss:BatchAllTripletLoss |
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widget: |
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- source_sentence: 实现一段代码，将给定短语中的每个单词按字母顺序排列，然后按照每个单词首字母的字典顺序对这些单词进行排序，并保留每个单词中字符的原始顺序。 |
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sentences: |
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- 可以给出小猫、小狗和小兔的年龄对温度的适应度和健康的影响的代码吗？ |
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- 绘制一个5x5的矩阵，矩阵中的单元格颜色交替，模式如下所描述（黑=实心块，白=空白块）： |
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- "Write a Java program that simulates a basic text-based RPG (Role-Playing Game)\ |
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\ with the following features:\n - Character creation: allow the user to choose\ |
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\ a name, class, and starting attributes.\n - Combat system: implement turn-based\ |
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\ combat against randomly generated enemies.\n - Leveling up: increase character\ |
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\ attributes based on experience points gained from defeating enemies.\n - Inventory\ |
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\ system: collect items dropped by defeated enemies and manage them in an inventory\ |
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\ menu.\n " |
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- source_sentence: 'Create a HTML page with an ordered list of items using Five items: |
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apple, banana, orange, strawberry, and lemon.' |
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sentences: |
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- 'How can we modify the given Ruby code to determine if a candidate fulfills all |
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the eligibility criteria for the US presidency? Specifically, in addition to the |
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age requirement of 35 years, we also need to check if the candidate is a natural-born |
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citizen and has been a resident of the United States for at least 14 years. Can |
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you suggest a more optimized code to accomplish this task? ' |
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- 从系统生物学的视角解读生物科技的重要性。 |
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- 为一家以室内植物为主的植物店计划一场营销活动。 |
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- source_sentence: 请使用尽可能简单的语言解释主体-客体模型（Subject-Object Model）。 |
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sentences: |
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- 'Generate an algorithm for the game Hangman. ' |
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- 如何使用 JavaScript 将两个 HTML 元素互换位置？ |
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- 'In Swift, what function can I use to shorten the sentence "I''m feeling kind |
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of tired after having worked all day" while maintaining the same meaning and tone? |
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Can you provide an example of the shortened sentence using the function? ' |
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- source_sentence: 在一个Dockerfile中，何时使用ADD指令与COPY指令？ |
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sentences: |
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- 在给定的数列中寻找子数组，使其元素和最大。 |
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- 'In an Excel spreadsheet that contains information about employees, there is a |
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column for job titles. John''s job title is listed as "Manager." Add a description |
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of John''s responsibilities to the sentence "John was a" using an Excel formula. ' |
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- 多项式p(z) = z^3 + Az^2 + Bz + C为复数系数多项式。如果我们知道A、B、C为实数，而p根存在两个复数根r1和r2，第三个根也是它们的共轭复数r3。证明虚部非零。 |
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- source_sentence: 解析三种大数据分析工具，请包括使用案例。 |
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sentences: |
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- "How can the traveler determine the correct number of open hands after the 2021st\ |
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\ gesture based on the pattern provided by the villagers? \nHere is a possible\ |
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\ solution in Ruby:\nopen_hands = 1\nclosed_hands = 1\n(1..2021).each do |i|\n\ |
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\ if i % 2 == 1\n closed_hands += open_hands\n open_hands = closed_hands\ |
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\ - open_hands\n else\n open_hands += closed_hands\n closed_hands = open_hands\ |
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\ - closed_hands\n end\nend\nputs \"After the 2021st gesture, the number of open\ |
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\ hands is #{open_hands}.\" \nCan you explain how this Ruby code works to solve\ |
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\ the puzzle posed by the villagers? " |
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- 'How can I use C# code to simulate the discovery of a rare and valuable book collection |
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secretly housed in a local library, and then capture the town''s reaction to the |
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discovery? ' |
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- 'How can I create a stylish outfit that incorporates intricate embroidery patterns |
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and luxurious fabric, while also being budget-friendly? Can you provide a creative |
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solution using Ruby code that balances affordability and elegance? |
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|
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For example, you could use the following code to search for affordable fabric |
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options and embroidery patterns: |
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|
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```ruby |
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|
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require ''etsy'' |
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# Search for affordable fabrics on Etsy |
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results = Etsy::Search.new(''affordable fabric'', :includes => [:Images], :price_max |
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=> 50).results |
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# Search for intricate embroidery patterns on Etsy |
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embroidery_results = Etsy::Search.new(''intricate embroidery pattern'', :includes |
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=> [:Images], :price_max => 100).results |
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# Choose a fabric and embroidery pattern to use in the outfit |
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chosen_fabric = results.sample |
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chosen_embroidery = embroidery_results.sample |
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# Use the chosen fabric and embroidery pattern to create a stylish outfit |
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|
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# ... |
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``` |
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Can you provide additional code and reasoning to complete the solution? ' |
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--- |
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|
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# SentenceTransformer based on BAAI/bge-small-en-v1.5 |
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|
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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|
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## Model Details |
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|
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 384 tokens |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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|
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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|
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(2): Normalize() |
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) |
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``` |
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|
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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|
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```bash |
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pip install -U sentence-transformers |
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``` |
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|
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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|
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# Download from the 🤗 Hub |
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model = SentenceTransformer("Snivellus789/router-embedding-tuned-2") |
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# Run inference |
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sentences = [ |
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'解析三种大数据分析工具，请包括使用案例。', |
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"How can I create a stylish outfit that incorporates intricate embroidery patterns and luxurious fabric, while also being budget-friendly? Can you provide a creative solution using Ruby code that balances affordability and elegance?\nFor example, you could use the following code to search for affordable fabric options and embroidery patterns:\n```ruby\nrequire 'etsy'\n# Search for affordable fabrics on Etsy\nresults = Etsy::Search.new('affordable fabric', :includes => [:Images], :price_max => 50).results\n# Search for intricate embroidery patterns on Etsy\nembroidery_results = Etsy::Search.new('intricate embroidery pattern', :includes => [:Images], :price_max => 100).results\n# Choose a fabric and embroidery pattern to use in the outfit\nchosen_fabric = results.sample\nchosen_embroidery = embroidery_results.sample\n# Use the chosen fabric and embroidery pattern to create a stylish outfit\n# ...\n```\nCan you provide additional code and reasoning to complete the solution? ", |
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"How can I use C# code to simulate the discovery of a rare and valuable book collection secretly housed in a local library, and then capture the town's reaction to the discovery? ", |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 384] |
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|
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 1,500 training samples |
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* Columns: <code>sentence</code> and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence | label | |
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|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------| |
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| type | string | int | |
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| details | <ul><li>min: 8 tokens</li><li>mean: 95.61 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>0: ~50.00%</li><li>1: ~50.00%</li></ul> | |
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* Samples: |
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| sentence | label | |
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|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
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| <code>请输出所有跟政企市场相关的关键词列表</code> | <code>0</code> | |
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| <code>开发一个定制的JavaScript解决方案，用于有效地平衡和排序一个二叉树。你可以假设输入是一个平衡因子擯至2的大O()为Log(N)的AVL树。专注于实现自我调整二叉搜索树的变换，当面对不平衡操作时，如插入或删除节点。确保你的解决方案为潜在的边缘案例做好准备，并具有健壮的错误处理策略。你的代码应该清晰地记录和优化效率。</code> | <code>0</code> | |
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| <code>在一个尚未被公开的领域中，描述五个最具创新性的产品概念。</code> | <code>0</code> | |
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* Loss: [<code>BatchAllTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchalltripletloss) |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 2 |
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- `warmup_ratio`: 0.1 |
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- `bf16`: True |
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- `batch_sampler`: no_duplicates |
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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: no |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 2 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: True |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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</details> |
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|
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### Training Logs |
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| Epoch | Step | Training Loss | |
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|:------:|:----:|:-------------:| |
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| 1.0638 | 100 | 0.097 | |
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|
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### Framework Versions |
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- Python: 3.10.12 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.42.4 |
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- PyTorch: 2.3.1+cu121 |
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- Accelerate: 0.33.0.dev0 |
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- Datasets: 2.20.0 |
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- Tokenizers: 0.19.1 |
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|
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## Citation |
|
|
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### BibTeX |
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|
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#### Sentence Transformers |
|
```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### BatchAllTripletLoss |
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```bibtex |
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@misc{hermans2017defense, |
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title={In Defense of the Triplet Loss for Person Re-Identification}, |
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author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, |
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year={2017}, |
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eprint={1703.07737}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
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*Clearly define terms in order to be accessible across audiences.* |
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