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--- |
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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:849 |
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- loss:MultipleNegativesRankingLoss |
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base_model: sentence-transformers/all-MiniLM-L12-v2 |
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widget: |
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- source_sentence: Graphic designer who specializes in creating visual content for |
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brands, including logos, marketing materials, and user interfaces. Focuses on |
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aesthetics, user experience, and brand identity. |
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sentences: |
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- 'user_1: I''m looking to refresh my company''s brand image but don''t know where |
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to start. |
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user_2: You should consult a brand manager.' |
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- 'user_1: I need help designing a logo for my new business. |
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user_2: Have you thought about hiring a graphic designer? |
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user_1: Yes, I want something that really represents my brand.' |
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- 'user_1: My car''s making a weird noise, and I don''t know what to do. |
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user_2: You should take it to a mechanic.' |
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- source_sentence: Nutritionist who specializes in dietary planning and nutritional |
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counseling. Helps clients achieve their health goals through personalized meal |
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plans and education. |
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sentences: |
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- 'user_1: I''m trying to lose weight but I don''t know what to eat. |
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user_2: Have you considered talking to a nutritionist?' |
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- 'user_1: Our database is running slow, and I don''t know why. |
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user_2: Have you checked the indexing?' |
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- 'user_1: I need help fixing my car''s engine; it''s making a weird noise. |
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user_2: Have you checked the oil level?' |
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- source_sentence: 'user_2: Sure, what problem are you working on?' |
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sentences: |
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- Gardening expert specializing in vegetable gardening techniques and plant care. |
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- Event planner focusing on corporate events and wedding coordination. |
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- Math tutor specializing in teaching and clarifying mathematical concepts and problem-solving. |
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- source_sentence: 'user_2: Have you thought about getting some storage bins?' |
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sentences: |
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- Web developer focused on software engineering and application design. |
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- Professional organizer specializing in home organization and decluttering strategies. |
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- Pet behavior specialist who provides advice on dog breeds and training for small |
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living spaces. |
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- source_sentence: 'user_1: Maybe the national parks, I want to see some nature.' |
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sentences: |
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- Mental health counselor specializing in stress management and coping strategies. |
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- Data analyst focusing on market trends and business intelligence. |
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- Travel consultant specializing in road trip planning and national park itineraries. |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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--- |
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# SentenceTransformer based on sentence-transformers/all-MiniLM-L12-v2 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) on the semantic_triplets_round1 and inverse_semantic_triplets datasets. 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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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) <!-- at revision c004d8e3e901237d8fa7e9fff12774962e391ce5 --> |
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- **Maximum Sequence Length:** 128 tokens |
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- **Output Dimensionality:** 384 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Datasets:** |
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- semantic_triplets_round1 |
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- inverse_semantic_triplets |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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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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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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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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# Download from the 🤗 Hub |
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model = SentenceTransformer("sentence_transformers_model_id") |
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# Run inference |
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sentences = [ |
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'user_1: Maybe the national parks, I want to see some nature.', |
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'Travel consultant specializing in road trip planning and national park itineraries.', |
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'Data analyst focusing on market trends and business intelligence.', |
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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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# 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 Datasets |
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#### semantic_triplets_round1 |
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* Dataset: semantic_triplets_round1 |
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* Size: 422 training samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 422 samples: |
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| | anchor | positive | negative | |
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|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 10 tokens</li><li>mean: 17.44 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 14.17 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 12.49 tokens</li><li>max: 20 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------| |
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| <code>user_1: Can anyone recommend a good app for tracking my expenses?</code> | <code>Personal finance advisor specializing in budgeting tools and expense tracking applications.</code> | <code>Fitness instructor focusing on workout plans and nutrition.</code> | |
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| <code>user_1: Can anyone recommend a good workout routine for beginners?</code> | <code>Fitness trainer who specializes in creating beginner workout plans and exercise coaching.</code> | <code>Financial advisor focused on investment strategies and retirement planning.</code> | |
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| <code>user_2: What kind of vegetables are you thinking of planting?</code> | <code>Gardening expert who provides guidance on vegetable gardening techniques and plant care.</code> | <code>Investment advisor specializing in stock market strategies and financial planning.</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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#### inverse_semantic_triplets |
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* Dataset: inverse_semantic_triplets |
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* Size: 427 training samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 427 samples: |
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| | anchor | positive | negative | |
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|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 18 tokens</li><li>mean: 28.42 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 40.04 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 27.66 tokens</li><li>max: 62 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------| |
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| <code>UX researcher specializing in user experience design and user testing. Conducts research to understand user needs and improve product usability.</code> | <code>user_1: I'm looking for ways to improve the usability of our app.<br>user_2: Have you considered conducting user interviews?</code> | <code>user_1: I need to plan a trip to Europe next summer.<br>user_2: What countries are you thinking about visiting?</code> | |
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| <code>Software developer specializing in web applications, proficient in various programming languages and frameworks. I design, develop, and maintain software solutions, focusing on user experience and functionality.</code> | <code>user_1: I'm trying to build a web application, but I'm stuck on how to integrate the backend with the frontend.<br>user_2: What technologies are you using for both?<br>user_1: I’m using Node.js for the backend and React for the frontend.</code> | <code>user_1: I'm looking for a good recipe for chocolate chip cookies.<br>user_2: I can share my favorite one!</code> | |
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| <code>Marketing strategist who focuses on developing comprehensive marketing plans to drive brand engagement and sales growth. Specializes in digital marketing and content strategy.</code> | <code>user_1: I'm launching a new product and need a marketing strategy.<br>user_2: Have you set any goals for your campaign?</code> | <code>user_1: I'm looking for a new pair of running shoes.<br>user_2: What brand do you prefer?</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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### Evaluation Datasets |
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#### semantic_triplets_round1 |
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* Dataset: semantic_triplets_round1 |
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* Size: 47 evaluation samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 47 samples: |
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| | anchor | positive | negative | |
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|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 12 tokens</li><li>mean: 17.87 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 14.32 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 12.49 tokens</li><li>max: 16 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------| |
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| <code>user_1: What's the best way to train my puppy to stop barking?</code> | <code>Dog training specialist focused on behavioral issues and obedience training.</code> | <code>Financial advisor who specializes in investment strategies and wealth management.</code> | |
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| <code>user_2: What vegetables do you want to grow?</code> | <code>Gardening expert specializing in vegetable gardening and sustainable practices.</code> | <code>Real estate agent focusing on home buying and selling.</code> | |
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| <code>user_1: Anyone have tips on how to improve my running time for a 5k?</code> | <code>Running coach specializing in training plans and performance improvement.</code> | <code>Financial advisor focusing on investment strategies and retirement planning.</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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#### inverse_semantic_triplets |
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* Dataset: inverse_semantic_triplets |
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* Size: 48 evaluation samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 48 samples: |
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| | anchor | positive | negative | |
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|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 20 tokens</li><li>mean: 28.42 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 39.71 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 28.4 tokens</li><li>max: 52 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------| |
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| <code>Graphic designer who specializes in creating visual content for brands, including logos, marketing materials, and user interfaces. Focuses on aesthetics, user experience, and brand identity.</code> | <code>user_1: I need help designing a logo for my new business.<br>user_2: Have you thought about hiring a graphic designer?<br>user_1: Yes, I want something that really represents my brand.</code> | <code>user_1: My car's making a weird noise, and I don't know what to do.<br>user_2: You should take it to a mechanic.</code> | |
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| <code>Physical therapist specializing in rehabilitation for sports injuries, pain management, and improving mobility through tailored exercise programs.</code> | <code>user_1: I twisted my ankle playing basketball, and it's really swollen.<br>user_2: Have you seen a doctor about it?</code> | <code>user_1: I'm thinking of redecorating my living room.<br>user_2: What style are you going for?</code> | |
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| <code>An accountant who specializes in financial record-keeping, tax preparation, and business consulting. Provides services to help clients manage their finances effectively and ensure compliance with tax regulations.</code> | <code>user_1: I need help with my taxes this year.<br>user_2: Are you looking for someone to prepare them for you?</code> | <code>user_1: I'm thinking about getting a puppy.</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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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`: 1 |
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- `warmup_ratio`: 0.1 |
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- `batch_sampler`: no_duplicates |
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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`: steps |
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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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- `torch_empty_cache_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`: 1 |
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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`: False |
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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`: None |
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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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- `include_for_metrics`: [] |
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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 |
|
|
- `ray_scope`: last |
|
|
- `ddp_timeout`: 1800 |
|
|
- `torch_compile`: False |
|
|
- `torch_compile_backend`: None |
|
|
- `torch_compile_mode`: None |
|
|
- `include_tokens_per_second`: False |
|
|
- `include_num_input_tokens_seen`: False |
|
|
- `neftune_noise_alpha`: None |
|
|
- `optim_target_modules`: None |
|
|
- `batch_eval_metrics`: False |
|
|
- `eval_on_start`: False |
|
|
- `use_liger_kernel`: False |
|
|
- `eval_use_gather_object`: False |
|
|
- `average_tokens_across_devices`: False |
|
|
- `prompts`: None |
|
|
- `batch_sampler`: no_duplicates |
|
|
- `multi_dataset_batch_sampler`: proportional |
|
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|
|
|
</details> |
|
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|
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### Framework Versions |
|
|
- Python: 3.12.9 |
|
|
- Sentence Transformers: 4.1.0 |
|
|
- Transformers: 4.52.4 |
|
|
- PyTorch: 2.7.1 |
|
|
- Accelerate: 1.8.1 |
|
|
- Datasets: 3.6.0 |
|
|
- Tokenizers: 0.21.1 |
|
|
|
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## Citation |
|
|
|
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### BibTeX |
|
|
|
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|
#### Sentence Transformers |
|
|
```bibtex |
|
|
@inproceedings{reimers-2019-sentence-bert, |
|
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
|
month = "11", |
|
|
year = "2019", |
|
|
publisher = "Association for Computational Linguistics", |
|
|
url = "https://arxiv.org/abs/1908.10084", |
|
|
} |
|
|
``` |
|
|
|
|
|
#### MultipleNegativesRankingLoss |
|
|
```bibtex |
|
|
@misc{henderson2017efficient, |
|
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
|
year={2017}, |
|
|
eprint={1705.00652}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.CL} |
|
|
} |
|
|
``` |
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