| --- |
| tags: |
| - setfit |
| - sentence-transformers |
| - text-classification |
| - generated_from_setfit_trainer |
| widget: |
| - text: What are the deadlines and deliverables listed in this project plan summary? |
| - text: Quickly, just give me the dates and locations mentioned in this travel itinerary. |
| - text: Convert this list of configuration parameters into a JSON object. Keys are |
| parameter names, values are their settings. |
| - text: My GitLab CI/CD pipeline fails at `npm install`. The error log is `[log snippet]`. |
| What's wrong? |
| - text: 'Three friends, Alice, Bob, and Carol, each have a favorite color: red, blue, |
| or green. Alice doesn''t like red. Bob doesn''t like green. The person who likes |
| blue is not Carol. Who likes which color?' |
| metrics: |
| - accuracy |
| pipeline_tag: text-classification |
| library_name: setfit |
| inference: true |
| base_model: sentence-transformers/all-MiniLM-L6-v2 |
| --- |
| |
| # SetFit with sentence-transformers/all-MiniLM-L6-v2 |
|
|
| This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. |
|
|
| The model has been trained using an efficient few-shot learning technique that involves: |
|
|
| 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
| 2. Training a classification head with features from the fine-tuned Sentence Transformer. |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** SetFit |
| - **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) |
| - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
| - **Maximum Sequence Length:** 256 tokens |
| - **Number of Classes:** 4 classes |
| <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### Model Sources |
|
|
| - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
| - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
| - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
|
|
| ### Model Labels |
| | Label | Examples | |
| |:------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | simple_chat | <ul><li>'What precisely is your nature?'</li><li>'What is your primary function?'</li><li>'Good morning.'</li></ul> | |
| | extraction | <ul><li>'Summarize the main benefits of this service based on the provided marketing copy.'</li><li>"Can you just summarize the key findings from this research data list? I don't need all the numbers."</li><li>'Convert this list of configuration parameters into a JSON object. Keys are parameter names, values are their settings.'</li></ul> | |
| | reasoning | <ul><li>"What's the best way to pivot our struggling brick-and-mortar bookstore to survive in the digital age?"</li><li>'Develop a decision tree for purchasing a new company car, considering budget, fuel efficiency, maintenance costs, and resale value.'</li><li>"What's 15% of 250?"</li></ul> | |
| | coding | <ul><li>'Refactor this C++ legacy code to use `std::unique_ptr` and `std::shared_ptr` instead of raw pointers.'</li><li>'Refactor this spaghetti PHP script to separate business logic, presentation, and data access layers.'</li><li>"What's the fundamental difference between SQL and NoSQL databases, and when should I use each?"</li></ul> | |
| |
| ## Uses |
| |
| ### Direct Use for Inference |
| |
| First install the SetFit library: |
| |
| ```bash |
| pip install setfit |
| ``` |
| |
| Then you can load this model and run inference. |
| |
| ```python |
| from setfit import SetFitModel |
| |
| # Download from the 🤗 Hub |
| model = SetFitModel.from_pretrained("setfit_model_id") |
| # Run inference |
| preds = model("What are the deadlines and deliverables listed in this project plan summary?") |
| ``` |
| |
| <!-- |
| ### Downstream Use |
| |
| *List how someone could finetune this model on their own dataset.* |
| --> |
| |
| <!-- |
| ### Out-of-Scope Use |
| |
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
| |
| <!-- |
| ## Bias, Risks and Limitations |
| |
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
| |
| <!-- |
| ### Recommendations |
| |
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
| |
| ## Training Details |
| |
| ### Training Set Metrics |
| | Training set | Min | Median | Max | |
| |:-------------|:----|:--------|:----| |
| | Word count | 1 | 13.5101 | 41 | |
| |
| | Label | Training Sample Count | |
| |:------------|:----------------------| |
| | simple_chat | 48 | |
| | extraction | 50 | |
| | reasoning | 50 | |
| | coding | 50 | |
| |
| ### Training Hyperparameters |
| - batch_size: (16, 16) |
| - num_epochs: (1, 1) |
| - max_steps: -1 |
| - sampling_strategy: oversampling |
| - num_iterations: 10 |
| - body_learning_rate: (2e-05, 1e-05) |
| - head_learning_rate: 0.01 |
| - loss: CosineSimilarityLoss |
| - distance_metric: cosine_distance |
| - margin: 0.25 |
| - end_to_end: False |
| - use_amp: True |
| - warmup_proportion: 0.1 |
| - l2_weight: 0.01 |
| - seed: 42 |
| - evaluation_strategy: no |
| - eval_max_steps: -1 |
| - load_best_model_at_end: False |
| |
| ### Training Results |
| | Epoch | Step | Training Loss | Validation Loss | |
| |:------:|:----:|:-------------:|:---------------:| |
| | 0.0040 | 1 | 0.5538 | - | |
| | 0.2016 | 50 | 0.2712 | - | |
| | 0.4032 | 100 | 0.1337 | - | |
| | 0.6048 | 150 | 0.0604 | - | |
| | 0.8065 | 200 | 0.0284 | - | |
| |
| ### Framework Versions |
| - Python: 3.9.6 |
| - SetFit: 1.1.3 |
| - Sentence Transformers: 5.1.2 |
| - Transformers: 4.57.6 |
| - PyTorch: 2.8.0 |
| - Datasets: 4.5.0 |
| - Tokenizers: 0.22.1 |
| |
| ## Citation |
| |
| ### BibTeX |
| ```bibtex |
| @article{https://doi.org/10.48550/arxiv.2209.11055, |
| doi = {10.48550/ARXIV.2209.11055}, |
| url = {https://arxiv.org/abs/2209.11055}, |
| author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
| keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
| title = {Efficient Few-Shot Learning Without Prompts}, |
| publisher = {arXiv}, |
| year = {2022}, |
| copyright = {Creative Commons Attribution 4.0 International} |
| } |
| ``` |
| |
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| ## Glossary |
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| --> |
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|
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| ## Model Card Authors |
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|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
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| ## Model Card Contact |
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| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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