| --- |
| tags: |
| - setfit |
| - sentence-transformers |
| - text-classification |
| - generated_from_setfit_trainer |
| widget: |
| - text: Although traditional database search methods can effectively identify peptide |
| matches, this approach correlates tandem mass spectral data with amino acid sequences |
| in a protein database 'however' providing additional confirmation and improving |
| identification accuracy. |
| - text: The study involved 30 smallholder farmers from three different regions in |
| Africa, each with an average farm size of 1.5 hectares and an annual income from |
| farming of approximately $1,500. |
| - text: This study aimed to evaluate the efficacy and safety of interferon α2b plus |
| ribavirin for 48 weeks or 24 weeks compared to interferon α2b plus placebo for |
| 48 weeks in the treatment of chronic hepatitis C virus infection. |
| - text: The study reported that 73% of the psychotherapists endorsed the use of cognitive |
| techniques in their treatment of eating disorders, while 61% reported using behavioral |
| techniques. |
| - text: Previous research on the psychoanalytic concept of the working alliance has |
| established its significance in therapeutic change and identified key components |
| such as the bond between therapist and client and the agreement on therapeutic |
| goals. |
| metrics: |
| - accuracy |
| pipeline_tag: text-classification |
| library_name: setfit |
| inference: true |
| base_model: sentence-transformers/all-MiniLM-L6-v2 |
| model-index: |
| - name: SetFit with sentence-transformers/all-MiniLM-L6-v2 |
| results: |
| - task: |
| type: text-classification |
| name: Text Classification |
| dataset: |
| name: Unknown |
| type: unknown |
| split: test |
| metrics: |
| - type: accuracy |
| value: 0.9498398588143016 |
| name: Accuracy |
| --- |
| |
| # 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:** 2 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 | |
| |:------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | Misc | <ul><li>'Pravastatin therapy in patients with average cholesterol levels following myocardial infarction has been shown to reduce the risk of coronary events, highlighting the importance of lipid-lowering therapy in internal medicine for cardiovascular disease prevention.'</li><li>'However, the efficacy of pravastatin in patients with average cholesterol levels is less clear.'</li><li>'This study investigates the impact of Pravastatin on reducing coronary events in internal medicine patients with average cholesterol levels after a myocardial infarction.'</li></ul> | |
| | Uncertainty | <ul><li>'Despite the widespread use of pravastatin in post-myocardial infarction patients with average cholesterol levels, the evidence regarding its impact on coronary events remains inconclusive and sometimes contradictory.'</li><li>'Despite the findings of this study showing a reduction in coronary events with Pravastatin use in patients with average cholesterol levels, contrasting evidence exists suggesting no significant benefit in similar patient populations (Miller et al., 2018).'</li><li>'Despite the proven benefits of dual antiplatelet therapy with aspirin and clopidogrel in the secondary prevention of cardiovascular events, particularly in coronary artery disease, there is a paucity of data specifically addressing its use in stroke or transient ischemic attack (TIA) patients.'</li></ul> | |
|
|
| ## Evaluation |
|
|
| ### Metrics |
| | Label | Accuracy | |
| |:--------|:---------| |
| | **all** | 0.9498 | |
|
|
| ## 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("Corran/SciGenSetfit24Binary") |
| # Run inference |
| preds = model("The study reported that 73% of the psychotherapists endorsed the use of cognitive techniques in their treatment of eating disorders, while 61% reported using behavioral techniques.") |
| ``` |
|
|
| <!-- |
| ### 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 | 8 | 29.6038 | 60 | |
|
|
| | Label | Training Sample Count | |
| |:------------|:----------------------| |
| | Misc | 2500 | |
| | Uncertainty | 2500 | |
|
|
| ### Training Hyperparameters |
| - batch_size: (300, 300) |
| - num_epochs: (1, 1) |
| - max_steps: -1 |
| - sampling_strategy: oversampling |
| - num_iterations: 5 |
| - 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: False |
| - warmup_proportion: 0.1 |
| - l2_weight: 0.01 |
| - seed: 42 |
| - eval_max_steps: -1 |
| - load_best_model_at_end: False |
|
|
| ### Training Results |
| | Epoch | Step | Training Loss | Validation Loss | |
| |:------:|:----:|:-------------:|:---------------:| |
| | 0.0060 | 1 | 0.4529 | - | |
| | 0.2994 | 50 | 0.3104 | - | |
| | 0.5988 | 100 | 0.2514 | - | |
| | 0.8982 | 150 | 0.25 | - | |
| | 1.0 | 167 | - | 0.2479 | |
| | 0.0060 | 1 | 0.2406 | - | |
| | 0.2994 | 50 | 0.1576 | - | |
| | 0.5988 | 100 | 0.0912 | - | |
| | 0.8982 | 150 | 0.0656 | - | |
| | 1.0 | 167 | - | 0.0683 | |
| | 0.0060 | 1 | 0.0827 | - | |
| | 0.2994 | 50 | 0.0581 | - | |
| | 0.5988 | 100 | 0.0393 | - | |
| | 0.8982 | 150 | 0.0339 | - | |
| | 1.0 | 167 | - | 0.0516 | |
|
|
| ### Framework Versions |
| - Python: 3.10.12 |
| - SetFit: 1.2.0.dev0 |
| - Sentence Transformers: 3.1.1 |
| - Transformers: 4.42.2 |
| - PyTorch: 2.5.1+cu121 |
| - Datasets: 3.2.0 |
| - Tokenizers: 0.19.1 |
|
|
| ## 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} |
| } |
| ``` |
|
|
| <!-- |
| ## Glossary |
|
|
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
|
|
| <!-- |
| ## Model Card Authors |
|
|
| *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 |
|
|
| *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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