| | --- |
| | tags: |
| | - setfit |
| | - sentence-transformers |
| | - text-classification |
| | - generated_from_setfit_trainer |
| | widget: |
| | - text: 'Procedure note: Colonoscopy completed. Findings consistent with diverticulosis. |
| | CPT 45378 used.' |
| | - text: 'Patient diagnosed with Acute appendicitis (ICD-10-CM: K35.80). Appendectomy |
| | performed (CPT: 44950).' |
| | - text: If your child shows signs of anxiety, consider talking to a pediatric psychologist. |
| | - text: Encounter billed under APC 5371. Patient stable. To follow up with PCP. |
| | - text: Learn how cognitive behavioral therapy can improve focus and reduce anxiety. |
| | metrics: |
| | - accuracy |
| | pipeline_tag: text-classification |
| | library_name: setfit |
| | inference: true |
| | base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
| | --- |
| | |
| | # SetFit with sentence-transformers/paraphrase-mpnet-base-v2 |
| |
|
| | This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) |
| | - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
| | - **Maximum Sequence Length:** 512 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 | |
| | |:--------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | | med | <ul><li>'Patient diagnosed with Acute appendicitis (ICD-10-CM: K35.80). Appendectomy performed (CPT: 44950).'</li><li>'Follow-up on 04/10/2025. Status post laparoscopic cholecystectomy (ICD-10: K81.1, CPT: 47562).'</li><li>'Procedure note: Colonoscopy completed. Findings consistent with diverticulosis. CPT 45378 used.'</li></ul> | |
| | | general | <ul><li>'The Chinese gender calendar predicts baby gender based on conception month and mother’s age.'</li><li>'This article outlines tips for stress management through meditation and mindfulness.'</li><li>'Eating a balanced diet and regular exercise improves overall well-being.'</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("ashercn97/medicalcode-classifier") |
| | # Run inference |
| | preds = model("Encounter billed under APC 5371. Patient stable. To follow up with PCP.") |
| | ``` |
| |
|
| | <!-- |
| | ### 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 | 10 | 11.4375 | 14 | |
| |
|
| | | Label | Training Sample Count | |
| | |:--------|:----------------------| |
| | | med | 8 | |
| | | general | 8 | |
| |
|
| | ### Training Hyperparameters |
| | - batch_size: (8, 8) |
| | - num_epochs: (2, 2) |
| | - max_steps: -1 |
| | - sampling_strategy: oversampling |
| | - 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 | |
| | |:------:|:----:|:-------------:|:---------------:| |
| | | 0.0556 | 1 | 0.385 | - | |
| | |
| | ### Framework Versions |
| | - Python: 3.10.12 |
| | - SetFit: 1.1.2 |
| | - Sentence Transformers: 4.0.2 |
| | - Transformers: 4.51.2 |
| | - PyTorch: 2.6.0+cpu |
| | - Datasets: 3.5.0 |
| | - Tokenizers: 0.21.1 |
| | |
| | ## 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.* |
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| | ## 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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