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--- |
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tags: |
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- setfit |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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widget: |
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- text: Framing shop providing custom picture framing and art preservation services. |
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We frame artwork, photographs, and memorabilia with quality materials. |
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- text: Lymph node pathology reveals metastatic adenocarcinoma with extracapsular |
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extension. Immunostains are consistent with breast primary tumor origin. |
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- text: Fluoroscopy-guided placement of central venous catheter for chemotherapy administration. |
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Port placement performed successfully with proper tip positioning confirmed. |
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- text: Immunotherapy harnesses the body's immune system to fight cancer. Checkpoint |
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inhibitors block proteins like PD-1 and CTLA-4 that normally prevent immune cells |
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from attacking cancer cells, while CAR-T therapy genetically modifies T cells |
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to better recognize cancer. |
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- text: Endometrial biopsy shows endometrioid adenocarcinoma, grade 2, with squamous |
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differentiation. Deep myometrial invasion is present involving the outer half. |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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library_name: setfit |
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inference: true |
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base_model: TimKond/S-PubMedBert-MedQuAD |
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model-index: |
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- name: SetFit with TimKond/S-PubMedBert-MedQuAD |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: Unknown |
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type: unknown |
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split: test |
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metrics: |
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- type: accuracy |
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value: 1.0 |
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name: Accuracy |
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--- |
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# SetFit with TimKond/S-PubMedBert-MedQuAD |
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [TimKond/S-PubMedBert-MedQuAD](https://huggingface.co/TimKond/S-PubMedBert-MedQuAD) 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. |
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The model has been trained using an efficient few-shot learning technique that involves: |
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
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2. Training a classification head with features from the fine-tuned Sentence Transformer. |
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## Model Details |
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [TimKond/S-PubMedBert-MedQuAD](https://huggingface.co/TimKond/S-PubMedBert-MedQuAD) |
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
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- **Maximum Sequence Length:** 512 tokens |
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- **Number of Classes:** 2 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
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### Model Labels |
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| Label | Examples | |
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|:-------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| non_oncology | <ul><li>'Food truck festival coordination service organizing mobile vendor events, permit management, and location logistics.'</li><li>'Traditional Japanese tea ceremony instruction teaching the ancient art of chanoyu. Cultural immersion program exploring Japanese aesthetics, philosophy, and mindfulness practices.'</li><li>'Yoga studio offering various classes including Hatha, Vinyasa, and restorative yoga. We provide a peaceful environment for mindfulness and physical wellness.'</li></ul> | |
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| oncology | <ul><li>'Combination therapy uses multiple treatment modalities to improve outcomes. For example, neoadjuvant chemotherapy can shrink tumors before surgery, while adjuvant therapy eliminates remaining cancer cells after primary treatment.'</li><li>'Hereditary cancer syndromes account for 5-10% of all cancers. BRCA1 and BRCA2 mutations significantly increase breast and ovarian cancer risk, while Lynch syndrome increases colorectal and endometrial cancer risk.'</li><li>'Endoplasmic Reticulum: The cisternae of the endoplasmic reticulum are distended by fluid in hydropic swelling. In other forms of acute, reversible cell injury, membrane-bound polysomes may undergo disaggregation and detach from the surface of the rough endoplasmic reticulum.'</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 1.0 | |
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## Uses |
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### Direct Use for Inference |
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First install the SetFit library: |
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```bash |
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pip install setfit |
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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 setfit import SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("setfit_model_id") |
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# Run inference |
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preds = model("Lymph node pathology reveals metastatic adenocarcinoma with extracapsular extension. Immunostains are consistent with breast primary tumor origin.") |
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``` |
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<!-- |
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### Downstream Use |
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*List how someone could finetune this model on their own dataset.* |
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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 Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:--------|:----| |
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| Word count | 12 | 31.1543 | 929 | |
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| Label | Training Sample Count | |
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|:-------------|:----------------------| |
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| non_oncology | 293 | |
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| oncology | 180 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (1, 1) |
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- max_steps: 200 |
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- sampling_strategy: oversampling |
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- body_learning_rate: (2e-05, 1e-05) |
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- head_learning_rate: 0.01 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: False |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- l2_weight: 0.01 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: False |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:-----:|:----:|:-------------:|:---------------:| |
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| 0.005 | 1 | 0.1922 | - | |
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| 0.25 | 50 | 0.0766 | - | |
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| 0.5 | 100 | 0.0081 | - | |
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| 0.75 | 150 | 0.0058 | - | |
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| 1.0 | 200 | 0.0005 | 0.0074 | |
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### Framework Versions |
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- Python: 3.11.10 |
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- SetFit: 1.1.2 |
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- Sentence Transformers: 5.0.0 |
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- Transformers: 4.49.0 |
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- PyTorch: 2.7.1 |
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- Datasets: 3.6.0 |
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- Tokenizers: 0.21.2 |
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## Citation |
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### BibTeX |
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```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
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doi = {10.48550/ARXIV.2209.11055}, |
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url = {https://arxiv.org/abs/2209.11055}, |
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Efficient Few-Shot Learning Without Prompts}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
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