--- tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: "Fido the Stamp is quite an innovative concept, blending pet ownership with\ \ personalized mailing. Many dog owners likely delight in the idea of featuring\ \ their pets' images on parcels. This personalization could make mailing feel\ \ more intimate and fun for both senders and recipients. However, one might wonder\ \ if everyone would want their pet's face plastered on every package they send.\ \ \n\nIt’s plausible that some users might find it amusing to enhance the mundane\ \ task of mailing with a dash of pet-inspired personality. Yet, others might feel\ \ that featuring their dog on a stamp diminishes the seriousness of the parcels\ \ they are sending. Fido the Stamp could also lead" - text: 'Policy statement revised; IV antibiotic therapy is not medically necessary for uncomplicated cranial nerve palsy associated with Lyme disease and antibiotic-refractory Lyme arthritis 7/19/2007: Reviewed and approved by MPAC 7/10/2009: Policy reviewed, no changes 12/15/2009: Coding Section revised with 2010 CPT4 and HCPCS revisions 02/23/2011: Added the following to the policy statement: Determination of levels of the B lymphocyte chemoattractant CXCL13 for diagnosis or monitoring treatment is considered investigational. No changes to other policy statements. Removed deleted HCPCS codes J0530, J0540, and J0550 from the Code Reference section. 02/24/2012: Add the following policy statement: A single 2- to 4-week course of IV antibiotics may be considered medically necessary in patients with Lyme carditis, as evidenced by positive serologic findings (defined above) and associated with a high degree of atrioventricular block or a PR interval of greater than 0.3 second. Documentation of Lyme carditis may include PCR-based direct detection of B burgdorferi in the blood when results of serologic studies are equivocal.' - text: The subdued July inflation figures indicate that consumer prices are not increasing rapidly, which usually boosts investor confidence in fixed-income securities like Treasuries. Likewise, soaring oil prices suggest that consumers may start cutting back on spending, leading to a cooling of the overall economy. This cooling effect can trigger a flight to safety among investors who prefer stable returns in uncertain times, causing an uptick in Treasury demand. Moreover, summer BBQs would probably be less popular if gas prices continue to rise, as people will likely prioritize their spending on essentials. However, a decrease in consumer spending could ironically lead to increased economic activity as consumers save money instead of spending it - text: 'POLICY HISTORY1/1994: Approved by Medical Policy Advisory Committee (MPAC) 5/1/2002: Type of Service and Place of Service deleted 3/25/2004: Reviewed by MPAC, Policy title “Lyme Disease Treatment” renamed “Intravenous Antiobiotic Therapy for Lyme Disease”, Description and Policy sections revised to be consistent with BCBSA policy # 5.01.08, intravenous antibiotic therapy changed from investigational to medically necessary for certain indications, investigation definition added, Sources updated, tables added to Code Reference section 5/5/2004: Code Reference section completed 3/13/2006: Policy reviewed, no changes 9/12/2006: Coding reviewed. ICD9 2006 revisions added to policy 11/13/2006: Code Reference section updated: CPT codes 87475, 87476, and 87477 deleted from policy 4/24/2007: Policy reviewed, policy statement rewritten for clarification 6/21/2007: Policy reviewed, description updated. Policy statement revised; IV antibiotic therapy is not medically necessary for uncomplicated cranial nerve palsy associated with Lyme disease and antibiotic-refractory Lyme arthritis 7/19/2007: Reviewed and approved by MPAC 7/10/2009: Policy reviewed, no changes 12/15/2009: Coding Section revised with 2010 CPT4 and HCPCS revisions 02/23/2011: Added the following to the policy statement: Determination of levels of the B lymphocyte chemoattractant CXCL13 for diagnosis or monitoring treatment is considered investigational. No changes to other policy statements. Removed deleted HCPCS codes J0530, J0540, and J0550 from the Code Reference section.' - text: Given the symptoms described, the most likely karyotype for this 15-year-old boy is 47,XXY, which is characteristic of Klinefelter syndrome. The combination of decreased facial and pubic hair, gynecomastia, small testes, long extremities, and tall stature aligns with this chromosomal pattern. Klinefelter syndrome is caused by the presence of an extra X chromosome, leading to the 47,XXY karyotype. metrics: - accuracy pipeline_tag: text-classification library_name: setfit inference: true base_model: sentence-transformers/paraphrase-mpnet-base-v2 model-index: - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 1.0 name: Accuracy --- # 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 ### 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 | |:---------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | positive | | | negative | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 1.0 | ## 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/code-y-v1") # Run inference preds = model("Given the symptoms described, the most likely karyotype for this 15-year-old boy is 47,XXY, which is characteristic of Klinefelter syndrome. The combination of decreased facial and pubic hair, gynecomastia, small testes, long extremities, and tall stature aligns with this chromosomal pattern. Klinefelter syndrome is caused by the presence of an extra X chromosome, leading to the 47,XXY karyotype.") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:----| | Word count | 52 | 148.16 | 266 | | Label | Training Sample Count | |:---------|:----------------------| | negative | 9 | | positive | 16 | ### Training Hyperparameters - batch_size: (16, 16) - num_epochs: (4, 4) - 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: True ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:------:|:----:|:-------------:|:---------------:| | 0.0435 | 1 | 0.1928 | - | | 1.0 | 23 | - | 0.0154 | | 2.0 | 46 | - | 0.0023 | | 2.1739 | 50 | 0.0214 | - | | 3.0 | 69 | - | 0.0018 | | 4.0 | 92 | - | 0.0015 | ### Framework Versions - Python: 3.11.10 - SetFit: 1.1.2 - Sentence Transformers: 4.0.2 - Transformers: 4.51.3 - PyTorch: 2.4.1+cu124 - 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} } ```