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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: Although traditional database search methods can effectively identify peptide |
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matches, this approach correlates tandem mass spectral data with amino acid sequences |
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in a protein database 'however' providing additional confirmation and improving |
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identification accuracy. |
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- text: The study involved 30 smallholder farmers from three different regions in |
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Africa, each with an average farm size of 1.5 hectares and an annual income from |
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farming of approximately $1,500. |
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- text: This study aimed to evaluate the efficacy and safety of interferon α2b plus |
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ribavirin for 48 weeks or 24 weeks compared to interferon α2b plus placebo for |
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48 weeks in the treatment of chronic hepatitis C virus infection. |
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- text: The study reported that 73% of the psychotherapists endorsed the use of cognitive |
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techniques in their treatment of eating disorders, while 61% reported using behavioral |
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techniques. |
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- text: Previous research on the psychoanalytic concept of the working alliance has |
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established its significance in therapeutic change and identified key components |
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such as the bond between therapist and client and the agreement on therapeutic |
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goals. |
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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: sentence-transformers/all-MiniLM-L6-v2 |
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model-index: |
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- name: SetFit with sentence-transformers/all-MiniLM-L6-v2 |
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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: 0.9498398588143016 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/all-MiniLM-L6-v2 |
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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 [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. |
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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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2) |
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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:** 256 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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| 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> | |
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| 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> | |
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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** | 0.9498 | |
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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("Corran/SciGenSetfit24Binary") |
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# Run inference |
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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.") |
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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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### 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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## 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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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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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 | 8 | 29.6038 | 60 | |
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| Label | Training Sample Count | |
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|:------------|:----------------------| |
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| Misc | 2500 | |
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| Uncertainty | 2500 | |
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### Training Hyperparameters |
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- batch_size: (300, 300) |
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- num_epochs: (1, 1) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 5 |
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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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| 0.0060 | 1 | 0.4529 | - | |
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| 0.2994 | 50 | 0.3104 | - | |
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| 0.5988 | 100 | 0.2514 | - | |
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| 0.8982 | 150 | 0.25 | - | |
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| 1.0 | 167 | - | 0.2479 | |
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| 0.0060 | 1 | 0.2406 | - | |
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| 0.2994 | 50 | 0.1576 | - | |
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| 0.5988 | 100 | 0.0912 | - | |
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| 0.8982 | 150 | 0.0656 | - | |
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| 1.0 | 167 | - | 0.0683 | |
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| 0.0060 | 1 | 0.0827 | - | |
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| 0.2994 | 50 | 0.0581 | - | |
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| 0.5988 | 100 | 0.0393 | - | |
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| 0.8982 | 150 | 0.0339 | - | |
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| 1.0 | 167 | - | 0.0516 | |
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### Framework Versions |
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- Python: 3.10.12 |
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- SetFit: 1.2.0.dev0 |
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- Sentence Transformers: 3.1.1 |
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- Transformers: 4.42.2 |
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- PyTorch: 2.5.1+cu121 |
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- Datasets: 3.2.0 |
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- Tokenizers: 0.19.1 |
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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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