| --- |
| base_model: cambridgeltl/SapBERT-from-PubMedBERT-fulltext |
| library_name: setfit |
| metrics: |
| - accuracy |
| pipeline_tag: text-classification |
| tags: |
| - setfit |
| - sentence-transformers |
| - text-classification |
| - generated_from_setfit_trainer |
| widget: |
| - text: exorphins |
| - text: phosphatidylethanolamines |
| - text: lipopolysaccharides |
| - text: ion channels |
| - text: caspases |
| inference: false |
| model-index: |
| - name: SetFit with cambridgeltl/SapBERT-from-PubMedBERT-fulltext |
| results: |
| - task: |
| type: text-classification |
| name: Text Classification |
| dataset: |
| name: Unknown |
| type: unknown |
| split: test |
| metrics: |
| - type: accuracy |
| value: 0.17570754716981132 |
| name: Accuracy |
| --- |
| |
| # SetFit with cambridgeltl/SapBERT-from-PubMedBERT-fulltext |
|
|
| This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [cambridgeltl/SapBERT-from-PubMedBERT-fulltext](https://huggingface.co/cambridgeltl/SapBERT-from-PubMedBERT-fulltext) as the Sentence Transformer embedding model. A MultiOutputClassifier 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:** [cambridgeltl/SapBERT-from-PubMedBERT-fulltext](https://huggingface.co/cambridgeltl/SapBERT-from-PubMedBERT-fulltext) |
| - **Classification head:** a MultiOutputClassifier instance |
| - **Maximum Sequence Length:** 512 tokens |
| <!-- - **Number of Classes:** Unknown --> |
| <!-- - **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) |
|
|
| ## Evaluation |
|
|
| ### Metrics |
| | Label | Accuracy | |
| |:--------|:---------| |
| | **all** | 0.1757 | |
|
|
| ## 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("setfit_model_id") |
| # Run inference |
| preds = model("caspases") |
| ``` |
|
|
| <!-- |
| ### 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 | 1 | 1.7652 | 5 | |
|
|
| ### Training Hyperparameters |
| - batch_size: (16, 16) |
| - num_epochs: (3, 3) |
| - max_steps: -1 |
| - sampling_strategy: oversampling |
| - num_iterations: 15 |
| - body_learning_rate: (2e-05, 2e-05) |
| - head_learning_rate: 2e-05 |
| - 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 | |
| |:------:|:----:|:-------------:|:---------------:| |
| | 0.0009 | 1 | 0.2361 | - | |
| | 0.0463 | 50 | 0.2377 | - | |
| | 0.0927 | 100 | 0.2269 | - | |
| | 0.1390 | 150 | 0.2104 | - | |
| | 0.1854 | 200 | 0.1871 | - | |
| | 0.2317 | 250 | 0.1437 | - | |
| | 0.2780 | 300 | 0.1322 | - | |
| | 0.3244 | 350 | 0.1365 | - | |
| | 0.3707 | 400 | 0.1155 | - | |
| | 0.4171 | 450 | 0.1144 | - | |
| | 0.4634 | 500 | 0.1068 | - | |
| | 0.5097 | 550 | 0.1011 | - | |
| | 0.5561 | 600 | 0.095 | - | |
| | 0.6024 | 650 | 0.0933 | - | |
| | 0.6487 | 700 | 0.1063 | - | |
| | 0.6951 | 750 | 0.0999 | - | |
| | 0.7414 | 800 | 0.0823 | - | |
| | 0.7878 | 850 | 0.0877 | - | |
| | 0.8341 | 900 | 0.0767 | - | |
| | 0.8804 | 950 | 0.0849 | - | |
| | 0.9268 | 1000 | 0.0796 | - | |
| | 0.9731 | 1050 | 0.0877 | - | |
| | 1.0195 | 1100 | 0.0759 | - | |
| | 1.0658 | 1150 | 0.0705 | - | |
| | 1.1121 | 1200 | 0.0728 | - | |
| | 1.1585 | 1250 | 0.0738 | - | |
| | 1.2048 | 1300 | 0.0767 | - | |
| | 1.2512 | 1350 | 0.0692 | - | |
| | 1.2975 | 1400 | 0.0697 | - | |
| | 1.3438 | 1450 | 0.0639 | - | |
| | 1.3902 | 1500 | 0.0729 | - | |
| | 1.4365 | 1550 | 0.0759 | - | |
| | 1.4829 | 1600 | 0.0786 | - | |
| | 1.5292 | 1650 | 0.0618 | - | |
| | 1.5755 | 1700 | 0.0722 | - | |
| | 1.6219 | 1750 | 0.0719 | - | |
| | 1.6682 | 1800 | 0.072 | - | |
| | 1.7146 | 1850 | 0.0654 | - | |
| | 1.7609 | 1900 | 0.0683 | - | |
| | 1.8072 | 1950 | 0.0654 | - | |
| | 1.8536 | 2000 | 0.0679 | - | |
| | 1.8999 | 2050 | 0.0643 | - | |
| | 1.9462 | 2100 | 0.0662 | - | |
| | 1.9926 | 2150 | 0.0642 | - | |
| | 2.0389 | 2200 | 0.0812 | - | |
| | 2.0853 | 2250 | 0.068 | - | |
| | 2.1316 | 2300 | 0.0583 | - | |
| | 2.1779 | 2350 | 0.0627 | - | |
| | 2.2243 | 2400 | 0.0654 | - | |
| | 2.2706 | 2450 | 0.0571 | - | |
| | 2.3170 | 2500 | 0.0623 | - | |
| | 2.3633 | 2550 | 0.0639 | - | |
| | 2.4096 | 2600 | 0.059 | - | |
| | 2.4560 | 2650 | 0.0637 | - | |
| | 2.5023 | 2700 | 0.0675 | - | |
| | 2.5487 | 2750 | 0.0696 | - | |
| | 2.5950 | 2800 | 0.0669 | - | |
| | 2.6413 | 2850 | 0.0633 | - | |
| | 2.6877 | 2900 | 0.0606 | - | |
| | 2.7340 | 2950 | 0.0609 | - | |
| | 2.7804 | 3000 | 0.054 | - | |
| | 2.8267 | 3050 | 0.0598 | - | |
| | 2.8730 | 3100 | 0.0597 | - | |
| | 2.9194 | 3150 | 0.0618 | - | |
| | 2.9657 | 3200 | 0.065 | - | |
|
|
| ### Framework Versions |
| - Python: 3.10.12 |
| - SetFit: 1.1.0 |
| - Sentence Transformers: 3.1.1 |
| - Transformers: 4.39.0 |
| - PyTorch: 2.4.1+cu121 |
| - Datasets: 3.0.0 |
| - Tokenizers: 0.15.2 |
|
|
| ## 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.* |
| --> |
|
|
| <!-- |
| ## Model Card Contact |
|
|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| --> |
