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--- |
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license: apache-2.0 |
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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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pipeline_tag: text-classification |
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--- |
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# moshew/bge-small-en-v1.5_setfit-sst2-english |
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This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. 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) ("BAAI/bge-small-en-v1.5") with contrastive learning. |
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2. Training a classification head with features from the fine-tuned Sentence Transformer. |
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## Training code |
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```python |
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from setfit import SetFitModel |
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from datasets import load_dataset |
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from setfit import SetFitModel, SetFitTrainer |
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# Load a dataset from the Hugging Face Hub |
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dataset = load_dataset("SetFit/sst2") |
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# Upload Train and Test data |
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num_classes = 2 |
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test_ds = dataset["test"] |
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train_ds = dataset["train"] |
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model = SetFitModel.from_pretrained("BAAI/bge-small-en-v1.5") |
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trainer = SetFitTrainer(model=model, train_dataset=train_ds, eval_dataset=test_ds) |
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# Train and evaluate |
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trainer.train() |
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trainer.evaluate()['accuracy'] |
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``` |
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## Usage |
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To use this model for inference, first install the SetFit library: |
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```bash |
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python -m pip install setfit |
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``` |
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You can then run inference as follows: |
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```python |
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from setfit import SetFitModel |
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# Download from Hub and run inference |
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model = SetFitModel.from_pretrained("moshew/bge-small-en-v1.5_setfit-sst2-english") |
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# Run inference |
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preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) |
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``` |
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## Accuracy |
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On SST-2 dev set: |
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91.4% SetFit |
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88.4% (no Fine-Tuning) |
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## BibTeX entry and citation info |
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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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