library_name | tags | metrics | widget | pipeline_tag | inference | base_model | model-index | ||||||||||||||||||||||||||||||||||
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setfit |
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text-classification |
true |
sentence-transformers/paraphrase-mpnet-base-v2 |
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This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
- Model Type: SetFit
- Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Label | Accuracy |
---|---|
all | 0.9444 |
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import SetFitModel
# Download from the ๐ค Hub
model = SetFitModel.from_pretrained("setfit_model_id")
# Run inference
preds = model("I loved the spiderman movie!")
- Python: 3.10.11
- SetFit: 1.0.3
- Sentence Transformers: 2.2.2
- Transformers: 4.37.1
- PyTorch: 2.1.2+cpu
- Datasets: 2.15.0
- Tokenizers: 0.15.0
@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}
}