Instructions to use bachbouch/setfit-ethos-multilabel-example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use bachbouch/setfit-ethos-multilabel-example with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("bachbouch/setfit-ethos-multilabel-example") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - setfit
How to use bachbouch/setfit-ethos-multilabel-example with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("bachbouch/setfit-ethos-multilabel-example") - Notebooks
- Google Colab
- Kaggle
Sam Bouguerra commited on
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README.md
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# samYahoo/setfit-ethos-multilabel-example
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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) with contrastive learning.
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# samYahoo/setfit-ethos-multilabel-example
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https://colab.research.google.com/github/huggingface/setfit/blob/main/notebooks/text-classification_multilabel.ipynb#scrollTo=552ec552
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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) with contrastive learning.
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