Instructions to use BeToast/xml_xnli__inclusiveORexclusive__binary_classification__frenchANDenglish with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BeToast/xml_xnli__inclusiveORexclusive__binary_classification__frenchANDenglish with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BeToast/xml_xnli__inclusiveORexclusive__binary_classification__frenchANDenglish") 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 BeToast/xml_xnli__inclusiveORexclusive__binary_classification__frenchANDenglish with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("BeToast/xml_xnli__inclusiveORexclusive__binary_classification__frenchANDenglish") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
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by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:93d2b25cdb8947febadb599c58bfbfa8e5448d371989ebde37f9e580b30b0c91
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size 1112201288
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