Instructions to use SouhilOuchene/text_conso with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use SouhilOuchene/text_conso with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SouhilOuchene/text_conso") 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 SouhilOuchene/text_conso with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("SouhilOuchene/text_conso") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from SouhilOuchene/text_conso: direct link, hf CLI and curl.
- Browser
- Download file 2.42 MB
-
https://huggingface.co/SouhilOuchene/text_conso/resolve/main/tokenizer.json
- Command line
-
hf download hf://SouhilOuchene/text_conso/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/SouhilOuchene/text_conso/resolve/main/tokenizer.json
2.42 MB
- Xet hash:
- 498b4f7109664dd798c3632bccd5ca3c54b0275f6bf67d1ee99910b5e56d3402
- Size of remote file:
- 2.42 MB
- SHA256:
- da4a19b41f008689418c2e004192b6eeca0292138a0ef0c775d7dfaf70584a0c
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