Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Maxim01/Intent_Classification_Test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use Maxim01/Intent_Classification_Test with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("Maxim01/Intent_Classification_Test") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use Maxim01/Intent_Classification_Test with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Maxim01/Intent_Classification_Test") 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] - Notebooks
- Google Colab
- Kaggle
Download unigram.json from Maxim01/Intent_Classification_Test: direct link, hf CLI and curl.
- Browser
- Download file 14.8 MB
-
https://huggingface.co/Maxim01/Intent_Classification_Test/resolve/main/unigram.json
- Command line
-
hf download hf://Maxim01/Intent_Classification_Test/unigram.json
-
curl -L -o unigram.json https://huggingface.co/Maxim01/Intent_Classification_Test/resolve/main/unigram.json
14.8 MB
- Xet hash:
- 7c6ea87e3e52bb3f864db4c88be6cd2c0d66d17042d8cf403fb3b0642cf431f1
- Size of remote file:
- 14.8 MB
- SHA256:
- da145b5e7700ae40f16691ec32a0b1fdc1ee3298db22a31ea55f57a966c4a65d
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