Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use faodl/model_g20_multilabel_MiniLM-L12-v_final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use faodl/model_g20_multilabel_MiniLM-L12-v_final with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("faodl/model_g20_multilabel_MiniLM-L12-v_final") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use faodl/model_g20_multilabel_MiniLM-L12-v_final with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("faodl/model_g20_multilabel_MiniLM-L12-v_final") 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 tokenizer.json from faodl/model_g20_multilabel_MiniLM-L12-v_final: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/faodl/model_g20_multilabel_MiniLM-L12-v_final/resolve/main/tokenizer.json
- Command line
-
hf download hf://faodl/model_g20_multilabel_MiniLM-L12-v_final/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/faodl/model_g20_multilabel_MiniLM-L12-v_final/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 5b1eebf28d51b425919503bde32f76a54f1055eb4b3e3f6a03946174a9a2060d
- Size of remote file:
- 17.1 MB
- SHA256:
- cad551d5600a84242d0973327029452a1e3672ba6313c2a3c3d69c4310e12719
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