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") - 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
- Xet hash:
- 7c6ea87e3e52bb3f864db4c88be6cd2c0d66d17042d8cf403fb3b0642cf431f1
- Size of remote file:
- 14.8 MB
- SHA256:
- da145b5e7700ae40f16691ec32a0b1fdc1ee3298db22a31ea55f57a966c4a65d
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