Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use praisethefool/human_tech-fields-multilabelclassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use praisethefool/human_tech-fields-multilabelclassifier with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("praisethefool/human_tech-fields-multilabelclassifier") - sentence-transformers
How to use praisethefool/human_tech-fields-multilabelclassifier with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("praisethefool/human_tech-fields-multilabelclassifier") 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:
- 7f971d727ee49732790e9b80db3188324a280edf25d9b99c8cc4e9993518bb34
- Size of remote file:
- 438 MB
- SHA256:
- 2c841d37bb500bca1a5e80c130b1487c32bc1bdceb49e1d8ad20a07066049238
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