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:
- b68fec5f983503717275e121b9d3d93118f4f47f383d72047e2a0a7ef66523ed
- Size of remote file:
- 66 kB
- SHA256:
- 4fb8d2b8ffc343cdc33857ee31dae6069cde750a2c3a5504030b5bcede1df299
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.