Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use PulseWave/PAYMENT-TERMS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use PulseWave/PAYMENT-TERMS with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("PulseWave/PAYMENT-TERMS") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use PulseWave/PAYMENT-TERMS with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("PulseWave/PAYMENT-TERMS") 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 model_head.pkl from PulseWave/PAYMENT-TERMS: direct link, hf CLI and curl.
- Browser
- Download file 7.01 kB
-
https://huggingface.co/PulseWave/PAYMENT-TERMS/resolve/main/model_head.pkl
- Command line
-
hf download hf://PulseWave/PAYMENT-TERMS/model_head.pkl
-
curl -L -o model_head.pkl https://huggingface.co/PulseWave/PAYMENT-TERMS/resolve/main/model_head.pkl
7.01 kB
- Xet hash:
- 1f48af884547e8de041e9b0ee28847a04ec578067b672f37b60c6fdcb8e663c3
- Size of remote file:
- 7.01 kB
- SHA256:
- 39c1653da166b28f93cb8441f00ad98df3aca459de8ae656fcb6078734578a80
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.