Instructions to use JohanHeinsen/PE_header_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use JohanHeinsen/PE_header_classifier with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("JohanHeinsen/PE_header_classifier") 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] - setfit
How to use JohanHeinsen/PE_header_classifier with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("JohanHeinsen/PE_header_classifier") - Notebooks
- Google Colab
- Kaggle
Download config_sentence_transformers.json from JohanHeinsen/PE_header_classifier: direct link, hf CLI and curl.
- Browser
- Download file 199 Bytes
-
https://huggingface.co/JohanHeinsen/PE_header_classifier/resolve/main/config_sentence_transformers.json
- Command line
-
hf download hf://JohanHeinsen/PE_header_classifier/config_sentence_transformers.json
-
curl -L -o config_sentence_transformers.json https://huggingface.co/JohanHeinsen/PE_header_classifier/resolve/main/config_sentence_transformers.json
199 Bytes
| { | |
| "__version__": { | |
| "sentence_transformers": "4.1.0", | |
| "transformers": "4.51.3", | |
| "pytorch": "2.7.0" | |
| }, | |
| "prompts": {}, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine" | |
| } |