Instructions to use oneryalcin/finepdfs-purpose-setfit-neomme-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use oneryalcin/finepdfs-purpose-setfit-neomme-v2 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("oneryalcin/finepdfs-purpose-setfit-neomme-v2") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use oneryalcin/finepdfs-purpose-setfit-neomme-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("oneryalcin/finepdfs-purpose-setfit-neomme-v2") 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 1_Pooling/config.json from oneryalcin/finepdfs-purpose-setfit-neomme-v2: direct link, hf CLI and curl.
- Browser
- Download file 91 Bytes
-
https://huggingface.co/oneryalcin/finepdfs-purpose-setfit-neomme-v2/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://oneryalcin/finepdfs-purpose-setfit-neomme-v2/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/oneryalcin/finepdfs-purpose-setfit-neomme-v2/resolve/main/1_Pooling/config.json
91 Bytes
| { | |
| "embedding_dimension": 1024, | |
| "pooling_mode": "mean", | |
| "include_prompt": true | |
| } |