Instructions to use vgarg/context_flag_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use vgarg/context_flag_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vgarg/context_flag_model") 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 vgarg/context_flag_model with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("vgarg/context_flag_model") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - Notebooks
- Google Colab
- Kaggle
Download config_sentence_transformers.json from vgarg/context_flag_model: direct link, hf CLI and curl.
- Browser
- Download file 170 Bytes
-
https://huggingface.co/vgarg/context_flag_model/resolve/main/config_sentence_transformers.json
- Command line
-
hf download hf://vgarg/context_flag_model/config_sentence_transformers.json
-
curl -L -o config_sentence_transformers.json https://huggingface.co/vgarg/context_flag_model/resolve/main/config_sentence_transformers.json
170 Bytes
| { | |
| "__version__": { | |
| "sentence_transformers": "2.0.0", | |
| "transformers": "4.7.0", | |
| "pytorch": "1.9.0+cu102" | |
| }, | |
| "prompts": {}, | |
| "default_prompt_name": null | |
| } |