Instructions to use vgarg/my-fw9-identification-model-e5_large_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use vgarg/my-fw9-identification-model-e5_large_v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vgarg/my-fw9-identification-model-e5_large_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] - setfit
How to use vgarg/my-fw9-identification-model-e5_large_v2 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("vgarg/my-fw9-identification-model-e5_large_v2") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from vgarg/my-fw9-identification-model-e5_large_v2: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/vgarg/my-fw9-identification-model-e5_large_v2/resolve/main/tokenizer.json
- Command line
-
hf download hf://vgarg/my-fw9-identification-model-e5_large_v2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/vgarg/my-fw9-identification-model-e5_large_v2/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 212bfaaf7beffc70a3f2b8fbd9929f72e086ac0287cc39a5d98486b405c9209a
- Size of remote file:
- 17.1 MB
- SHA256:
- 46afe88da5fd71bdbab5cfab5e84c1adce59c246ea5f9341bbecef061891d0a7
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