Instructions to use davanstrien/dataset_mentions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use davanstrien/dataset_mentions with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("davanstrien/dataset_mentions") 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 davanstrien/dataset_mentions with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("davanstrien/dataset_mentions") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from davanstrien/dataset_mentions: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/davanstrien/dataset_mentions/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://davanstrien/dataset_mentions/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/davanstrien/dataset_mentions/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- b550e7d41b21a0847f64953ab50648e30f2be86cdb550c5675c02ef70dfa48be
- Size of remote file:
- 438 MB
- SHA256:
- cf30a40f9392d7003ac14b675d8d7802508f861635a84978950a6e539527bf88
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