Instructions to use pt-mteb/average_pt_nilc_glove_s1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use pt-mteb/average_pt_nilc_glove_s1000 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("pt-mteb/average_pt_nilc_glove_s1000") 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 0_WordEmbeddings/pytorch_model.bin from pt-mteb/average_pt_nilc_glove_s1000: direct link, hf CLI and curl.
- Browser
- Download file 3.72 GB
-
https://huggingface.co/pt-mteb/average_pt_nilc_glove_s1000/resolve/main/0_WordEmbeddings/pytorch_model.bin
- Command line
-
hf download hf://pt-mteb/average_pt_nilc_glove_s1000/0_WordEmbeddings/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/pt-mteb/average_pt_nilc_glove_s1000/resolve/main/0_WordEmbeddings/pytorch_model.bin
3.72 GB
- Xet hash:
- cd61a7d11085dab2e82f6e3b531728806e65b0647486752a44d4f9dff6c3110f
- Size of remote file:
- 3.72 GB
- SHA256:
- 5941f353fb9cb2a8bdf138ce5aac238a5685f90e1dda3e7381e76f920e9a921e
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