Instructions to use lambdaofgod/query_nbow_embedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use lambdaofgod/query_nbow_embedder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("lambdaofgod/query_nbow_embedder") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download 0_WordEmbeddings/tokenize_fn.pkl from lambdaofgod/query_nbow_embedder: direct link, hf CLI and curl.
- Browser
- Download file 237 Bytes
-
https://huggingface.co/lambdaofgod/query_nbow_embedder/resolve/main/0_WordEmbeddings/tokenize_fn.pkl
- Command line
-
hf download hf://lambdaofgod/query_nbow_embedder/0_WordEmbeddings/tokenize_fn.pkl
-
curl -L -o tokenize_fn.pkl https://huggingface.co/lambdaofgod/query_nbow_embedder/resolve/main/0_WordEmbeddings/tokenize_fn.pkl
237 Bytes
- Xet hash:
- 16df9a918470bbb43b0110caae2260db22bb86c418d191f937c68c71282814d7
- Size of remote file:
- 237 Bytes
- SHA256:
- 46f96ac7225a91561d0034a80a6b46bb0ea327e0a19ae32a82db5e0fe9428c34
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