Instructions to use lambdaofgod/document_nbow_embedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use lambdaofgod/document_nbow_embedder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("lambdaofgod/document_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 modules.json from lambdaofgod/document_nbow_embedder: direct link, hf CLI and curl.
- Browser
- Download file 372 Bytes
-
https://huggingface.co/lambdaofgod/document_nbow_embedder/resolve/main/modules.json
- Command line
-
hf download hf://lambdaofgod/document_nbow_embedder/modules.json
-
curl -L -o modules.json https://huggingface.co/lambdaofgod/document_nbow_embedder/resolve/main/modules.json
372 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "0_WordEmbeddings", | |
| "type": "sentence_transformers.models.WordEmbeddings" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_WordWeights", | |
| "type": "sentence_transformers.models.WordWeights" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |