Instructions to use lambdaofgod/paperswithcode_word2vec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use lambdaofgod/paperswithcode_word2vec with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("lambdaofgod/paperswithcode_word2vec") 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/wordembedding_config.json from lambdaofgod/paperswithcode_word2vec: direct link, hf CLI and curl.
- Browser
- Download file 164 Bytes
-
https://huggingface.co/lambdaofgod/paperswithcode_word2vec/resolve/main/0_WordEmbeddings/wordembedding_config.json
- Command line
-
hf download hf://lambdaofgod/paperswithcode_word2vec/0_WordEmbeddings/wordembedding_config.json
-
curl -L -o wordembedding_config.json https://huggingface.co/lambdaofgod/paperswithcode_word2vec/resolve/main/0_WordEmbeddings/wordembedding_config.json
164 Bytes
| { | |
| "tokenizer_class": "sentence_transformers.models.tokenizer.WhitespaceTokenizer.WhitespaceTokenizer", | |
| "update_embeddings": false, | |
| "max_seq_length": 1000000 | |
| } |