Sentence Similarity
sentence-transformers
Safetensors
English
multilingual
xlm-roberta
embedding
ror
affiliation-matching
contrastive-learning
text-embeddings-inference
Instructions to use cometadata/snowflake-arctic-ror-affiliations with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cometadata/snowflake-arctic-ror-affiliations with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cometadata/snowflake-arctic-ror-affiliations") 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 tokenizer.json from cometadata/snowflake-arctic-ror-affiliations: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/cometadata/snowflake-arctic-ror-affiliations/resolve/main/tokenizer.json
- Command line
-
hf download hf://cometadata/snowflake-arctic-ror-affiliations/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/cometadata/snowflake-arctic-ror-affiliations/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- fabce6d1ec5937042718271ab890f7ad71a561ea1f7d9365a71a42f4137092c2
- Size of remote file:
- 17.1 MB
- SHA256:
- b8cd58c3e5a595b5f311ecf453cd69a9468b128efe720b707a331b8f748fdd75
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