Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use seongil-dn/unsupervised_4m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use seongil-dn/unsupervised_4m with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("seongil-dn/unsupervised_4m") 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 seongil-dn/unsupervised_4m: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/seongil-dn/unsupervised_4m/resolve/main/tokenizer.json
- Command line
-
hf download hf://seongil-dn/unsupervised_4m/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/seongil-dn/unsupervised_4m/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 610d14d3c1491ced8dc6919faea023c53e696a97633eb11d09cc249e74b9eda6
- Size of remote file:
- 17.1 MB
- SHA256:
- 249df0778f236f6ece390de0de746838ef25b9d6954b68c2ee71249e0a9d8fd4
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