sentence-transformers
Safetensors
multilingual
xlm-roberta
claim2vec
embedding-model
fact-checking
claim-clustering
semantic-search
misinformation
contrastive-learning
multilingual-nlp
Instructions to use Rrubaa/claim2vec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Rrubaa/claim2vec with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Rrubaa/claim2vec") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Rrubaa/claim2vec: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/Rrubaa/claim2vec/resolve/main/tokenizer.json
- Command line
-
hf download hf://Rrubaa/claim2vec/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Rrubaa/claim2vec/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- f1e2d0633986c69eef4b87e00f5dd8956c23702b2ab002db0b14ab3bbd71ae06
- Size of remote file:
- 17.1 MB
- SHA256:
- d9a6af42442a3e3e9f05f618eae0bb2d98ca4f6a6406cb80ef7a4fa865204d61
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