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
- Xet hash:
- d73f3027a830d4c7cef11ed2647cdf7e410b3149a2ffb417279441b345a82ad0
- Size of remote file:
- 2.27 GB
- SHA256:
- 0fd6e24a68c2ab38d47e1767d13c291807f886fd0be7b0f4dce52c728e632b71
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