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
| { | |
| "word_embedding_dimension": 1024, | |
| "pooling_mode_cls_token": true, | |
| "pooling_mode_mean_tokens": false, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |