Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:2733
loss:TripletLoss
text-embeddings-inference
Instructions to use samanvitha7/semeval2026-bge_large-bge-large-all-bge_expanded-checkpoints-final_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use samanvitha7/semeval2026-bge_large-bge-large-all-bge_expanded-checkpoints-final_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("samanvitha7/semeval2026-bge_large-bge-large-all-bge_expanded-checkpoints-final_model") 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
Upload model from C:\Users\Lenovo\SemEval2026-task4\bge_large\bge-large-all\bge_expanded\checkpoints\final_model
b0d78dc verified - Xet hash:
- 95afbefdd18b4a65eb20de327a948d61a3bd09dc357d5e1af929241394b9da8a
- Size of remote file:
- 358 MB
- SHA256:
- 27f46a08374a161b92eb699c69e83072907866f72358372b5af7389324957135
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