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 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 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("samanvitha7/semeval2026-bge_large-bge-large-all-bge_expanded-checkpoints") 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
2b55b9c verified - Xet hash:
- 886b930e2eaa19bf7879e5d4f229ffe588e27d999c7127c0ec74c4cc70ae735c
- Size of remote file:
- 359 MB
- SHA256:
- b0510f8ed7fce73b4c2f522795a2bb90f7875095b6572129161ad3f50cef0cef
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.