Sentence Similarity
sentence-transformers
Safetensors
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use OneFly7/biencoder_ep2_bs32_trans3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use OneFly7/biencoder_ep2_bs32_trans3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("OneFly7/biencoder_ep2_bs32_trans3") 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] - Transformers
How to use OneFly7/biencoder_ep2_bs32_trans3 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("OneFly7/biencoder_ep2_bs32_trans3") model = AutoModel.from_pretrained("OneFly7/biencoder_ep2_bs32_trans3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 322d06222b8a0ab278770e6ca82c75d19fcaf2423f583cff4597fc1e8dd8ea3d
- Size of remote file:
- 1.11 GB
- SHA256:
- 71639e83d0e9908070d7ec8b59b6b40f67c09a8234f9b3234398e6e24072bd60
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