Sentence Similarity
sentence-transformers
Safetensors
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use OneFly7/biencoder_ep10_bs64_trans3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use OneFly7/biencoder_ep10_bs64_trans3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("OneFly7/biencoder_ep10_bs64_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_ep10_bs64_trans3 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("OneFly7/biencoder_ep10_bs64_trans3") model = AutoModel.from_pretrained("OneFly7/biencoder_ep10_bs64_trans3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b1eebf28d51b425919503bde32f76a54f1055eb4b3e3f6a03946174a9a2060d
- Size of remote file:
- 17.1 MB
- SHA256:
- cad551d5600a84242d0973327029452a1e3672ba6313c2a3c3d69c4310e12719
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