Sentence Similarity
sentence-transformers
Safetensors
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use OneFly7/biencoder_ep10_bs64_trans1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use OneFly7/biencoder_ep10_bs64_trans1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("OneFly7/biencoder_ep10_bs64_trans1") 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_trans1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("OneFly7/biencoder_ep10_bs64_trans1") model = AutoModel.from_pretrained("OneFly7/biencoder_ep10_bs64_trans1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- feeba9cf087969f70889a4f2cd4895ab9cc20a18b3567479187897c58119d6f1
- Size of remote file:
- 1.11 GB
- SHA256:
- a4caa3e39096ac5a8d1b7c0c68a9a5be703b5ac64e31b344bcf52209bf06497f
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