Sentence Similarity
sentence-transformers
Safetensors
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use OneFly7/biencoder_ep10_bs64_all with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use OneFly7/biencoder_ep10_bs64_all with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("OneFly7/biencoder_ep10_bs64_all") 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_all with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("OneFly7/biencoder_ep10_bs64_all") model = AutoModel.from_pretrained("OneFly7/biencoder_ep10_bs64_all", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8798f9a6aa3e93970577dd82af127122a5cb31d30789ba8290b39d8108188962
- Size of remote file:
- 1.11 GB
- SHA256:
- e396525eaa7288b9bf586e336c756a7d3d9936badf4dcb59d815f77585e46abc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.