Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use NetworkIsLife/SciBert_sentence_transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NetworkIsLife/SciBert_sentence_transformer with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NetworkIsLife/SciBert_sentence_transformer") 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] - Transformers
How to use NetworkIsLife/SciBert_sentence_transformer with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("NetworkIsLife/SciBert_sentence_transformer") model = AutoModel.from_pretrained("NetworkIsLife/SciBert_sentence_transformer") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2d4219bdce1c3c238835a0f66530d3f8ed3fc768372355a3fc9764bf41314c93
- Size of remote file:
- 440 MB
- SHA256:
- d8dc7ae9ddc91a95866ecae33863ea9e4c8a683ce3c717f112de7f11a1fe3d45
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.