Sentence Similarity
sentence-transformers
PyTorch
Transformers
English
bert
fill-mask
information retrieval
ir
documents retrieval
passage retrieval
beir
benchmark
sts
semantic search
feature-extraction
custom_code
text-embeddings-inference
Instructions to use shreyansh26/bert-base-1024-biencoder-64M-pairs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use shreyansh26/bert-base-1024-biencoder-64M-pairs with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("shreyansh26/bert-base-1024-biencoder-64M-pairs", trust_remote_code=True) 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 shreyansh26/bert-base-1024-biencoder-64M-pairs with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("shreyansh26/bert-base-1024-biencoder-64M-pairs", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("shreyansh26/bert-base-1024-biencoder-64M-pairs", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b4312478c0ea95a966cfa2e98e7b9ce934919243ad4511e3747a529935626e1f
- Size of remote file:
- 550 MB
- SHA256:
- 8618eb7144e3c902857e5e24a5c3afb04a6175c95eb9464415e6ec2bd881df33
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.