Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use sentence-transformers/msmarco-bert-base-dot-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/msmarco-bert-base-dot-v5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/msmarco-bert-base-dot-v5") 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 sentence-transformers/msmarco-bert-base-dot-v5 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/msmarco-bert-base-dot-v5") model = AutoModel.from_pretrained("sentence-transformers/msmarco-bert-base-dot-v5") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 016123608ad164ded389ac87671a862d8889ae0ba26893f7221ec33cd126eaaf
- Size of remote file:
- 438 MB
- SHA256:
- dbce66b05653369175bf318af64513fa2bf95f57782b850a6fa7f36c1723fd3c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.