Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use adalbertojunior/db-msm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use adalbertojunior/db-msm with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("adalbertojunior/db-msm") 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 adalbertojunior/db-msm with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("adalbertojunior/db-msm") model = AutoModel.from_pretrained("adalbertojunior/db-msm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from adalbertojunior/db-msm: direct link, hf CLI and curl.
- Browser
- Download file 266 MB
-
https://huggingface.co/adalbertojunior/db-msm/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://adalbertojunior/db-msm/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/adalbertojunior/db-msm/resolve/main/pytorch_model.bin
266 MB
- Xet hash:
- bcb02c220503ac4c40a6ba9796f7dcdbe3c5221e8b026c467e43dd15fb853545
- Size of remote file:
- 266 MB
- SHA256:
- 1f2684fcf1dd2836d9d33d136f5c060e50553b9dccdbf42828f5d3f693a225bb
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