Sentence Similarity
sentence-transformers
PyTorch
Safetensors
bert
feature-extraction
text-embeddings-inference
Instructions to use NetherlandsForensicInstitute/ARM64BERT-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NetherlandsForensicInstitute/ARM64BERT-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NetherlandsForensicInstitute/ARM64BERT-embedding") 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] - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from NetherlandsForensicInstitute/ARM64BERT-embedding: direct link, hf CLI and curl.
- Browser
- Download file 351 MB
-
https://huggingface.co/NetherlandsForensicInstitute/ARM64BERT-embedding/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://NetherlandsForensicInstitute/ARM64BERT-embedding/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/NetherlandsForensicInstitute/ARM64BERT-embedding/resolve/main/pytorch_model.bin
351 MB
- Xet hash:
- bd9103bdb5a2ed354fe3bd3fe638d947727ae8be2d91bd60b5e0919b7c405d26
- Size of remote file:
- 351 MB
- SHA256:
- 4b4e5ccdcbfd19de031def7666334ecc8314ab2f6eec0d7a21d3a5402dde08fe
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