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 added_tokens.json from NetherlandsForensicInstitute/ARM64BERT-embedding: direct link, hf CLI and curl.
- Browser
- Download file 96 Bytes
-
https://huggingface.co/NetherlandsForensicInstitute/ARM64BERT-embedding/resolve/main/added_tokens.json
- Command line
-
hf download hf://NetherlandsForensicInstitute/ARM64BERT-embedding/added_tokens.json
-
curl -L -o added_tokens.json https://huggingface.co/NetherlandsForensicInstitute/ARM64BERT-embedding/resolve/main/added_tokens.json
96 Bytes
| { | |
| "[CLS]": 2754, | |
| "[MASK]": 2755, | |
| "[PAD]": 2753, | |
| "[SEP]": 2752, | |
| "[UNK]": 2751 | |
| } | |