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
File size: 447 Bytes
dfd3f5e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
"backend": "tokenizers",
"clean_up_tokenization_spaces": true,
"cls_token": "[CLS]",
"do_basic_tokenize": false,
"do_lower_case": false,
"is_local": false,
"local_files_only": false,
"mask_token": "[MASK]",
"model_max_length": 512,
"never_split": null,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"strip_accents": null,
"tokenize_chinese_chars": false,
"tokenizer_class": "ARM64Tokenizer",
"unk_token": "[UNK]"
}
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