Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
retrieval
bge
beir
vstash
mnrl
fine-tuned
text-embeddings-inference
Instructions to use Stffens/bge-small-rrf-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Stffens/bge-small-rrf-v3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Stffens/bge-small-rrf-v3") 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
add tokenizer_config.json
Browse files- tokenizer_config.json +17 -0
tokenizer_config.json
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{
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"backend": "tokenizers",
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"clean_up_tokenization_spaces": true,
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"is_local": false,
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"mask_token": "[MASK]",
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"model_max_length": 256,
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"never_split": null,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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