Sentence Similarity
sentence-transformers
Safetensors
Transformers
Japanese
English
llama
feature-extraction
text-embeddings-inference
Instructions to use sbintuitions/sarashina-embedding-v1-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sbintuitions/sarashina-embedding-v1-1b with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sbintuitions/sarashina-embedding-v1-1b") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use sbintuitions/sarashina-embedding-v1-1b with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sbintuitions/sarashina-embedding-v1-1b") model = AutoModel.from_pretrained("sbintuitions/sarashina-embedding-v1-1b") - Notebooks
- Google Colab
- Kaggle
Update tokenizer_config.json
Browse files- tokenizer_config.json +1 -1
tokenizer_config.json
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@@ -158,7 +158,7 @@
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"keep_accents": true,
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"legacy": false,
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"mask_token": "<mask>",
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"model_max_length":
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"pad_token": "<pad>",
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"padding_side": "left",
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"sep_token": "<sep>",
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"keep_accents": true,
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"legacy": false,
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"mask_token": "<mask>",
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"model_max_length": 8192,
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"pad_token": "<pad>",
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"padding_side": "left",
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"sep_token": "<sep>",
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