Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
tiny
Eval Results (legacy)
text-embeddings-inference
Instructions to use tabularisai/Zip-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tabularisai/Zip-1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tabularisai/Zip-1") 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
Update model weights (v70 soup20, quick MTEB 63.97)
Browse files- 1_Pooling/config.json +8 -8
1_Pooling/config.json
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{
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"word_embedding_dimension": 32,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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"include_prompt": true
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}
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