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
config_sentence_transformers.json
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{
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"__version__": {
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"sentence_transformers": "
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"transformers": "
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"pytorch": "2.
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"prompts": {},
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"default_prompt_name": null,
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"similarity_fn_name": "cosine"
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}
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{
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"__version__": {
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"sentence_transformers": "5.3.0",
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"transformers": "5.3.0",
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"pytorch": "2.8.0+cu128"
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},
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"prompts": {
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"query": "",
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"document": ""
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"default_prompt_name": null,
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"similarity_fn_name": "cosine",
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"model_type": "SentenceTransformer"
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}
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