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
Download training_state.pth from tabularisai/Zip-1: direct link, hf CLI and curl.
- Browser
- Download file 2.58 kB
-
https://huggingface.co/tabularisai/Zip-1/resolve/main/training_state.pth
- Command line
-
hf download hf://tabularisai/Zip-1/training_state.pth
-
curl -L -o training_state.pth https://huggingface.co/tabularisai/Zip-1/resolve/main/training_state.pth
2.58 kB
- Xet hash:
- 2f507afed289c2fd5cf46e4382421d4173282ae6adfeb640a6b69d311b966b05
- Size of remote file:
- 2.58 kB
- SHA256:
- da40e0dea3f19b04228b37c4be914930e4240d58d39997397aaf31e334bb837e
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