Sentence Similarity
sentence-transformers
Transformers
feature-extraction
mteb
Eval Results (legacy)
Instructions to use jncraton/gte-tiny-ct2-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jncraton/gte-tiny-ct2-int8 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jncraton/gte-tiny-ct2-int8") 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] - Transformers
How to use jncraton/gte-tiny-ct2-int8 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jncraton/gte-tiny-ct2-int8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.bin from jncraton/gte-tiny-ct2-int8: direct link, hf CLI and curl.
- Browser
- Download file 23.2 MB
-
https://huggingface.co/jncraton/gte-tiny-ct2-int8/resolve/main/model.bin
- Command line
-
hf download hf://jncraton/gte-tiny-ct2-int8/model.bin
-
curl -L -o model.bin https://huggingface.co/jncraton/gte-tiny-ct2-int8/resolve/main/model.bin
23.2 MB
- Xet hash:
- 7a9fa34a2c45360887f1491dd5c1f7a0f02b1f966ad9762edcc3c31274be2aa4
- Size of remote file:
- 23.2 MB
- SHA256:
- 0ac0847713eb055f012126fa39f2bda1047451aca0794946f341c3c084ef7c14
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