Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
English
bert
feature-extraction
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use avsolatorio/GIST-small-Embedding-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use avsolatorio/GIST-small-Embedding-v0 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("avsolatorio/GIST-small-Embedding-v0") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
Download commit-info.json from avsolatorio/GIST-small-Embedding-v0: direct link, hf CLI and curl.
- Browser
- Download file 233 Bytes
-
https://huggingface.co/avsolatorio/GIST-small-Embedding-v0/resolve/main/commit-info.json
- Command line
-
hf download hf://avsolatorio/GIST-small-Embedding-v0/commit-info.json
-
curl -L -o commit-info.json https://huggingface.co/avsolatorio/GIST-small-Embedding-v0/resolve/main/commit-info.json
233 Bytes
| {"repo_id": "avsolatorio/01-100-11-1-2-2-0-0-cls-normed-384-512_GIST_BAAI_bge-small-en-v1.5-20240210215734-latest", "commit_message": "{\"loss\": 0.3166, \"learning_rate\": 3.3265738818312985e-06, \"epoch\": 2.66, \"step\": 302500}"} |