zeroshot/gte-small-dense
By zeroshot · ↓ 0 · ♥ 0 · Updated 2023-10-12
What's in this repo
This page lists the files in zeroshot/gte-small-dense, including model.onnx.
- 1 ONNX models (.onnx files)
- 9 files, including 5 .json, 1 .md, 1 .onnx, 1 .txt
- Last updated 2023-10-12
ONNX files
| File | Size | Download |
|---|---|---|
| model.onnx | — | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
This is the ONNX variant of the gte-small embeddings model created with the DeepSparse Optimum integration.
To replicate ONNX export, run:
pip install git+https://github.com/neuralmagic/optimum-deepsparse.git
from optimum.deepsparse import DeepSparseModelForFeatureExtraction
from transformers.onnx.utils import get_preprocessor
from pathlib import Path
model_id = "thenlper/gte-small"
# load model and convert to onnx
model = DeepSparseModelForFeatureExtraction.from_pretrained(model_id, export=True)
tokenizer = get_preprocessor(model_id)
# save onnx checkpoint and tokenizer
onnx_path = Path("gte-small-dense")
model.save_pretrained(onnx_path)
tokenizer.save_pretrained(onnx_path)
Model info
feature-extraction
transformers
transformers
onnx
bert
feature-extraction
en
license:mit
text-embeddings-inference
endpoints_compatible
region:us
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♥ 0
Created: 2023-10-12
Updated: 2023-10-12
View on Hugging FaceFiles (9)
.gitattributes
README.md
added_tokens.json
config.json
model.onnx
ONNX
special_tokens_map.json
tokenizer.json
tokenizer_config.json
vocab.txt