josephj6802/codereviewer
By josephj6802 · ↓ 8 · ♥ 0 · Updated 2024-05-17
What's in this repo
This page lists the files in josephj6802/codereviewer, including decoder_model.onnx, decoder_model_merged.onnx, decoder_model_merged_quantized.onnx and more.
- 8 ONNX models (.onnx files)
- 20 files, including 8 .json, 8 .onnx, 1 .md, 1 .txt
- Last updated 2024-05-17
ONNX files
| File | Size | Download |
|---|---|---|
| onnx/decoder_model.onnx | — | Download |
| onnx/decoder_model_merged.onnx | — | Download |
| onnx/decoder_model_merged_quantized.onnx | — | Download |
| onnx/decoder_model_quantized.onnx | — | Download |
| onnx/decoder_with_past_model.onnx | — | Download |
| onnx/decoder_with_past_model_quantized.onnx | — | Download |
| onnx/encoder_model.onnx | — | Download |
| onnx/encoder_model_quantized.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
Codereviewer ONNX Model
This is an ONNX version of the microsoft/codereviewer model.
Model Description
The Codereviewer model is designed for text classification tasks, specifically for code review comments. This model has been converted to the ONNX format to enable efficient inference.
Intended Use
This model can be used to classify code review comments into various categories. It is suitable for integrating into automated code review systems to assist developers in identifying and categorizing comments.
How to Use
You can use the model with the transformers library by loading it as follows:
from transformers import pipeline
model_id = "josephj6802/codereviewer"
# Load the model
classifier = pipeline("text-classification", model=model_id)
# Use the model for inference
result = classifier("Your input text here")
print(result)
Citation
@article{li2022codereviewer,
title={CodeReviewer: Pre-Training for Automating Code Review Activities},
author={Li, Zhiyu and Lu, Shuai and Guo, Daya and Duan, Nan and Jannu, Shailesh and Jenks, Grant and Majumder, Deep and Green, Jared and Svyatkovskiy, Alexey and Fu, Shengyu and others},
journal={arXiv preprint arXiv:2203.09095},
year={2022}
}
Model info
Created: 2024-05-16
Updated: 2024-05-17
View on Hugging FaceFiles (20)
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