YAML Metadata Warning: The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

CMG/CMC: CodeT5 (without history)

This is the checkpoint for CodeT5 model, fine-tuned for the commit message generation (and/or completion) task as part of the paper "From Commit Message Generation to History-Aware Commit Message Completion", ASE 2023.

Details

🔍 For further details, please refer to:

Paper: https://arxiv.org/abs/2308.07655

Repository: https://github.com/JetBrains-Research/commit_message_generation

This model is based on Salesforce/codet5-base checkpoint from 📜 CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation.
This model was trained with commit diffs, WITHOUT commit message history.
This model was trained on the CommitChronicle dataset introduced in our study.

Our hyperparameter setting is mostly based on 📜 RACE: Retrieval-augmented Commit Message Generation. The exact values are provided below:

Hyperparameter	Value
Encoder context max length	512
Decoder context max length	512
Number of training epochs	1
Batch size	32
Optimizer	AdamW
Warmup	Linear
Number of warmup steps	100
Peak learning rate	0.00002

Available checkpoints

We also released checkpoints for other models fine-tuned as part of our study.

Models trained with commit message history:
- CodeT5: 🤗 JetBrains-Research/cmg-codet5-with-history
- CodeReviewer: 🤗 JetBrains-Research/cmg-codereviewer-with-history
- RACE: 🤗 JetBrains-Research/cmg-race-with-history
Models trained without commit message history:
- CodeT5: 🤗 JetBrains-Research/cmg-codet5-without-history (this model)
- CodeReviewer: 🤗 JetBrains-Research/cmg-codereviewer-without-history
- RACE: 🤗 JetBrains-Research/cmg-race-without-history

Citation

TODO

Downloads last month: -

Safetensors

Model size

0.2B params

Tensor type

F32

Dataset used to train JetBrains-Research/cmg-codet5-without-history

Collection including JetBrains-Research/cmg-codet5-without-history

📜 History-Aware Commit Message Completion 🔮

Collection

All the resources for our ASE'23 paper! • 8 items • Updated Mar 14 • 4