---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: CodeBERTa-commit-message-autocomplete
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# CodeBERTa-commit-message-autocomplete

This model is a fine-tuned version of [microsoft/codebert-base-mlm](https://huggingface.co/microsoft/codebert-base-mlm) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8796
- Accuracy: 0.6381

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 40   | 4.5229          | 0.3460   |
| No log        | 2.0   | 80   | 3.8419          | 0.3792   |
| No log        | 3.0   | 120  | 3.1830          | 0.4538   |
| No log        | 4.0   | 160  | 2.8435          | 0.5      |
| No log        | 5.0   | 200  | 2.6741          | 0.5126   |
| No log        | 6.0   | 240  | 2.6468          | 0.5211   |
| No log        | 7.0   | 280  | 2.4902          | 0.5431   |
| No log        | 8.0   | 320  | 2.4223          | 0.5590   |
| No log        | 9.0   | 360  | 2.3677          | 0.5625   |
| No log        | 10.0  | 400  | 2.3634          | 0.5654   |
| No log        | 11.0  | 440  | 2.3334          | 0.5693   |
| No log        | 12.0  | 480  | 2.1738          | 0.5963   |
| 3.0595        | 13.0  | 520  | 2.2148          | 0.5882   |
| 3.0595        | 14.0  | 560  | 2.2387          | 0.5878   |
| 3.0595        | 15.0  | 600  | 2.1472          | 0.5938   |
| 3.0595        | 16.0  | 640  | 2.1703          | 0.5963   |
| 3.0595        | 17.0  | 680  | 2.1183          | 0.5937   |
| 3.0595        | 18.0  | 720  | 2.1139          | 0.6035   |
| 3.0595        | 19.0  | 760  | 2.0543          | 0.6106   |
| 3.0595        | 20.0  | 800  | 2.0135          | 0.6148   |
| 3.0595        | 21.0  | 840  | 2.0445          | 0.6119   |
| 3.0595        | 22.0  | 880  | 1.9723          | 0.6221   |
| 3.0595        | 23.0  | 920  | 1.9972          | 0.6205   |
| 3.0595        | 24.0  | 960  | 1.9588          | 0.6280   |
| 2.1206        | 25.0  | 1000 | 1.9563          | 0.6280   |
| 2.1206        | 26.0  | 1040 | 1.9421          | 0.6254   |
| 2.1206        | 27.0  | 1080 | 1.9820          | 0.6291   |
| 2.1206        | 28.0  | 1120 | 1.8989          | 0.6315   |
| 2.1206        | 29.0  | 1160 | 1.8743          | 0.6330   |
| 2.1206        | 30.0  | 1200 | 1.8840          | 0.6389   |
| 2.1206        | 31.0  | 1240 | 1.9038          | 0.6325   |
| 2.1206        | 32.0  | 1280 | 1.8796          | 0.6381   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2