---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: CommitPredictor
  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. -->

# CommitPredictor

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: 0.5888
- Accuracy: 0.8783
- F1: 0.8783
- Bleu4: 0.8598

## 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: 42
- eval_batch_size: 42
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 126
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Bleu4  |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|
| No log        | 1.0   | 236  | 0.8706          | 0.8253   | 0.8253 | 0.7764 |
| No log        | 2.0   | 472  | 0.7296          | 0.8503   | 0.8503 | 0.8287 |
| 1.0825        | 3.0   | 708  | 0.6826          | 0.8594   | 0.8594 | 0.8123 |
| 1.0825        | 4.0   | 944  | 0.6655          | 0.8645   | 0.8645 | 0.8480 |
| 0.755         | 5.0   | 1180 | 0.6317          | 0.8696   | 0.8696 | 0.9028 |
| 0.755         | 6.0   | 1416 | 0.6333          | 0.8699   | 0.8699 | 0.8870 |
| 0.6948        | 7.0   | 1652 | 0.6147          | 0.8738   | 0.8738 | 0.9187 |
| 0.6948        | 8.0   | 1888 | 0.6110          | 0.8738   | 0.8738 | 0.8080 |
| 0.6633        | 9.0   | 2124 | 0.5987          | 0.8770   | 0.8770 | 0.8903 |
| 0.6633        | 10.0  | 2360 | 0.5888          | 0.8783   | 0.8783 | 0.8598 |


### Framework versions

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