|
--- |
|
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: 1.8906 |
|
- Accuracy: 0.6346 |
|
|
|
## 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 | 4.5523 | 0.3432 | |
|
| No log | 2.0 | 80 | 3.8711 | 0.3796 | |
|
| No log | 3.0 | 120 | 3.2419 | 0.4503 | |
|
| No log | 4.0 | 160 | 2.8709 | 0.4962 | |
|
| No log | 5.0 | 200 | 2.6999 | 0.5085 | |
|
| No log | 6.0 | 240 | 2.6622 | 0.5216 | |
|
| No log | 7.0 | 280 | 2.5048 | 0.5410 | |
|
| No log | 8.0 | 320 | 2.4249 | 0.5581 | |
|
| No log | 9.0 | 360 | 2.3727 | 0.5623 | |
|
| No log | 10.0 | 400 | 2.3625 | 0.5665 | |
|
| No log | 11.0 | 440 | 2.3320 | 0.5706 | |
|
| No log | 12.0 | 480 | 2.1704 | 0.5950 | |
|
| 3.081 | 13.0 | 520 | 2.2109 | 0.5893 | |
|
| 3.081 | 14.0 | 560 | 2.2330 | 0.5884 | |
|
| 3.081 | 15.0 | 600 | 2.1454 | 0.5954 | |
|
| 3.081 | 16.0 | 640 | 2.1740 | 0.5951 | |
|
| 3.081 | 17.0 | 680 | 2.1219 | 0.5920 | |
|
| 3.081 | 18.0 | 720 | 2.1136 | 0.6052 | |
|
| 3.081 | 19.0 | 760 | 2.0586 | 0.6127 | |
|
| 3.081 | 20.0 | 800 | 2.0185 | 0.6113 | |
|
| 3.081 | 21.0 | 840 | 2.0493 | 0.6129 | |
|
| 3.081 | 22.0 | 880 | 1.9766 | 0.6217 | |
|
| 3.081 | 23.0 | 920 | 1.9968 | 0.6189 | |
|
| 3.081 | 24.0 | 960 | 1.9567 | 0.6276 | |
|
| 2.122 | 25.0 | 1000 | 1.9611 | 0.6269 | |
|
| 2.122 | 26.0 | 1040 | 1.9437 | 0.6254 | |
|
| 2.122 | 27.0 | 1080 | 1.9865 | 0.6266 | |
|
| 2.122 | 28.0 | 1120 | 1.9112 | 0.6295 | |
|
| 2.122 | 29.0 | 1160 | 1.8903 | 0.6292 | |
|
| 2.122 | 30.0 | 1200 | 1.8992 | 0.6376 | |
|
| 2.122 | 31.0 | 1240 | 1.9122 | 0.6327 | |
|
| 2.122 | 32.0 | 1280 | 1.8906 | 0.6346 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.25.1 |
|
- Pytorch 1.13.0+cu117 |
|
- Datasets 2.7.1 |
|
- Tokenizers 0.13.2 |
|
|
