metadata
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: CommitPredictor
results: []
CommitPredictor
This model is a fine-tuned version of microsoft/codebert-base-mlm on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.9935
- Accuracy: 0.6325
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: 21
- eval_batch_size: 21
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 63
- 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 |
|---|---|---|---|---|
| No log | 1.0 | 448 | 2.4744 | 0.5376 |
| 2.9007 | 2.0 | 896 | 2.4149 | 0.5473 |
| 2.5284 | 3.0 | 1344 | 2.3077 | 0.5639 |
| 2.3292 | 4.0 | 1792 | 2.2617 | 0.5640 |
| 2.2692 | 5.0 | 2240 | 2.2155 | 0.5719 |
| 2.1766 | 6.0 | 2688 | 2.1555 | 0.5792 |
| 2.0842 | 7.0 | 3136 | 2.0758 | 0.6030 |
| 2.0268 | 8.0 | 3584 | 2.1446 | 0.5942 |
| 1.9416 | 9.0 | 4032 | 2.1110 | 0.5840 |
| 1.9416 | 10.0 | 4480 | 2.1379 | 0.5888 |
| 1.8969 | 11.0 | 4928 | 2.0461 | 0.6082 |
| 1.8247 | 12.0 | 5376 | 2.0585 | 0.6007 |
| 1.8038 | 13.0 | 5824 | 2.0541 | 0.6022 |
| 1.7601 | 14.0 | 6272 | 2.0832 | 0.6043 |
| 1.7086 | 15.0 | 6720 | 2.0224 | 0.6096 |
| 1.7087 | 16.0 | 7168 | 2.0853 | 0.6057 |
| 1.653 | 17.0 | 7616 | 2.0259 | 0.6124 |
| 1.5953 | 18.0 | 8064 | 1.9913 | 0.6207 |
| 1.6074 | 19.0 | 8512 | 1.9798 | 0.6157 |
| 1.6074 | 20.0 | 8960 | 2.0234 | 0.6033 |
| 1.5749 | 21.0 | 9408 | 1.9686 | 0.6197 |
| 1.535 | 22.0 | 9856 | 2.0068 | 0.6163 |
| 1.4942 | 23.0 | 10304 | 1.9486 | 0.6310 |
| 1.4765 | 24.0 | 10752 | 1.9502 | 0.6304 |
| 1.4558 | 25.0 | 11200 | 1.9509 | 0.6328 |
| 1.4617 | 26.0 | 11648 | 1.9903 | 0.6196 |
| 1.4224 | 27.0 | 12096 | 1.9849 | 0.6321 |
| 1.4019 | 28.0 | 12544 | 1.9781 | 0.6193 |
| 1.4019 | 29.0 | 12992 | 2.0661 | 0.6145 |
| 1.3624 | 30.0 | 13440 | 1.9948 | 0.6191 |
| 1.3517 | 31.0 | 13888 | 1.9117 | 0.6392 |
| 1.3613 | 32.0 | 14336 | 2.0300 | 0.6176 |
| 1.3428 | 33.0 | 14784 | 2.0005 | 0.6226 |
| 1.3257 | 34.0 | 15232 | 2.0079 | 0.6149 |
| 1.3127 | 35.0 | 15680 | 2.0231 | 0.6213 |
| 1.289 | 36.0 | 16128 | 1.9961 | 0.6296 |
| 1.2689 | 37.0 | 16576 | 1.9930 | 0.6221 |
| 1.2651 | 38.0 | 17024 | 1.9675 | 0.6314 |
| 1.2651 | 39.0 | 17472 | 1.9835 | 0.6220 |
| 1.2638 | 40.0 | 17920 | nan | 0.6275 |
| 1.235 | 41.0 | 18368 | 2.0100 | 0.6299 |
| 1.2239 | 42.0 | 18816 | 2.0384 | 0.6152 |
| 1.2147 | 43.0 | 19264 | 2.0421 | 0.6209 |
| 1.1961 | 44.0 | 19712 | 2.0041 | 0.6212 |
| 1.1988 | 45.0 | 20160 | 1.9905 | 0.6230 |
| 1.2007 | 46.0 | 20608 | 2.0222 | 0.6275 |
| 1.2029 | 47.0 | 21056 | 1.9856 | 0.6361 |
| 1.1779 | 48.0 | 21504 | 2.0348 | 0.6184 |
| 1.1779 | 49.0 | 21952 | 1.9196 | 0.6324 |
| 1.1973 | 50.0 | 22400 | 1.9935 | 0.6325 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2