| --- |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: GraphCodeBert-Moreepcohs |
| 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. --> |
|
|
| # GraphCodeBert-Moreepcohs |
|
|
| This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 3.3134 |
| - Rouge2 Precision: 0.259 |
| - Rouge2 Recall: 0.2528 |
| - Rouge2 Fmeasure: 0.25 |
| - Bleu Score: 0.2387 |
|
|
| ## 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: 5e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 1500 |
| - num_epochs: 8 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | Bleu Score | |
| |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|:----------:| |
| | 5.8439 | 0.87 | 1500 | 4.1071 | 0.1478 | 0.1394 | 0.139 | 0.1384 | |
| | 3.7868 | 1.73 | 3000 | 3.6540 | 0.1742 | 0.1731 | 0.1697 | 0.1777 | |
| | 3.4877 | 2.6 | 4500 | 3.4311 | 0.205 | 0.1966 | 0.1959 | 0.193 | |
| | 3.0162 | 3.46 | 6000 | 3.3283 | 0.2249 | 0.2162 | 0.2142 | 0.2124 | |
| | 2.9085 | 4.33 | 7500 | 3.2684 | 0.2418 | 0.2312 | 0.2303 | 0.2233 | |
| | 2.5761 | 5.2 | 9000 | 3.2578 | 0.2427 | 0.2398 | 0.2354 | 0.2293 | |
| | 2.4163 | 6.06 | 10500 | 3.3048 | 0.2596 | 0.2516 | 0.2498 | 0.2386 | |
| | 2.2094 | 6.93 | 12000 | 3.2825 | 0.2561 | 0.2529 | 0.2492 | 0.2396 | |
| | 2.0907 | 7.79 | 13500 | 3.3134 | 0.259 | 0.2528 | 0.25 | 0.2387 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.25.1 |
| - Pytorch 1.13.1+cu116 |
| - Datasets 2.8.0 |
| - Tokenizers 0.13.2 |
| |
