| --- |
| license: bsd-3-clause |
| base_model: Salesforce/codet5p-220m |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: SolExplain |
| 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. --> |
|
|
| # SolExplain |
|
|
| This model is a fine-tuned version of [Salesforce/codet5p-220m](https://huggingface.co/Salesforce/codet5p-220m) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.2952 |
|
|
| ## 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: 0.0001 |
| - train_batch_size: 10 |
| - eval_batch_size: 10 |
| - seed: 100 |
| - distributed_type: multi-GPU |
| - num_devices: 4 |
| - total_train_batch_size: 40 |
| - total_eval_batch_size: 40 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 15 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | |
| |:-------------:|:-----:|:------:|:---------------:| |
| | 0.658 | 1.0 | 19184 | 0.5830 | |
| | 0.5403 | 2.0 | 38368 | 0.4988 | |
| | 0.48 | 3.0 | 57552 | 0.4473 | |
| | 0.4282 | 4.0 | 76736 | 0.4124 | |
| | 0.3918 | 5.0 | 95920 | 0.3857 | |
| | 0.3464 | 6.0 | 115104 | 0.3650 | |
| | 0.3222 | 7.0 | 134288 | 0.3503 | |
| | 0.3007 | 8.0 | 153472 | 0.3357 | |
| | 0.2782 | 9.0 | 172656 | 0.3269 | |
| | 0.2562 | 10.0 | 191840 | 0.3184 | |
| | 0.2386 | 11.0 | 211024 | 0.3101 | |
| | 0.2196 | 12.0 | 230208 | 0.3020 | |
| | 0.2042 | 13.0 | 249392 | 0.3005 | |
| | 0.1931 | 14.0 | 268576 | 0.2971 | |
| | 0.1734 | 15.0 | 287760 | 0.2952 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.33.0 |
| - Pytorch 2.1.2+cu121 |
| - Datasets 2.16.1 |
| - Tokenizers 0.13.3 |
|
|
