| --- |
| license: bsd-3-clause |
| base_model: Salesforce/codet5p-220m |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: SolCoderFuncs |
| 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. --> |
|
|
| # SolCoderFuncs |
|
|
| 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: 0.5574 |
|
|
| ## 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: 37 |
| - eval_batch_size: 37 |
| - seed: 100 |
| - distributed_type: multi-GPU |
| - num_devices: 4 |
| - total_train_batch_size: 148 |
| - total_eval_batch_size: 148 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 40 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | |
| |:-------------:|:-----:|:------:|:---------------:| |
| | 0.8793 | 1.0 | 3600 | 0.7881 | |
| | 0.7622 | 2.0 | 7200 | 0.7190 | |
| | 0.7077 | 3.0 | 10800 | 0.6769 | |
| | 0.659 | 4.0 | 14400 | 0.6518 | |
| | 0.6212 | 5.0 | 18000 | 0.6300 | |
| | 0.589 | 6.0 | 21600 | 0.6119 | |
| | 0.562 | 7.0 | 25200 | 0.6014 | |
| | 0.5361 | 8.0 | 28800 | 0.5905 | |
| | 0.5171 | 9.0 | 32400 | 0.5799 | |
| | 0.4973 | 10.0 | 36000 | 0.5747 | |
| | 0.4772 | 11.0 | 39600 | 0.5666 | |
| | 0.4619 | 12.0 | 43200 | 0.5610 | |
| | 0.4443 | 13.0 | 46800 | 0.5588 | |
| | 0.4335 | 14.0 | 50400 | 0.5571 | |
| | 0.4192 | 15.0 | 54000 | 0.5534 | |
| | 0.4062 | 16.0 | 57600 | 0.5512 | |
| | 0.3977 | 17.0 | 61200 | 0.5513 | |
| | 0.3864 | 18.0 | 64800 | 0.5515 | |
| | 0.3791 | 19.0 | 68400 | 0.5507 | |
| | 0.3718 | 20.0 | 72000 | 0.5510 | |
| | 0.4132 | 21.0 | 75600 | 0.5551 | |
| | 0.4079 | 22.0 | 79200 | 0.5499 | |
| | 0.3957 | 23.0 | 82800 | 0.5522 | |
| | 0.3895 | 24.0 | 86400 | 0.5482 | |
| | 0.3797 | 25.0 | 90000 | 0.5477 | |
| | 0.3686 | 26.0 | 93600 | 0.5486 | |
| | 0.3628 | 27.0 | 97200 | 0.5491 | |
| | 0.3518 | 28.0 | 100800 | 0.5502 | |
| | 0.3452 | 29.0 | 104400 | 0.5494 | |
| | 0.3379 | 30.0 | 108000 | 0.5546 | |
| | 0.3292 | 31.0 | 111600 | 0.5486 | |
| | 0.3232 | 32.0 | 115200 | 0.5522 | |
| | 0.3146 | 33.0 | 118800 | 0.5524 | |
| | 0.31 | 34.0 | 122400 | 0.5505 | |
| | 0.3057 | 35.0 | 126000 | 0.5538 | |
| | 0.301 | 36.0 | 129600 | 0.5549 | |
| | 0.2955 | 37.0 | 133200 | 0.5557 | |
| | 0.2901 | 38.0 | 136800 | 0.5554 | |
| | 0.2872 | 39.0 | 140400 | 0.5564 | |
| | 0.2844 | 40.0 | 144000 | 0.5574 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.33.0 |
| - Pytorch 2.1.0+cu121 |
| - Datasets 2.11.0 |
| - Tokenizers 0.13.3 |
|
|
