| --- |
| license: bsd-3-clause |
| base_model: Salesforce/codet5-large |
| tags: |
| - generated_from_trainer |
| datasets: |
| - arrow |
| library_name: peft |
| model-index: |
| - name: codet5-large-2024-11-27_23-08 |
| 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. --> |
|
|
| # codet5-large-2024-11-27_23-08 |
| |
| This model is a fine-tuned version of [Salesforce/codet5-large](https://huggingface.co/Salesforce/codet5-large) on the arrow dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.2038 |
| - Gen Len: 18.9997 |
| - Bertscorer-p: 0.6236 |
| - Bertscorer-r: 0.2353 |
| - Bertscorer-f1: 0.4224 |
| - Sacrebleu-score: 14.0575 |
| - Sacrebleu-precisions: [93.21674851306209, 85.96364041936204, 80.9029722765622, 77.23407849541078] |
| - Bleu-bp: 0.1671 |
| |
| ## 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: 0.0003 |
| - 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 |
| - num_epochs: 10 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Gen Len | Bertscorer-p | Bertscorer-r | Bertscorer-f1 | Sacrebleu-score | Sacrebleu-precisions | Bleu-bp | |
| |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------------:|:------------:|:-------------:|:---------------:|:----------------------------------------------------------------------------:|:-------:| |
| | 0.307 | 1.0 | 2386 | 0.2578 | 18.9996 | 0.6125 | 0.2274 | 0.4130 | 13.3756 | [91.98367952522256, 82.75386027211427, 76.38867305533972, 71.8096760543514] | 0.1664 | |
| | 0.2249 | 2.0 | 4772 | 0.2234 | 18.9998 | 0.6129 | 0.2272 | 0.4130 | 13.6165 | [92.0984638163983, 83.32430926029143, 77.59097368761036, 73.62220971675107] | 0.1673 | |
| | 0.1705 | 3.0 | 7158 | 0.2052 | 18.9997 | 0.6144 | 0.2270 | 0.4137 | 13.6722 | [92.39128019377347, 83.86287225736548, 78.10795204812425, 74.06802567856741] | 0.1671 | |
| | 0.1359 | 4.0 | 9544 | 0.1975 | 18.9999 | 0.6180 | 0.2312 | 0.4176 | 13.8305 | [92.69034856516717, 84.60221526799009, 79.12022601595204, 75.24504516334781] | 0.1673 | |
| | 0.1124 | 5.0 | 11930 | 0.1965 | 18.9997 | 0.6219 | 0.2347 | 0.4212 | 14.0296 | [93.00053938628186, 85.37114434185644, 79.99295344980192, 76.11429212978557] | 0.1683 | |
| | 0.0901 | 6.0 | 14316 | 0.1953 | 18.9997 | 0.6228 | 0.2341 | 0.4214 | 13.9769 | [93.14913197145842, 85.62195160827568, 80.38468501866524, 76.62666892006084] | 0.1669 | |
| | 0.0717 | 7.0 | 16702 | 0.1976 | 18.9998 | 0.6252 | 0.2356 | 0.4233 | 14.0892 | [93.2416842914824, 85.8948155335173, 80.64185934489403, 76.84015322512667] | 0.1679 | |
| | 0.0608 | 8.0 | 19088 | 0.2002 | 18.9997 | 0.6235 | 0.2355 | 0.4224 | 14.0253 | [93.20067563563089, 85.85486736946112, 80.71698243315461, 76.97434501403373] | 0.1670 | |
| | 0.0492 | 9.0 | 21474 | 0.2014 | 18.9998 | 0.6256 | 0.2367 | 0.4240 | 14.0964 | [93.32790404975198, 86.14961977943226, 81.03875968992249, 77.30994700558082] | 0.1673 | |
| | 0.0428 | 10.0 | 23860 | 0.2038 | 18.9997 | 0.6236 | 0.2353 | 0.4224 | 14.0575 | [93.21674851306209, 85.96364041936204, 80.9029722765622, 77.23407849541078] | 0.1671 | |
| |
| |
| ### Framework versions |
| |
| - PEFT 0.13.2 |
| - Transformers 4.40.1 |
| - Pytorch 1.13.1+cu117 |
| - Datasets 3.1.0 |
| - Tokenizers 0.19.1 |
