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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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metrics:
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- rouge
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- bleu
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model-index:
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- name: Salesforce-codet5-small-CodeXGLUE-CONCODE-adamw
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Salesforce-codet5-small-CodeXGLUE-CONCODE-adamw
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This model is a fine-tuned version of [Salesforce/codet5-small](https://huggingface.co/Salesforce/codet5-small) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7666
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- Exact Match: 0.163
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- Rouge1: 0.5716
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- Rouge2: 0.4046
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- Rougel: 0.5536
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- Rougelsum: 0.5614
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- Bleu: 0.1335
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0001
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.05
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- num_epochs: 10
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Exact Match | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu |
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|:-------------:|:-----:|:-----:|:---------------:|:-----------:|:------:|:------:|:------:|:---------:|:------:|
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| 2.3935 | 0.16 | 500 | 0.9724 | 0.129 | 0.5286 | 0.3466 | 0.5098 | 0.5153 | 0.1127 |
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| 0.8984 | 0.32 | 1000 | 0.8919 | 0.138 | 0.5463 | 0.3714 | 0.5285 | 0.5353 | 0.1200 |
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| 0.8121 | 0.48 | 1500 | 0.8583 | 0.1455 | 0.5529 | 0.3787 | 0.5350 | 0.5426 | 0.1158 |
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| 0.7598 | 0.64 | 2000 | 0.8437 | 0.1485 | 0.5541 | 0.3813 | 0.5355 | 0.5432 | 0.1197 |
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| 0.7289 | 0.8 | 2500 | 0.8189 | 0.158 | 0.5597 | 0.3906 | 0.5416 | 0.5501 | 0.1222 |
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| 0.7053 | 0.96 | 3000 | 0.8145 | 0.161 | 0.5572 | 0.3888 | 0.5392 | 0.5469 | 0.1222 |
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| 0.6544 | 1.12 | 3500 | 0.7982 | 0.1565 | 0.5606 | 0.3920 | 0.5436 | 0.5517 | 0.1260 |
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| 0.6334 | 1.28 | 4000 | 0.7974 | 0.1585 | 0.5633 | 0.3906 | 0.5448 | 0.5529 | 0.1284 |
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| 0.6236 | 1.44 | 4500 | 0.7943 | 0.163 | 0.5639 | 0.3931 | 0.5455 | 0.5542 | 0.1275 |
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| 0.6221 | 1.6 | 5000 | 0.7824 | 0.1655 | 0.5718 | 0.4011 | 0.5537 | 0.5621 | 0.1310 |
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| 0.608 | 1.76 | 5500 | 0.7792 | 0.163 | 0.5664 | 0.3997 | 0.5490 | 0.5567 | 0.1314 |
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| 0.5956 | 1.92 | 6000 | 0.7785 | 0.1605 | 0.5641 | 0.3981 | 0.5470 | 0.5546 | 0.1294 |
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| 0.5701 | 2.08 | 6500 | 0.7800 | 0.157 | 0.5673 | 0.3955 | 0.5489 | 0.5568 | 0.1336 |
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| 0.5378 | 2.24 | 7000 | 0.7720 | 0.1655 | 0.5686 | 0.4000 | 0.5504 | 0.5582 | 0.1308 |
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| 0.541 | 2.4 | 7500 | 0.7709 | 0.1625 | 0.5699 | 0.3984 | 0.5511 | 0.5590 | 0.1313 |
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| 0.5359 | 2.56 | 8000 | 0.7673 | 0.164 | 0.5697 | 0.4023 | 0.5521 | 0.5601 | 0.1332 |
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| 0.5322 | 2.72 | 8500 | 0.7642 | 0.1665 | 0.5708 | 0.4033 | 0.5527 | 0.5606 | 0.1350 |
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| 0.5387 | 2.88 | 9000 | 0.7622 | 0.159 | 0.5672 | 0.3988 | 0.5500 | 0.5573 | 0.1342 |
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| 0.514 | 3.04 | 9500 | 0.7700 | 0.166 | 0.5722 | 0.4052 | 0.5546 | 0.5618 | 0.1352 |
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| 0.4895 | 3.2 | 10000 | 0.7676 | 0.1615 | 0.5696 | 0.4016 | 0.5516 | 0.5591 | 0.1359 |
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| 0.4827 | 3.36 | 10500 | 0.7665 | 0.162 | 0.5756 | 0.4072 | 0.5577 | 0.5656 | 0.1367 |
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| 0.4814 | 3.52 | 11000 | 0.7700 | 0.1605 | 0.5709 | 0.4026 | 0.5528 | 0.5605 | 0.1334 |
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| 0.4847 | 3.68 | 11500 | 0.7666 | 0.163 | 0.5716 | 0.4046 | 0.5536 | 0.5614 | 0.1335 |
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### Framework versions
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- Transformers 4.27.1
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- Pytorch 1.12.1+cu113
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- Datasets 2.10.1
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- Tokenizers 0.13.2
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