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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-test |
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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-test |
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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: 0.8508 |
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- Exact Match: 0.156 |
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- Rouge1: 0.5559 |
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- Rouge2: 0.3857 |
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- Rougel: 0.5378 |
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- Rougelsum: 0.5465 |
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- Bleu: 0.1246 |
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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: 0.001 |
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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: 1 |
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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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| 1.3563 | 0.16 | 500 | 1.1652 | 0.1115 | 0.5098 | 0.3191 | 0.4915 | 0.4982 | 0.1088 | |
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| 0.9656 | 0.32 | 1000 | 1.0435 | 0.1245 | 0.5246 | 0.3444 | 0.5075 | 0.5145 | 0.1164 | |
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| 0.8627 | 0.48 | 1500 | 0.9851 | 0.121 | 0.5275 | 0.3420 | 0.5074 | 0.5154 | 0.1132 | |
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| 0.7718 | 0.64 | 2000 | 0.9288 | 0.1385 | 0.5334 | 0.3589 | 0.5174 | 0.5242 | 0.1206 | |
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| 0.7237 | 0.8 | 2500 | 0.8867 | 0.1495 | 0.5505 | 0.3762 | 0.5328 | 0.5406 | 0.1208 | |
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| 0.6812 | 0.96 | 3000 | 0.8508 | 0.156 | 0.5559 | 0.3857 | 0.5378 | 0.5465 | 0.1246 | |
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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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