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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: GraphCodeBert-Moreepcohs |
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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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# GraphCodeBert-Moreepcohs |
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This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.3134 |
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- Rouge2 Precision: 0.259 |
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- Rouge2 Recall: 0.2528 |
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- Rouge2 Fmeasure: 0.25 |
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- Bleu Score: 0.2387 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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_steps: 1500 |
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- num_epochs: 8 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | Bleu Score | |
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|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|:----------:| |
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| 5.8439 | 0.87 | 1500 | 4.1071 | 0.1478 | 0.1394 | 0.139 | 0.1384 | |
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| 3.7868 | 1.73 | 3000 | 3.6540 | 0.1742 | 0.1731 | 0.1697 | 0.1777 | |
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| 3.4877 | 2.6 | 4500 | 3.4311 | 0.205 | 0.1966 | 0.1959 | 0.193 | |
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| 3.0162 | 3.46 | 6000 | 3.3283 | 0.2249 | 0.2162 | 0.2142 | 0.2124 | |
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| 2.9085 | 4.33 | 7500 | 3.2684 | 0.2418 | 0.2312 | 0.2303 | 0.2233 | |
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| 2.5761 | 5.2 | 9000 | 3.2578 | 0.2427 | 0.2398 | 0.2354 | 0.2293 | |
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| 2.4163 | 6.06 | 10500 | 3.3048 | 0.2596 | 0.2516 | 0.2498 | 0.2386 | |
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| 2.2094 | 6.93 | 12000 | 3.2825 | 0.2561 | 0.2529 | 0.2492 | 0.2396 | |
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| 2.0907 | 7.79 | 13500 | 3.3134 | 0.259 | 0.2528 | 0.25 | 0.2387 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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