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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: src_prober_codellama-13b-last1unfreeze |
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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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# src_prober_codellama-13b-last1unfreeze |
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6267 |
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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: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 1000 |
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- num_epochs: 5 |
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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 | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 0.7443 | 0.12 | 500 | 0.7429 | |
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| 0.6851 | 0.24 | 1000 | 0.7170 | |
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| 0.6723 | 0.36 | 1500 | 0.6912 | |
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| 0.6605 | 0.48 | 2000 | 0.6730 | |
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| 0.6475 | 0.6 | 2500 | 0.6643 | |
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| 0.6419 | 0.72 | 3000 | 0.6584 | |
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| 0.6307 | 0.85 | 3500 | 0.6532 | |
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| 0.6167 | 0.97 | 4000 | 0.6495 | |
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| 0.6272 | 1.09 | 4500 | 0.6477 | |
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| 0.6002 | 1.21 | 5000 | 0.6445 | |
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| 0.6303 | 1.33 | 5500 | 0.6429 | |
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| 0.6405 | 1.45 | 6000 | 0.6421 | |
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| 0.6041 | 1.57 | 6500 | 0.6387 | |
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| 0.5912 | 1.69 | 7000 | 0.6370 | |
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| 0.6121 | 1.81 | 7500 | 0.6360 | |
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| 0.613 | 1.93 | 8000 | 0.6344 | |
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| 0.6126 | 2.05 | 8500 | 0.6338 | |
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| 0.5932 | 2.17 | 9000 | 0.6344 | |
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| 0.5927 | 2.3 | 9500 | 0.6332 | |
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| 0.5883 | 2.42 | 10000 | 0.6317 | |
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| 0.6023 | 2.54 | 10500 | 0.6308 | |
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| 0.5898 | 2.66 | 11000 | 0.6311 | |
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| 0.576 | 2.78 | 11500 | 0.6291 | |
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| 0.5699 | 2.9 | 12000 | 0.6291 | |
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| 0.6093 | 3.02 | 12500 | 0.6290 | |
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| 0.5754 | 3.14 | 13000 | 0.6292 | |
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| 0.6294 | 3.26 | 13500 | 0.6282 | |
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| 0.591 | 3.38 | 14000 | 0.6283 | |
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| 0.599 | 3.5 | 14500 | 0.6273 | |
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| 0.5933 | 3.62 | 15000 | 0.6281 | |
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| 0.565 | 3.75 | 15500 | 0.6268 | |
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| 0.5884 | 3.87 | 16000 | 0.6267 | |
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| 0.5809 | 3.99 | 16500 | 0.6266 | |
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| 0.5618 | 4.11 | 17000 | 0.6271 | |
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| 0.5749 | 4.23 | 17500 | 0.6274 | |
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| 0.577 | 4.35 | 18000 | 0.6268 | |
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| 0.5947 | 4.47 | 18500 | 0.6267 | |
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| 0.5902 | 4.59 | 19000 | 0.6268 | |
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| 0.5869 | 4.71 | 19500 | 0.6268 | |
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| 0.5829 | 4.83 | 20000 | 0.6268 | |
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| 0.5587 | 4.95 | 20500 | 0.6267 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.2.0 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.2 |
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