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--- |
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license: apache-2.0 |
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base_model: Pipper/SolCoder |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: SolCoder |
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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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# SolCoder |
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This model is a fine-tuned version of [Pipper/SolCoder](https://huggingface.co./Pipper/SolCoder) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5568 |
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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: 37 |
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- eval_batch_size: 37 |
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- seed: 100 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- total_train_batch_size: 148 |
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- total_eval_batch_size: 148 |
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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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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:------:|:---------------:| |
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| 0.6094 | 1.0 | 7440 | 0.6185 | |
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| 0.598 | 2.0 | 14880 | 0.6124 | |
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| 0.5845 | 3.0 | 22320 | 0.6075 | |
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| 0.5723 | 4.0 | 29760 | 0.6006 | |
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| 0.5589 | 5.0 | 37200 | 0.5943 | |
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| 0.5495 | 6.0 | 44640 | 0.5894 | |
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| 0.5371 | 7.0 | 52080 | 0.5861 | |
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| 0.5291 | 8.0 | 59520 | 0.5811 | |
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| 0.52 | 9.0 | 66960 | 0.5765 | |
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| 0.5095 | 10.0 | 74400 | 0.5746 | |
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| 0.5056 | 11.0 | 81840 | 0.5700 | |
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| 0.4967 | 12.0 | 89280 | 0.5682 | |
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| 0.4894 | 13.0 | 96720 | 0.5659 | |
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| 0.4861 | 14.0 | 104160 | 0.5619 | |
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| 0.4773 | 15.0 | 111600 | 0.5599 | |
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| 0.4754 | 16.0 | 119040 | 0.5599 | |
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| 0.4689 | 17.0 | 126480 | 0.5578 | |
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| 0.4642 | 18.0 | 133920 | 0.5575 | |
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| 0.4627 | 19.0 | 141360 | 0.5566 | |
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| 0.4573 | 20.0 | 148800 | 0.5568 | |
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### Framework versions |
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- Transformers 4.33.0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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