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--- |
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base_model: bigcode/starencoder |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- accuracy |
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model-index: |
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- name: stack-edu-classifier-go |
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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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# stack-edu-classifier-go |
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This model is a fine-tuned version of [bigcode/starencoder](https://huggingface.co/bigcode/starencoder) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3999 |
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- Precision: 0.5624 |
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- Recall: 0.2985 |
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- F1 Macro: 0.3294 |
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- Accuracy: 0.5640 |
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- F1 Binary Minimum3: 0.6539 |
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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: 0.0003 |
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- train_batch_size: 64 |
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- eval_batch_size: 256 |
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- seed: 0 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- total_train_batch_size: 128 |
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- total_eval_batch_size: 512 |
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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: 200 |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 Macro | Accuracy | F1 Binary Minimum3 | |
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|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:--------:|:--------:|:------------------:| |
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| No log | 0 | 0 | 4.5815 | 0.0004 | 0.1667 | 0.0007 | 0.0022 | 0 | |
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| 0.4211 | 1.4451 | 1000 | 0.4201 | 0.4406 | 0.2753 | 0.2931 | 0.5507 | 0.6478 | |
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| 0.4039 | 2.8902 | 2000 | 0.4134 | 0.3772 | 0.2738 | 0.2876 | 0.5521 | 0.6465 | |
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| 0.405 | 4.3353 | 3000 | 0.4097 | 0.4386 | 0.2797 | 0.2979 | 0.5642 | 0.6287 | |
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| 0.3997 | 5.7803 | 4000 | 0.4122 | 0.4282 | 0.2856 | 0.3020 | 0.5675 | 0.6163 | |
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| 0.4135 | 7.2254 | 5000 | 0.4058 | 0.5497 | 0.2862 | 0.3084 | 0.5673 | 0.6401 | |
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| 0.3915 | 8.6705 | 6000 | 0.4051 | 0.5559 | 0.2911 | 0.3162 | 0.5594 | 0.6525 | |
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| 0.3873 | 10.1156 | 7000 | 0.4035 | 0.5652 | 0.2868 | 0.3113 | 0.5618 | 0.6511 | |
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| 0.3981 | 11.5607 | 8000 | 0.4020 | 0.5510 | 0.2890 | 0.3108 | 0.5693 | 0.6414 | |
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| 0.3855 | 13.0058 | 9000 | 0.4074 | 0.5470 | 0.2879 | 0.3104 | 0.5708 | 0.6192 | |
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| 0.3863 | 14.4509 | 10000 | 0.4003 | 0.5196 | 0.3047 | 0.3370 | 0.5664 | 0.6534 | |
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| 0.3784 | 15.8960 | 11000 | 0.4007 | 0.5616 | 0.2919 | 0.3179 | 0.5622 | 0.6516 | |
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| 0.3982 | 17.3410 | 12000 | 0.4034 | 0.5658 | 0.2910 | 0.3159 | 0.5595 | 0.6635 | |
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| 0.4013 | 18.7861 | 13000 | 0.3999 | 0.5624 | 0.2985 | 0.3294 | 0.5640 | 0.6539 | |
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### Framework versions |
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- Transformers 4.43.4 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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