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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-ruby |
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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-ruby |
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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: 0.3282 |
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- Precision: 0.4623 |
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- Recall: 0.3260 |
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- F1 Macro: 0.3536 |
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- Accuracy: 0.6657 |
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- F1 Binary Minimum3: 0.6101 |
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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 | 4.7647 | 0.0010 | 0.1667 | 0.0020 | 0.0062 | 0 | |
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| 0.358 | 1.4368 | 1000 | 0.3549 | 0.4093 | 0.2953 | 0.3072 | 0.6513 | 0.5882 | |
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| 0.3559 | 2.8736 | 2000 | 0.3425 | 0.4649 | 0.3087 | 0.3294 | 0.6571 | 0.6143 | |
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| 0.3534 | 4.3103 | 3000 | 0.3391 | 0.4318 | 0.3144 | 0.3349 | 0.6586 | 0.6149 | |
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| 0.3539 | 5.7471 | 4000 | 0.3394 | 0.4219 | 0.3244 | 0.3446 | 0.6579 | 0.6298 | |
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| 0.3585 | 7.1839 | 5000 | 0.3359 | 0.4756 | 0.3106 | 0.3350 | 0.6622 | 0.6069 | |
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| 0.3476 | 8.6207 | 6000 | 0.3339 | 0.4551 | 0.3178 | 0.3415 | 0.6638 | 0.6082 | |
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| 0.3496 | 10.0575 | 7000 | 0.3307 | 0.4512 | 0.3263 | 0.3505 | 0.6656 | 0.6204 | |
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| 0.3362 | 11.4943 | 8000 | 0.3307 | 0.4657 | 0.3228 | 0.3485 | 0.6640 | 0.6178 | |
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| 0.3442 | 12.9310 | 9000 | 0.3307 | 0.4771 | 0.3248 | 0.3517 | 0.6677 | 0.6095 | |
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| 0.344 | 14.3678 | 10000 | 0.3287 | 0.4774 | 0.3222 | 0.3496 | 0.6660 | 0.6147 | |
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| 0.3332 | 15.8046 | 11000 | 0.3281 | 0.4678 | 0.3240 | 0.3504 | 0.6658 | 0.6168 | |
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| 0.3359 | 17.2414 | 12000 | 0.3300 | 0.4658 | 0.3203 | 0.3471 | 0.6643 | 0.6100 | |
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| 0.3306 | 18.6782 | 13000 | 0.3282 | 0.4623 | 0.3260 | 0.3536 | 0.6657 | 0.6101 | |
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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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