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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: distilbert-base-uncased-finetuned-code-snippet-quality-scoring |
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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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# distilbert-base-uncased-finetuned-code-snippet-quality-scoring |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4070 |
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- Accuracy: 0.8568 |
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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: 2e-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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- num_epochs: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 0.5353 | 0.13 | 1000 | 0.5110 | 0.7574 | |
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| 0.4686 | 0.26 | 2000 | 0.4339 | 0.7859 | |
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| 0.4517 | 0.39 | 3000 | 0.4240 | 0.8002 | |
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| 0.4263 | 0.52 | 4000 | 0.3906 | 0.8169 | |
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| 0.4053 | 0.66 | 5000 | 0.3934 | 0.8191 | |
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| 0.3867 | 0.79 | 6000 | 0.3859 | 0.8253 | |
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| 0.3906 | 0.92 | 7000 | 0.3936 | 0.8335 | |
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| 0.3418 | 1.05 | 8000 | 0.3615 | 0.8380 | |
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| 0.3418 | 1.18 | 9000 | 0.3585 | 0.8400 | |
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| 0.3307 | 1.31 | 10000 | 0.3520 | 0.8432 | |
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| 0.3301 | 1.44 | 11000 | 0.3476 | 0.8475 | |
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| 0.3275 | 1.57 | 12000 | 0.3511 | 0.8497 | |
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| 0.3192 | 1.71 | 13000 | 0.3519 | 0.8540 | |
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| 0.3218 | 1.84 | 14000 | 0.3402 | 0.8495 | |
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| 0.3199 | 1.97 | 15000 | 0.3375 | 0.8580 | |
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| 0.2591 | 2.1 | 16000 | 0.3687 | 0.8568 | |
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| 0.2732 | 2.23 | 17000 | 0.3619 | 0.8521 | |
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| 0.2681 | 2.36 | 18000 | 0.3574 | 0.8563 | |
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| 0.2606 | 2.49 | 19000 | 0.3404 | 0.8581 | |
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| 0.2662 | 2.62 | 20000 | 0.3708 | 0.8566 | |
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| 0.2685 | 2.76 | 21000 | 0.3743 | 0.8591 | |
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| 0.246 | 2.89 | 22000 | 0.3786 | 0.8531 | |
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| 0.258 | 3.02 | 23000 | 0.3781 | 0.8578 | |
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| 0.2284 | 3.15 | 24000 | 0.3938 | 0.8583 | |
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| 0.2206 | 3.28 | 25000 | 0.4121 | 0.8583 | |
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| 0.2131 | 3.41 | 26000 | 0.4091 | 0.8575 | |
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| 0.2181 | 3.54 | 27000 | 0.4264 | 0.8535 | |
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| 0.2289 | 3.67 | 28000 | 0.3998 | 0.8568 | |
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| 0.2262 | 3.81 | 29000 | 0.3983 | 0.8580 | |
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| 0.2095 | 3.94 | 30000 | 0.4070 | 0.8568 | |
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### Framework versions |
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- Transformers 4.21.2 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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