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--- |
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base_model: microsoft/codebert-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: CodeBert-finetuned-the-stack-bash |
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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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# CodeBert-finetuned-the-stack-bash |
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This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.0540 |
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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: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 4 |
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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: 100 |
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- training_steps: 10000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 4.3461 | 0.05 | 500 | 4.6252 | |
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| 3.028 | 0.1 | 1000 | 3.2745 | |
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| 2.978 | 0.15 | 1500 | 2.8286 | |
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| 3.2116 | 0.2 | 2000 | 2.5426 | |
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| 2.9872 | 0.25 | 2500 | 2.4032 | |
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| 2.5853 | 0.3 | 3000 | 2.3376 | |
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| 2.6977 | 0.35 | 3500 | 2.2992 | |
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| 2.5997 | 0.4 | 4000 | 2.2394 | |
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| 2.3712 | 0.45 | 4500 | 2.1834 | |
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| 2.1944 | 0.5 | 5000 | 2.1589 | |
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| 2.4029 | 0.55 | 5500 | 2.1265 | |
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| 2.3307 | 0.6 | 6000 | 2.1207 | |
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| 2.45 | 0.65 | 6500 | 2.0951 | |
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| 2.3575 | 0.7 | 7000 | 2.0987 | |
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| 2.3894 | 0.75 | 7500 | 2.0740 | |
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| 2.741 | 0.8 | 8000 | 2.0610 | |
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| 1.6001 | 0.85 | 8500 | 2.0542 | |
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| 2.5929 | 0.9 | 9000 | 2.0488 | |
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| 2.0965 | 0.95 | 9500 | 2.0473 | |
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| 2.6814 | 1.0 | 10000 | 2.0540 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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