End of training
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- model.safetensors +1 -1
README.md
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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: 0.
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- Accuracy: 0.
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- F1: 0.
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- Precision: 0.
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- Recall: 0.
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## Model description
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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:
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- eval_batch_size: 8
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- seed: 4711
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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:
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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### Framework versions
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- Transformers 4.
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- Pytorch 2.
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- Datasets 2.
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- Tokenizers 0.15.
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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: 0.6534
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- Accuracy: 0.7342
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- F1: 0.7413
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- Precision: 0.7066
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- Recall: 0.7795
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## Model description
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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: 8
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- eval_batch_size: 8
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- seed: 4711
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 32
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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: 5
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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| 0.6396 | 1.0 | 996 | 0.5277 | 0.6905 | 0.6502 | 0.7258 | 0.5889 |
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| 0.4862 | 2.0 | 1993 | 0.5331 | 0.7176 | 0.7393 | 0.6733 | 0.8196 |
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| 0.4043 | 3.0 | 2989 | 0.5521 | 0.7339 | 0.7343 | 0.7167 | 0.7528 |
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| 0.3439 | 4.0 | 3986 | 0.5945 | 0.7357 | 0.7422 | 0.7087 | 0.7790 |
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| 0.2946 | 5.0 | 4980 | 0.6534 | 0.7342 | 0.7413 | 0.7066 | 0.7795 |
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### Framework versions
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- Transformers 4.37.2
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- Pytorch 2.2.0+cu121
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- Datasets 2.17.1
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- Tokenizers 0.15.2
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model.safetensors
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