thinhkosay
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End of training
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README.md
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This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-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.
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- Precision: 0.
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- Recall: 0.
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- F1: 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:
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size:
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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_ratio: 0.1
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
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|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|
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| 0.
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### Framework versions
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This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3184
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- Precision: 0.8894
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- Recall: 0.8897
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- F1: 0.8894
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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: 16
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- eval_batch_size: 16
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- seed: 42
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- gradient_accumulation_steps: 2
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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: cosine
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- lr_scheduler_warmup_ratio: 0.1
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
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|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|
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| 0.3301 | 0.9990 | 512 | 0.3780 | 0.8509 | 0.8697 | 0.8514 |
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| 0.3103 | 2.0 | 1025 | 0.2916 | 0.8907 | 0.8848 | 0.8870 |
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| 0.1607 | 2.9971 | 1536 | 0.3184 | 0.8894 | 0.8897 | 0.8894 |
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### Framework versions
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