End of training
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README.md
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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.
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- Accuracy: 0.
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- F1: 0.
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- Auc Roc: 0.
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- Log Loss: 0.
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## Model description
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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:
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- eval_batch_size:
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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: 6
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Auc Roc | Log Loss |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:-------:|:--------:|
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| 0.1517 | 6.0 | 750 | 0.5072 | 0.8864 | 0.6743 | 0.9227 | 0.5072 |
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### Framework versions
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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: 0.4572
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- Accuracy: 0.8168
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- F1: 0.8066
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- Auc Roc: 0.9030
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- Log Loss: 0.4572
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## Model description
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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: 32
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- eval_batch_size: 20
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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: 5
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Auc Roc | Log Loss |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:-------:|:--------:|
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| 0.6676 | 1.0 | 26 | 0.5624 | 0.7330 | 0.7692 | 0.8710 | 0.5624 |
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| 0.4428 | 2.0 | 52 | 0.4274 | 0.8115 | 0.8065 | 0.8901 | 0.4274 |
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| 0.2698 | 3.0 | 78 | 0.4124 | 0.8168 | 0.8148 | 0.9091 | 0.4124 |
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| 0.1714 | 4.0 | 104 | 0.4655 | 0.8115 | 0.8105 | 0.8991 | 0.4655 |
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| 0.1088 | 5.0 | 130 | 0.4572 | 0.8168 | 0.8066 | 0.9030 | 0.4572 |
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### Framework versions
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config.json
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"pad_token_id": 0,
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"problem_type": "single_label_classification",
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"torch_dtype": "float32",
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"pad_token_id": 0,
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"problem_type": "single_label_classification",
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"qa_dropout": 0.1,
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"seq_classif_dropout": 0.3,
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