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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: F_Roberta_classifier2 |
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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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# F_Roberta_classifier2 |
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This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1317 |
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- Accuracy: 0.9751 |
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- F1: 0.9751 |
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- Precision: 0.9751 |
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- Recall: 0.9751 |
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- C Report: precision recall f1-score support |
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0 0.97 0.98 0.98 1467 |
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1 0.98 0.97 0.98 1466 |
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accuracy 0.98 2933 |
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macro avg 0.98 0.98 0.98 2933 |
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weighted avg 0.98 0.98 0.98 2933 |
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- C Matrix: None |
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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: 32 |
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- eval_batch_size: 32 |
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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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- 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 | C Report | C Matrix | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------:| |
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| 0.1626 | 1.0 | 614 | 0.0936 | 0.9707 | 0.9707 | 0.9707 | 0.9707 | precision recall f1-score support |
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0 0.97 0.97 0.97 1467 |
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1 0.97 0.97 0.97 1466 |
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accuracy 0.97 2933 |
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macro avg 0.97 0.97 0.97 2933 |
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weighted avg 0.97 0.97 0.97 2933 |
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| None | |
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| 0.0827 | 2.0 | 1228 | 0.0794 | 0.9731 | 0.9731 | 0.9731 | 0.9731 | precision recall f1-score support |
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0 0.96 0.98 0.97 1467 |
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1 0.98 0.96 0.97 1466 |
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accuracy 0.97 2933 |
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macro avg 0.97 0.97 0.97 2933 |
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weighted avg 0.97 0.97 0.97 2933 |
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| None | |
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| 0.0525 | 3.0 | 1842 | 0.1003 | 0.9737 | 0.9737 | 0.9737 | 0.9737 | precision recall f1-score support |
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0 0.97 0.98 0.97 1467 |
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1 0.98 0.97 0.97 1466 |
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accuracy 0.97 2933 |
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macro avg 0.97 0.97 0.97 2933 |
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weighted avg 0.97 0.97 0.97 2933 |
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| None | |
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| 0.0329 | 4.0 | 2456 | 0.1184 | 0.9751 | 0.9751 | 0.9751 | 0.9751 | precision recall f1-score support |
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0 0.98 0.97 0.98 1467 |
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1 0.97 0.98 0.98 1466 |
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accuracy 0.98 2933 |
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macro avg 0.98 0.98 0.98 2933 |
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weighted avg 0.98 0.98 0.98 2933 |
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| None | |
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| 0.0179 | 5.0 | 3070 | 0.1317 | 0.9751 | 0.9751 | 0.9751 | 0.9751 | precision recall f1-score support |
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0 0.97 0.98 0.98 1467 |
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1 0.98 0.97 0.98 1466 |
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accuracy 0.98 2933 |
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macro avg 0.98 0.98 0.98 2933 |
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weighted avg 0.98 0.98 0.98 2933 |
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| None | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.2.0 |
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- Tokenizers 0.12.1 |
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