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--- |
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license: mit |
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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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- precision |
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- recall |
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- f1 |
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base_model: roberta-base |
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model-index: |
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- name: run-4 |
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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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# run-4 |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.6296 |
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- Accuracy: 0.685 |
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- Precision: 0.6248 |
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- Recall: 0.6164 |
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- F1: 0.6188 |
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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: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 1.0195 | 1.0 | 50 | 0.8393 | 0.615 | 0.4126 | 0.5619 | 0.4606 | |
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| 0.7594 | 2.0 | 100 | 0.7077 | 0.7 | 0.6896 | 0.6663 | 0.6178 | |
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| 0.5515 | 3.0 | 150 | 0.9342 | 0.68 | 0.6334 | 0.5989 | 0.6016 | |
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| 0.3739 | 4.0 | 200 | 0.7755 | 0.735 | 0.7032 | 0.7164 | 0.7063 | |
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| 0.2648 | 5.0 | 250 | 0.9200 | 0.7 | 0.6584 | 0.6677 | 0.6611 | |
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| 0.1726 | 6.0 | 300 | 1.1898 | 0.71 | 0.6653 | 0.6550 | 0.6570 | |
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| 0.1452 | 7.0 | 350 | 1.5086 | 0.73 | 0.6884 | 0.6768 | 0.6812 | |
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| 0.0856 | 8.0 | 400 | 2.6159 | 0.68 | 0.6754 | 0.5863 | 0.5951 | |
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| 0.1329 | 9.0 | 450 | 1.9491 | 0.71 | 0.6692 | 0.6442 | 0.6463 | |
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| 0.0322 | 10.0 | 500 | 1.7897 | 0.74 | 0.6977 | 0.6939 | 0.6946 | |
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| 0.0345 | 11.0 | 550 | 1.9100 | 0.725 | 0.6827 | 0.6853 | 0.6781 | |
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| 0.026 | 12.0 | 600 | 2.5041 | 0.68 | 0.6246 | 0.6115 | 0.6137 | |
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| 0.0084 | 13.0 | 650 | 2.5343 | 0.715 | 0.6708 | 0.6617 | 0.6637 | |
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| 0.0145 | 14.0 | 700 | 2.4112 | 0.715 | 0.6643 | 0.6595 | 0.6614 | |
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| 0.0119 | 15.0 | 750 | 2.5303 | 0.705 | 0.6479 | 0.6359 | 0.6390 | |
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| 0.0026 | 16.0 | 800 | 2.6299 | 0.705 | 0.6552 | 0.6447 | 0.6455 | |
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| 0.0077 | 17.0 | 850 | 2.4044 | 0.715 | 0.6667 | 0.6576 | 0.6596 | |
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| 0.0055 | 18.0 | 900 | 2.8077 | 0.68 | 0.6208 | 0.6065 | 0.6098 | |
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| 0.0078 | 19.0 | 950 | 2.5608 | 0.68 | 0.6200 | 0.6104 | 0.6129 | |
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| 0.0018 | 20.0 | 1000 | 2.6296 | 0.685 | 0.6248 | 0.6164 | 0.6188 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.1+cu116 |
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- Tokenizers 0.13.2 |
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