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--- |
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library_name: transformers |
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base_model: facebook/roberta-hate-speech-dynabench-r4-target |
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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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model-index: |
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- name: results |
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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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# results |
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This model is a fine-tuned version of [facebook/roberta-hate-speech-dynabench-r4-target](https://huggingface.co/facebook/roberta-hate-speech-dynabench-r4-target) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1543 |
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- Accuracy: 0.975 |
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- Precision: 0.9761 |
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- Recall: 0.975 |
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- F1: 0.9750 |
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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: 3e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 8 |
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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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- lr_scheduler_warmup_steps: 5 |
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- num_epochs: 10 |
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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 | Precision | Recall | F1 | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.4904 | 0.9895 | 59 | 0.3492 | 0.9 | 0.9015 | 0.9 | 0.8997 | |
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| 0.1344 | 1.9958 | 119 | 0.3267 | 0.9333 | 0.9374 | 0.9333 | 0.9330 | |
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| 0.0614 | 2.9853 | 178 | 0.2695 | 0.9333 | 0.9339 | 0.9333 | 0.9334 | |
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| 0.041 | 3.9916 | 238 | 0.2203 | 0.9583 | 0.9614 | 0.9583 | 0.9582 | |
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| 0.0674 | 4.9979 | 298 | 0.2079 | 0.9667 | 0.9687 | 0.9667 | 0.9666 | |
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| 0.0006 | 5.9874 | 357 | 0.1543 | 0.975 | 0.9761 | 0.975 | 0.9750 | |
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| 0.0004 | 6.9937 | 417 | 0.1883 | 0.975 | 0.9751 | 0.975 | 0.9750 | |
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| 0.0002 | 8.0 | 477 | 0.1628 | 0.9667 | 0.9667 | 0.9667 | 0.9667 | |
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| 0.0001 | 8.9895 | 536 | 0.2980 | 0.9667 | 0.9687 | 0.9667 | 0.9666 | |
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| 0.0001 | 9.8952 | 590 | 0.2377 | 0.975 | 0.9761 | 0.975 | 0.9750 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.5.0+cu121 |
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- Datasets 3.0.2 |
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- Tokenizers 0.19.1 |
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