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--- |
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library_name: transformers |
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license: mit |
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base_model: cardiffnlp/twitter-roberta-large-hate-latest |
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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: twitter-roberta-large-hate-latest-profanity-mr |
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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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# twitter-roberta-large-hate-latest-profanity-mr |
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This model is a fine-tuned version of [cardiffnlp/twitter-roberta-large-hate-latest](https://huggingface.co/cardiffnlp/twitter-roberta-large-hate-latest) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3265 |
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- Accuracy: 0.9035 |
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- Precision: 0.4517 |
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- Recall: 0.5 |
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- F1: 0.4746 |
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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: 128 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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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: 10 |
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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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| 0.3253 | 0.9836 | 30 | 0.3848 | 0.8819 | 0.4410 | 0.5 | 0.4686 | |
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| 0.3423 | 2.0 | 61 | 0.3673 | 0.8819 | 0.4410 | 0.5 | 0.4686 | |
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| 0.3399 | 2.9836 | 91 | 0.3823 | 0.8819 | 0.4410 | 0.5 | 0.4686 | |
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| 0.3248 | 4.0 | 122 | 0.3630 | 0.8819 | 0.4410 | 0.5 | 0.4686 | |
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| 0.3 | 4.9836 | 152 | 0.3922 | 0.8819 | 0.4410 | 0.5 | 0.4686 | |
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| 0.3094 | 6.0 | 183 | 0.3655 | 0.8819 | 0.4410 | 0.5 | 0.4686 | |
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| 0.3009 | 6.9836 | 213 | 0.3835 | 0.8819 | 0.4410 | 0.5 | 0.4686 | |
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| 0.2442 | 8.0 | 244 | 0.4946 | 0.7904 | 0.5976 | 0.6514 | 0.6105 | |
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| 0.1438 | 8.9836 | 274 | 0.3878 | 0.8699 | 0.6590 | 0.5992 | 0.6179 | |
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| 0.1372 | 9.8361 | 300 | 0.3606 | 0.8723 | 0.6665 | 0.6006 | 0.6207 | |
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### Framework versions |
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- Transformers 4.45.1 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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