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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-offensive-eval-kn |
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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-offensive-eval-kn |
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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: 0.8543 |
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- Accuracy: 0.7391 |
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- Precision: 0.4215 |
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- Recall: 0.3968 |
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- F1: 0.4020 |
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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: 1e-05 |
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- train_batch_size: 16 |
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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: 32 |
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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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| 0.9082 | 0.9968 | 157 | 0.8529 | 0.7286 | 0.3746 | 0.3354 | 0.3312 | |
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| 0.7593 | 2.0 | 315 | 0.7818 | 0.7393 | 0.5160 | 0.3778 | 0.3767 | |
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| 0.7264 | 2.9968 | 472 | 0.7640 | 0.7464 | 0.4450 | 0.3812 | 0.3861 | |
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| 0.6998 | 4.0 | 630 | 0.7941 | 0.7464 | 0.4461 | 0.4106 | 0.4218 | |
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| 0.5066 | 4.9968 | 787 | 0.8636 | 0.7518 | 0.4668 | 0.4156 | 0.4276 | |
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| 0.5164 | 6.0 | 945 | 0.8747 | 0.7482 | 0.4391 | 0.4342 | 0.4342 | |
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| 0.4098 | 6.9968 | 1102 | 0.9078 | 0.7446 | 0.4366 | 0.4324 | 0.4334 | |
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| 0.3556 | 8.0 | 1260 | 0.9286 | 0.7393 | 0.4279 | 0.4304 | 0.4282 | |
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| 0.3974 | 8.9968 | 1417 | 0.9444 | 0.7446 | 0.4434 | 0.4406 | 0.4411 | |
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| 0.318 | 9.9683 | 1570 | 0.9597 | 0.7411 | 0.4352 | 0.4370 | 0.4352 | |
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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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