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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: 0.7861 |
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- Accuracy: 0.7427 |
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- Precision: 0.4604 |
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- Recall: 0.3928 |
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- F1: 0.3901 |
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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: 5 |
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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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| 0.8805 | 0.9968 | 157 | 0.8450 | 0.7339 | 0.3531 | 0.3488 | 0.3407 | |
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| 0.7579 | 2.0 | 315 | 0.7816 | 0.7607 | 0.5057 | 0.4154 | 0.4200 | |
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| 0.7177 | 2.9968 | 472 | 0.7848 | 0.7571 | 0.4702 | 0.4043 | 0.4209 | |
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| 0.6914 | 4.0 | 630 | 0.8011 | 0.7446 | 0.4242 | 0.4029 | 0.4077 | |
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| 0.5218 | 4.9841 | 785 | 0.8166 | 0.7429 | 0.4122 | 0.4146 | 0.4115 | |
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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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