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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-hinglish-binary |
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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-hinglish-binary |
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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: 0.8288 |
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- Accuracy: 0.6779 |
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- Precision: 0.6455 |
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- Recall: 0.6188 |
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- F1: 0.6218 |
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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: 4 |
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- total_train_batch_size: 128 |
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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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| 0.6392 | 0.9709 | 25 | 0.6656 | 0.6376 | 0.3188 | 0.5 | 0.3894 | |
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| 0.63 | 1.9806 | 51 | 0.7095 | 0.6403 | 0.8197 | 0.5038 | 0.3975 | |
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| 0.6117 | 2.9903 | 77 | 0.6043 | 0.6921 | 0.6720 | 0.6174 | 0.6176 | |
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| 0.5974 | 4.0 | 103 | 0.6540 | 0.6730 | 0.6787 | 0.5667 | 0.5369 | |
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| 0.5512 | 4.9709 | 128 | 0.6216 | 0.6948 | 0.6745 | 0.6228 | 0.6243 | |
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| 0.5162 | 5.9806 | 154 | 0.6164 | 0.7057 | 0.6873 | 0.6394 | 0.6438 | |
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| 0.4525 | 6.9903 | 180 | 0.6715 | 0.6948 | 0.6757 | 0.6211 | 0.6221 | |
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| 0.4207 | 8.0 | 206 | 0.7581 | 0.7084 | 0.6844 | 0.6562 | 0.6620 | |
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| 0.3381 | 8.9709 | 231 | 0.7439 | 0.6812 | 0.6560 | 0.6575 | 0.6567 | |
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| 0.3016 | 9.7087 | 250 | 0.7481 | 0.7030 | 0.6762 | 0.6649 | 0.6688 | |
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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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