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--- |
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base_model: cardiffnlp/twitter-roberta-base-irony |
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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: twitter-roberta-base_3epoch10.16 |
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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-base_3epoch10.16 |
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This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-irony](https://huggingface.co/cardiffnlp/twitter-roberta-base-irony) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.1539 |
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- Accuracy: 0.7478 |
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- F1: 0.4224 |
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- Precision: 0.6154 |
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- Recall: 0.3216 |
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- Precision Sarcastic: 0.6154 |
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- Recall Sarcastic: 0.3216 |
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- F1 Sarcastic: 0.4224 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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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 | F1 | Precision | Recall | Precision Sarcastic | Recall Sarcastic | F1 Sarcastic | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------------:|:----------------:|:------------:| |
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| No log | 1.0 | 174 | 2.1148 | 0.7493 | 0.2869 | 0.7778 | 0.1759 | 0.7778 | 0.1759 | 0.2869 | |
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| No log | 2.0 | 348 | 1.2416 | 0.7320 | 0.3882 | 0.5619 | 0.2965 | 0.5619 | 0.2965 | 0.3882 | |
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| 0.0732 | 3.0 | 522 | 1.5725 | 0.7392 | 0.4873 | 0.5584 | 0.4322 | 0.5584 | 0.4322 | 0.4873 | |
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| 0.0732 | 4.0 | 696 | 1.7604 | 0.7450 | 0.4520 | 0.5887 | 0.3668 | 0.5887 | 0.3668 | 0.4520 | |
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| 0.0732 | 5.0 | 870 | 1.9529 | 0.7291 | 0.4749 | 0.5346 | 0.4271 | 0.5346 | 0.4271 | 0.4749 | |
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| 0.0278 | 6.0 | 1044 | 1.7258 | 0.7334 | 0.4699 | 0.5467 | 0.4121 | 0.5467 | 0.4121 | 0.4699 | |
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| 0.0278 | 7.0 | 1218 | 2.0437 | 0.7464 | 0.4568 | 0.592 | 0.3719 | 0.592 | 0.3719 | 0.4568 | |
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| 0.0278 | 8.0 | 1392 | 2.0771 | 0.7507 | 0.4508 | 0.6121 | 0.3568 | 0.6121 | 0.3568 | 0.4508 | |
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| 0.0081 | 9.0 | 1566 | 2.1414 | 0.7478 | 0.4186 | 0.6176 | 0.3166 | 0.6176 | 0.3166 | 0.4186 | |
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| 0.0081 | 10.0 | 1740 | 2.1539 | 0.7478 | 0.4224 | 0.6154 | 0.3216 | 0.6154 | 0.3216 | 0.4224 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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