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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_3epoch10.64 |
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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_3epoch10.64 |
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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: 2.0926 |
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- Accuracy: 0.7579 |
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- F1: 0.4615 |
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- Precision: 0.6372 |
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- Recall: 0.3618 |
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- Precision Sarcastic: 0.6372 |
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- Recall Sarcastic: 0.3618 |
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- F1 Sarcastic: 0.4615 |
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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: 64 |
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- eval_batch_size: 64 |
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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 | 44 | 2.1092 | 0.7550 | 0.4586 | 0.6261 | 0.3618 | 0.6261 | 0.3618 | 0.4586 | |
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| No log | 2.0 | 88 | 1.7332 | 0.7421 | 0.4559 | 0.5769 | 0.3769 | 0.5769 | 0.3769 | 0.4559 | |
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| No log | 3.0 | 132 | 1.9829 | 0.7392 | 0.4597 | 0.5662 | 0.3869 | 0.5662 | 0.3869 | 0.4597 | |
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| No log | 4.0 | 176 | 1.9446 | 0.7536 | 0.3915 | 0.6707 | 0.2764 | 0.6707 | 0.2764 | 0.3915 | |
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| No log | 5.0 | 220 | 1.6555 | 0.7594 | 0.4985 | 0.6194 | 0.4171 | 0.6194 | 0.4171 | 0.4985 | |
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| No log | 6.0 | 264 | 1.9983 | 0.7594 | 0.4261 | 0.6739 | 0.3116 | 0.6739 | 0.3116 | 0.4261 | |
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| No log | 7.0 | 308 | 1.9632 | 0.7622 | 0.4985 | 0.6308 | 0.4121 | 0.6308 | 0.4121 | 0.4985 | |
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| No log | 8.0 | 352 | 2.1204 | 0.7507 | 0.4055 | 0.6413 | 0.2965 | 0.6413 | 0.2965 | 0.4055 | |
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| No log | 9.0 | 396 | 2.0696 | 0.7637 | 0.4810 | 0.6496 | 0.3819 | 0.6496 | 0.3819 | 0.4810 | |
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| No log | 10.0 | 440 | 2.0926 | 0.7579 | 0.4615 | 0.6372 | 0.3618 | 0.6372 | 0.3618 | 0.4615 | |
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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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