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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_3epoch |
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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_3epoch |
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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: 0.9201 |
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- Accuracy: 0.7723 |
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- F1: 0.5183 |
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- Precision: 0.6589 |
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- Recall: 0.4271 |
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- Precision Sarcastic: 0.6589 |
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- Recall Sarcastic: 0.4271 |
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- F1 Sarcastic: 0.5183 |
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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: 2e-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: 3 |
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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 | 0.6210 | 0.7248 | 0.1116 | 0.75 | 0.0603 | 0.75 | 0.0603 | 0.1116 | |
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| No log | 2.0 | 348 | 0.5732 | 0.7767 | 0.5016 | 0.6964 | 0.3920 | 0.6964 | 0.3920 | 0.5016 | |
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| 0.3415 | 3.0 | 522 | 0.9201 | 0.7723 | 0.5183 | 0.6589 | 0.4271 | 0.6589 | 0.4271 | 0.5183 | |
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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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