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--- |
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library_name: transformers |
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base_model: michellejieli/emotion_text_classifier |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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model-index: |
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- name: results |
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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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# results |
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This model is a fine-tuned version of [michellejieli/emotion_text_classifier](https://huggingface.co/michellejieli/emotion_text_classifier) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2828 |
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- F1: 0.7879 |
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- Roc Auc: nan |
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- Hamming: 0.1039 |
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## Model description |
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This model uses a lightweight RoBERTa checkpoint that has been fine-tuned on evaluating emotions to further be trained on recognizing climate disinformation. |
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## Intended uses & limitations |
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To be used as a submission for the Frugal AI competition |
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## Training and evaluation data |
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Dataset of text and labels available on Frugal AI competition page. |
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## Training procedure |
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Used a binarizer to tokenize the text and found a seemingly suitable model checkpoint as a good place to start! |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 4 |
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### Training results |
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### Framework versions |
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- Transformers 4.47.1 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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