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--- |
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license: cc-by-nc-4.0 |
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base_model: mental/mental-roberta-base |
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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: 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 [mental/mental-roberta-base](https://huggingface.co/mental/mental-roberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7715 |
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- Accuracy: 0.8014 |
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- F1: 0.8161 |
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- Precision: 0.7816 |
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- Recall: 0.8537 |
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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: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 64 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 8 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.3921 | 0.99 | 31 | 0.4379 | 0.8042 | 0.8153 | 0.7943 | 0.8374 | |
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| 0.3376 | 1.98 | 62 | 0.4358 | 0.8112 | 0.8173 | 0.8162 | 0.8184 | |
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| 0.3126 | 2.98 | 93 | 0.4642 | 0.7972 | 0.8172 | 0.7642 | 0.8780 | |
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| 0.2838 | 4.0 | 125 | 0.4438 | 0.8196 | 0.8264 | 0.8209 | 0.8320 | |
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| 0.2504 | 4.99 | 156 | 0.5249 | 0.7958 | 0.8161 | 0.7624 | 0.8780 | |
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| 0.2912 | 5.98 | 187 | 0.6067 | 0.7972 | 0.8221 | 0.7511 | 0.9079 | |
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| 0.1335 | 6.98 | 218 | 0.7014 | 0.8 | 0.8197 | 0.7665 | 0.8808 | |
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| 0.1579 | 7.94 | 248 | 0.7715 | 0.8014 | 0.8161 | 0.7816 | 0.8537 | |
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### Framework versions |
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- Transformers 4.39.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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