| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: distilroberta-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: roberta-emotion-predictor |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # roberta-emotion-predictor |
|
|
| This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 2.2439 |
| - Macro F1: 0.3485 |
| - Weighted F1: 0.3644 |
| - Accuracy: 0.3689 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 64 |
| - eval_batch_size: 128 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 5 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Macro F1 | Weighted F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:| |
| | 2.4474 | 1.0 | 1625 | 2.4144 | 0.2943 | 0.3186 | 0.3269 | |
| | 2.261 | 2.0 | 3250 | 2.2982 | 0.3330 | 0.3523 | 0.3577 | |
| | 2.1515 | 3.0 | 4875 | 2.2638 | 0.3393 | 0.3567 | 0.3625 | |
| | 2.0117 | 4.0 | 6500 | 2.2536 | 0.3456 | 0.3634 | 0.3684 | |
| | 1.9649 | 5.0 | 8125 | 2.2569 | 0.3443 | 0.3630 | 0.3687 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.57.6 |
| - Pytorch 2.10.0+cu128 |
| - Datasets 2.14.7 |
| - Tokenizers 0.22.2 |
|
|
