| --- |
| library_name: transformers |
| license: mit |
| base_model: roberta-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: twitter-roberta-base-sentiment |
| 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. --> |
|
|
| # twitter-roberta-base-sentiment |
|
|
| This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.9462 |
| - Accuracy: 0.7222 |
| - Macro Precision: 0.7068 |
| - Macro Recall: 0.7491 |
| - Macro F1: 0.7246 |
|
|
| ## 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: 5e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 16 |
| - 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: linear |
| - num_epochs: 2 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro Precision | Macro Recall | Macro F1 | |
| |:-------------:|:------:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:| |
| | 0.9337 | 0.2667 | 1000 | 0.8398 | 0.6273 | 0.6577 | 0.6723 | 0.6322 | |
| | 0.8101 | 0.5333 | 2000 | 0.7526 | 0.6780 | 0.6598 | 0.7406 | 0.6851 | |
| | 0.7097 | 0.8 | 3000 | 0.8075 | 0.7068 | 0.6853 | 0.7515 | 0.7081 | |
| | 0.5513 | 1.0667 | 4000 | 0.8310 | 0.7113 | 0.7007 | 0.7316 | 0.7135 | |
| | 0.4368 | 1.3333 | 5000 | 0.9000 | 0.7154 | 0.7001 | 0.7487 | 0.7192 | |
| | 0.4084 | 1.6 | 6000 | 0.9042 | 0.7154 | 0.7035 | 0.7413 | 0.7194 | |
| | 0.3481 | 1.8667 | 7000 | 0.9868 | 0.7246 | 0.7121 | 0.7441 | 0.7255 | |
| | 0.3693 | 2.0 | 7500 | 0.9462 | 0.7222 | 0.7068 | 0.7491 | 0.7246 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 5.9.0 |
| - Pytorch 2.11.0+cu128 |
| - Datasets 4.8.5 |
| - Tokenizers 0.22.2 |
|
|
