roberta-base_corona_nlp_classif
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5166
Model description
This model is used to classify tweets regarding the COVID-19 as Extremely Positive, Positive, Neutral,Negative, Extremely Negative
Intended uses & limitations
Training is done on a raw uncleaned dataset.
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.6501 | 1.0 | 4496 | 0.6886 |
| 0.4461 | 2.0 | 8992 | 0.5166 |
| 0.3347 | 3.0 | 13488 | 0.6570 |
| 0.152 | 4.0 | 17984 | 0.6583 |
Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3