<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. -->
robertuito-HUHU-task2b
This model is a fine-tuned version of pysentimiento/robertuito-base-uncased for the HUHU Shared Task at IberLEF 2023. It was trained on a partition of the train set provided by the organizers.
Model description
This model is a fine-tuned version of pysentimiento/robertuito-base-uncased for the regression task of predicting on a continuous scale from 1 to 5 how prejudicial a tweet (considered to be hurtful or conveying prejudice in some way) is on average among minority groups.
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Epoch |
|---|---|
| 1.2750 | 1 |
| 0.4855 | 2 |
| 0.3276 | 3 |
| 0.1917 | 4 |
Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Tokenizers 0.13.3
