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hasan-mr/t5-small-finetuned-billsum-summarization
This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: nan
- Validation Loss: nan
- Train Rougel: tf.Tensor(0.0, shape=(), dtype=float32)
- Epoch: 3
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:
- optimizer: {'inner_optimizer': {'module': 'transformers.optimization_tf', 'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': 1.9999999494757503e-05, 'decay': 0.0, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}, 'registered_name': 'AdamWeightDecay'}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
Training results
| Train Loss | Validation Loss | Train Rougel | Epoch |
|---|---|---|---|
| nan | nan | tf.Tensor(0.0, shape=(), dtype=float32) | 0 |
| nan | nan | tf.Tensor(0.0, shape=(), dtype=float32) | 1 |
| nan | nan | tf.Tensor(0.0, shape=(), dtype=float32) | 2 |
| nan | nan | tf.Tensor(0.0, shape=(), dtype=float32) | 3 |
Framework versions
- Transformers 4.34.0
- TensorFlow 2.14.0
- Datasets 2.14.5
- Tokenizers 0.14.1
