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nlp-esg-scoring/bert-base-finetuned-esg-a4s-clean
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 2.5224
- Validation Loss: 2.2196
- Epoch: 9
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: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -824, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'passive_serialization': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Epoch |
|---|---|---|
| 2.5170 | 2.3060 | 0 |
| 2.5229 | 2.3220 | 1 |
| 2.5077 | 2.3155 | 2 |
| 2.5059 | 2.3151 | 3 |
| 2.5052 | 2.2596 | 4 |
| 2.5250 | 2.4044 | 5 |
| 2.5120 | 2.2901 | 6 |
| 2.5042 | 2.2847 | 7 |
| 2.4972 | 2.3168 | 8 |
| 2.5224 | 2.2196 | 9 |
Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
