Maxnotmarx/diaster_detection_model
This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.1309
- Train Accuracy: 0.9618
- Epoch: 7
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': '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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2375, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
Training results
Train Loss | Train Accuracy | Epoch |
---|---|---|
0.1317 | 0.9618 | 0 |
0.1317 | 0.9620 | 1 |
0.1332 | 0.9618 | 2 |
0.1307 | 0.9618 | 3 |
0.1314 | 0.9618 | 4 |
0.1321 | 0.9620 | 5 |
0.1316 | 0.9620 | 6 |
0.1309 | 0.9618 | 7 |
Framework versions
- Transformers 4.44.0
- TensorFlow 2.16.1
- Datasets 2.21.0
- Tokenizers 0.19.1
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Model tree for Maxnotmarx/diaster_detection_model
Base model
distilbert/distilbert-base-uncased