JuvManga-87/mobilebert-uncased-finetuned-ner
This model is a fine-tuned version of google/mobilebert-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.2691
- Validation Loss: 0.2428
- Train Precision: 0.6071
- Train Recall: 0.0424
- Train F1: 0.0793
- Train Accuracy: 0.9240
- Epoch: 11
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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 264, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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 | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
---|---|---|---|---|---|---|
1.2686 | 0.5142 | 0.0187 | 0.0125 | 0.0149 | 0.8729 | 0 |
0.4703 | 0.4127 | 0.02 | 0.0050 | 0.0080 | 0.9031 | 1 |
0.4033 | 0.3659 | 0.0588 | 0.0050 | 0.0092 | 0.9156 | 2 |
0.3657 | 0.3351 | 0.1429 | 0.0050 | 0.0096 | 0.9195 | 3 |
0.3434 | 0.3111 | 0.2143 | 0.0075 | 0.0145 | 0.9199 | 4 |
0.3223 | 0.2917 | 0.2667 | 0.0100 | 0.0192 | 0.9203 | 5 |
0.3035 | 0.2764 | 0.3125 | 0.0125 | 0.0240 | 0.9209 | 6 |
0.2916 | 0.2642 | 0.5294 | 0.0224 | 0.0431 | 0.9224 | 7 |
0.2780 | 0.2551 | 0.4762 | 0.0249 | 0.0474 | 0.9226 | 8 |
0.2755 | 0.2484 | 0.56 | 0.0349 | 0.0657 | 0.9234 | 9 |
0.2718 | 0.2442 | 0.6071 | 0.0424 | 0.0793 | 0.9240 | 10 |
0.2691 | 0.2428 | 0.6071 | 0.0424 | 0.0793 | 0.9240 | 11 |
Framework versions
- Transformers 4.42.4
- TensorFlow 2.17.0
- Datasets 2.21.0
- Tokenizers 0.19.1
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Model tree for JuvManga-87/mobilebert-uncased-finetuned-ner
Base model
google/mobilebert-uncased