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my_wnut_model – AI Model by danielcfox | AlphaNeural AI
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danielcfox
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my_wnut_model
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transformers
tf
distilbert
token-classification
generated_from_keras_callback
distilbert/distilbert-base-uncased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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danielcfox/my_wnut_model
This model is a fine-tuned version of
distilbert-base-uncased
on an unknown dataset. It achieves the following results on the evaluation set:
Train Loss: 0.1094
Validation Loss: 0.2448
Train Precision: 0.6503
Train Recall: 0.4426
Train F1: 0.5267
Train Accuracy: 0.9485
Epoch: 2
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': 636, '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
0.3421
0.2973
0.4769
0.1974
0.2792
0.9335
0
0.1456
0.2498
0.6686
0.4055
0.5048
0.9452
1
0.1094
0.2448
0.6503
0.4426
0.5267
0.9485
2
Framework versions
Transformers 4.35.0
TensorFlow 2.14.0
Datasets 2.14.6
Tokenizers 0.14.1
