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my_awesome_wnut_model – AI Model by rkulathumani | AlphaNeural AI
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rkulathumani
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my_awesome_wnut_model
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transformers
tf
distilbert
token-classification
generated_from_keras_callback
apache-2.0
autotrain_compatible
endpoints_compatible
us
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rkulathumani/my_awesome_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: 0.1270
Validation Loss: 0.2652
Train Precision: 0.5982
Train Recall: 0.3971
Train F1: 0.4774
Train Accuracy: 0.9447
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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 636, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, '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
0.3311
0.3183
0.4125
0.1184
0.1840
0.9297
0
0.1630
0.2787
0.5688
0.3708
0.4490
0.9427
1
0.1270
0.2652
0.5982
0.3971
0.4774
0.9447
2
Framework versions
Transformers 4.26.1
TensorFlow 2.11.0
Datasets 2.10.1
Tokenizers 0.13.2
