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conll_test – AI Model by jborras18 | AlphaNeural AI
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jborras18
/
conll_test
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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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jborras18/conll_test
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: 0.0271
Validation Loss: 0.0483
Train Precision: 0.9252
Train Recall: 0.9382
Train F1: 0.9317
Train Accuracy: 0.9868
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': 2631, '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
0.1611
0.0606
0.8987
0.9164
0.9074
0.9830
0
0.0427
0.0479
0.9227
0.9360
0.9293
0.9867
1
0.0271
0.0483
0.9252
0.9382
0.9317
0.9868
2
Framework versions
Transformers 4.26.1
TensorFlow 2.11.0
Datasets 2.9.0
Tokenizers 0.13.2
