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lwin_winery – AI Model by Pavan-124 | AlphaNeural AI
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Pavan-124
/
lwin_winery
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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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Pavan-124/lwin_winery
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: 0.0354
Validation Loss: 0.0980
Train Precision: 0.8918
Train Recall: 0.8986
Train F1: 0.8952
Train Accuracy: 0.9696
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': 5724, '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
0.1279
0.0885
0.8696
0.8806
0.8751
0.9650
0
0.0613
0.0873
0.8828
0.8924
0.8876
0.9681
1
0.0354
0.0980
0.8918
0.8986
0.8952
0.9696
2
Framework versions
Transformers 4.35.2
TensorFlow 2.14.0
Datasets 2.16.1
Tokenizers 0.15.0
