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DistilBert-finetuned – AI Model by guldasta | AlphaNeural AI
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guldasta
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DistilBert-finetuned
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transformers
tf
distilbert
text-classification
generated_from_keras_callback
distilbert/distilbert-base-uncased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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DistilBert-finetuned
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: 0.1621
Train Sparse Categorical Accuracy: 0.9377
Validation Loss: 0.1815
Validation Sparse Categorical Accuracy: 0.9280
Epoch: 1
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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 5e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
training_precision: float32
Training results
Train Loss
Train Sparse Categorical Accuracy
Validation Loss
Validation Sparse Categorical Accuracy
Epoch
0.6861
0.7653
0.2095
0.9240
0
0.1621
0.9377
0.1815
0.9280
1
Framework versions
Transformers 4.45.1
TensorFlow 2.16.1
Datasets 3.0.1
Tokenizers 0.20.0
