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rebert_v3 – AI Model by nikoslefkos | AlphaNeural AI
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nikoslefkos
/
rebert_v3
like
0
transformers
tf
distilbert
text-classification
generated_from_keras_callback
distilbert/distilbert-base-cased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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Model card
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nikoslefkos/rebert_trex_reformed_v3
This model is a fine-tuned version of
distilbert-base-cased
on trex for 250 labels. It achieves the following results on the evaluation set:
Train Loss: 0.4216
Train Accuracy: 0.8541
Validation Loss: 0.8042
Validation Accuracy: 0.7628
Epoch: 4
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': 0.01, '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': 1e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
training_precision: float32
Training results
Train Loss
Train Accuracy
Validation Loss
Validation Accuracy
Epoch
1.4168
0.6536
0.9242
0.7294
0
0.8272
0.7506
0.8106
0.7534
1
0.6786
0.7826
0.7871
0.7587
2
0.5718
0.8100
0.7981
0.7571
3
0.4216
0.8541
0.8042
0.7628
4
Framework versions
Transformers 4.34.0
TensorFlow 2.13.0
Datasets 2.14.5
Tokenizers 0.14.1
