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distilbert-finetuned-cisco – AI Model by MiguelCosta | AlphaNeural AI
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MiguelCosta
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distilbert-finetuned-cisco
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transformers
tf
distilbert
fill-mask
generated_from_keras_callback
apache-2.0
autotrain_compatible
endpoints_compatible
us
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MiguelCosta/distilbert-finetuned-cisco
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: 4.4181
Validation Loss: 4.2370
Epoch: 0
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': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -964, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '
passive_serialization
': True}, 'warmup_steps': 1000, 'power': 1.0, '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
Epoch
4.4181
4.2370
0
Framework versions
Transformers 4.22.1
TensorFlow 2.8.2
Datasets 2.4.0
Tokenizers 0.12.1
