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hateBERT-finetuned-cnn – AI Model by viditnaik | AlphaNeural AI
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viditnaik
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hateBERT-finetuned-cnn
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0
transformers
tf
bert
fill-mask
generated_from_keras_callback
GroNLP/hateBERT
finetune
autotrain_compatible
endpoints_compatible
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viditnaik/hateBERT-finetuned-cnn
This model is a fine-tuned version of
GroNLP/hateBERT
on an unknown dataset. It achieves the following results on the evaluation set:
Train Loss: 3.4700
Validation Loss: 3.0616
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': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 562, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
training_precision: mixed_float16
Training results
Train Loss
Validation Loss
Epoch
3.4700
3.0616
0
Framework versions
Transformers 4.35.0
TensorFlow 2.14.0
Datasets 2.14.6
Tokenizers 0.14.1
