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roberta-base-finetuned-for-IoC-Extracting – AI Model by Reza-Barati | AlphaNeural AI
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Reza-Barati
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roberta-base-finetuned-for-IoC-Extracting
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transformers
tf
tensorboard
roberta
token-classification
generated_from_keras_callback
FacebookAI/roberta-base
finetune
mit
autotrain_compatible
endpoints_compatible
us
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Reza-Barati/roberta-base-finetuned-for-IoC-Extracting
This model is a fine-tuned version of
roberta-base
on an unknown dataset. It achieves the following results on the evaluation set:
Train Loss: 0.1053
Validation Loss: 0.0568
Train Precision: 0.8956
Train Recall: 0.9257
Train F1: 0.9104
Train Accuracy: 0.9804
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': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 213432, '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
0.1053
0.0568
0.8956
0.9257
0.9104
0.9804
0
Framework versions
Transformers 4.38.2
TensorFlow 2.15.0
Datasets 2.18.0
Tokenizers 0.15.2
