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fine-tuned-cybersecurity-ner – AI Model by Abiral7 | AlphaNeural AI
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Abiral7
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fine-tuned-cybersecurity-ner
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transformers
pytorch
tensorboard
distilbert
token-classification
generated_from_trainer
apache-2.0
autotrain_compatible
endpoints_compatible
us
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fine-tuned-cybersecurity-ner
This model is a fine-tuned version of
distilbert-base-uncased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.1511
Precision: 0.4963
Recall: 0.4979
F1: 0.4971
Accuracy: 0.9566
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:
learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2
Training results
Training Loss
Epoch
Step
Validation Loss
Precision
Recall
F1
Accuracy
No log
1.0
176
0.1648
0.5069
0.4316
0.4662
0.9532
No log
2.0
352
0.1511
0.4963
0.4979
0.4971
0.9566
Framework versions
Transformers 4.28.1
Pytorch 2.0.0+cu118
Datasets 2.11.0
Tokenizers 0.13.3
