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bert-base-uncased_text_detection – AI Model by hmcgovern | AlphaNeural AI
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hmcgovern
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bert-base-uncased_text_detection
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generated_from_trainer
google-bert/bert-base-uncased
finetune
apache-2.0
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bert-base-uncased_text_detection
This model is a fine-tuned version of
bert-base-uncased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.0825
Accuracy: 0.9744
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: 0.0001
train_batch_size: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 4
total_train_batch_size: 128
total_eval_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 3
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
0.3842
1.0
96
0.3456
0.8564
0.123
1.99
192
0.1073
0.9634
0.0869
2.99
288
0.0825
0.9744
Framework versions
Transformers 4.32.0.dev0
Pytorch 2.0.1+cu117
Datasets 2.14.4
Tokenizers 0.13.3
