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bert-base-uncased-finetuned-boolq – AI Model by crzhao | AlphaNeural AI
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crzhao
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bert-base-uncased-finetuned-boolq
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transformers
tensorboard
safetensors
bert
text-classification
generated_from_trainer
super_glue
google-bert/bert-base-uncased
finetune
apache-2.0
model-index
autotrain_compatible
endpoints_compatible
us
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bert-base-uncased-finetuned-boolq
This model is a fine-tuned version of
bert-base-uncased
on the super_glue dataset. It achieves the following results on the evaluation set:
Loss: 0.9559
Accuracy: 0.7263
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 5
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
0.6445
1.0
590
0.5889
0.6972
0.5139
2.0
1180
0.5689
0.7183
0.3695
3.0
1770
0.7046
0.7159
0.2632
4.0
2360
0.9559
0.7263
0.1837
5.0
2950
1.0660
0.7248
Framework versions
Transformers 4.47.0.dev0
Pytorch 2.5.1+cpu
Datasets 3.1.0
Tokenizers 0.20.2
