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bert-pooling-based – AI Model by elifcen | AlphaNeural AI
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elifcen
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bert-pooling-based
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transformers
pytorch
tensorboard
bert
text-classification
generated_from_trainer
glue
apache-2.0
model-index
autotrain_compatible
endpoints_compatible
us
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bert-pooling-based
This model is a fine-tuned version of
bert-base-uncased
on the glue dataset. It achieves the following results on the evaluation set:
Loss: 0.5115
Matthews Correlation: 0.4086
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: 1.7718352056354854e-06
train_batch_size: 8
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
Matthews Correlation
0.5491
1.0
1069
0.5340
0.2513
0.4726
2.0
2138
0.5115
0.4086
Framework versions
Transformers 4.28.1
Pytorch 2.0.0+cu118
Datasets 2.12.0
Tokenizers 0.13.3
