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finetuned-bert-mrpc – AI Model by shahma | AlphaNeural AI
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shahma
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finetuned-bert-mrpc
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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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finetuned-bert-mrpc
This model is a fine-tuned version of
bert-base-cased
on the glue dataset. It achieves the following results on the evaluation set:
Loss: 0.4266
Accuracy: 0.8603
F1: 0.9032
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: 3.0
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1
0.5475
1.0
230
0.4024
0.8211
0.8785
0.3309
2.0
460
0.3702
0.8529
0.8986
0.1716
3.0
690
0.4266
0.8603
0.9032
Framework versions
Transformers 4.20.1
Pytorch 1.12.0+cu102
Datasets 2.3.2
Tokenizers 0.12.1
