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bert-base-uncased-finetuned-mrpc – AI Model by tudor-alp | AlphaNeural AI
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tudor-alp
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bert-base-uncased-finetuned-mrpc
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transformers
tensorboard
safetensors
bert
text-classification
generated_from_trainer
google-bert/bert-base-uncased
finetune
apache-2.0
text-embeddings-inference
endpoints_compatible
us
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bert-base-uncased-finetuned-mrpc
This model is a fine-tuned version of
bert-base-uncased
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.5190
Accuracy: 0.875
F1: 0.9113
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 OptimizerNames.ADAMW_TORCH_FUSED 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
F1
No log
1.0
230
0.4085
0.8309
0.8804
No log
2.0
460
0.4265
0.8382
0.8911
0.413
3.0
690
0.4550
0.8603
0.8946
0.413
4.0
920
0.5190
0.875
0.9113
0.1281
5.0
1150
0.5478
0.8725
0.9091
Framework versions
Transformers 4.56.1
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.22.0
