bert-base-uncased-finetuned-mrpc – AI Model by inesvander | AlphaNeural AI

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inesvander/
bert-base-uncased-finetuned-mrpc

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text-classification

generated_from_trainer

google-bert/bert-base-uncased

text-embeddings-inference

endpoints_compatible

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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: 0.4430
Accuracy: 0.8578
F1: 0.8949

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	0.4024	0.8186	0.875
No log	2.0	460	0.4179	0.8309	0.8874
0.4067	3.0	690	0.4430	0.8578	0.8949
0.4067	4.0	920	0.5181	0.8480	0.8924
0.1148	5.0	1150	0.5658	0.8480	0.8920

Framework versions

Transformers 4.56.1
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.22.0