bert-base-uncased-sst2-qnli – AI Model by AKHILESHANIL25 | AlphaNeural AI

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AKHILESHANIL25/
bert-base-uncased-sst2-qnli

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text-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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bert-base-uncased-sst2-qnli

This model is a fine-tuned version of google-bert/bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.4663
Accuracy: 0.8788
Precision: 0.8779
Recall: 0.8756
F1: 0.8767

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: 5e-05
train_batch_size: 8
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.4939	1.0	3438	0.4533	0.8325	0.8266	0.8350	0.8308
0.3678	2.0	6876	0.3815	0.8475	0.8254	0.8756	0.8498
0.2783	3.0	10314	0.4029	0.855	0.8424	0.8680	0.855
0.2132	4.0	13752	0.4663	0.8788	0.8779	0.8756	0.8767

Framework versions

Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.2