mbert-loraxs-qa-LT256x256-en – AI Model by NastasiaM | AlphaNeural AI

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NastasiaM/
mbert-loraxs-qa-LT256x256-en

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google-bert/bert-base-multilingual-cased

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mbert-loraxs-qa-LT256x256-en

This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.4042
Exact Match: 61.36
F1: 75.1893

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: 0.0004938912417557808
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
lr_scheduler_warmup_steps: 0.1
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Exact Match	F1
2.8002	1.0	634	1.4278	55.36	70.0363
1.4239	2.0	1268	1.3751	59.6	72.9845
1.0891	3.0	1902	1.2928	61.52	75.1850
0.8769	4.0	2536	1.3763	62.24	75.1312
0.7351	5.0	3170	1.4042	61.36	75.1893

Framework versions

PEFT 0.19.1
Transformers 5.10.2
Pytorch 2.11.0+cu128
Datasets 4.8.5
Tokenizers 0.22.2