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SQuAD-extractive_QA-bert-base-cased – AI Model by srvmishra832 | AlphaNeural AI

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srvmishra832/
SQuAD-extractive_QA-bert-base-cased

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question-answering

generated_from_trainer

google-bert/bert-base-cased

endpoints_compatible

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SQuAD-extractive_QA-bert-base-cased

This model is a fine-tuned version of bert-base-cased on the SQuAD dataset.

Model description

bert-base-cased

Intended uses & limitations

Extractive QA

Training and evaluation data

SQuAD dataset. Can be accessed using the load_dataset() method.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
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
num_epochs: 3
mixed_precision_training: Native AMP

Training results

After training, exact_match: 80.246, and f1: 87.665

Framework versions

Transformers 4.50.0
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1