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Question_Answering_extractive – AI Model by Prience91 | AlphaNeural AI
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Prience91
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Question_Answering_extractive
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transformers
safetensors
bert
question-answering
question answering
translation
generated_from_trainer
google-bert/bert-base-cased
finetune
apache-2.0
endpoints_compatible
us
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a-extractive
This model is a fine-tuned version of
bert-base-cased
on an unknown dataset.
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: 8
eval_batch_size: 8
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: 3
mixed_precision_training: Native AMP
Training results
Framework versions
Transformers 5.14.1
Pytorch 2.13.0+cu130
Datasets 5.0.0
Tokenizers 0.22.2
