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XLMR-finetuned-FQuAD – AI Model by lielbin | AlphaNeural AI
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XLMR-finetuned-FQuAD
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transformers
tensorboard
safetensors
xlm-roberta
question-answering
generated_from_trainer
FacebookAI/xlm-roberta-base
finetune
mit
endpoints_compatible
us
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XLMR-finetuned-FQuAD
This model is a fine-tuned version of
FacebookAI/xlm-roberta-base
on the None 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: 0.0001
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3
mixed_precision_training: Native AMP
Training results
Framework versions
Transformers 4.37.2
Pytorch 2.2.2+cu121
Datasets 2.18.0
Tokenizers 0.15.2
