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t5-base-sciq-mcq – AI Model by MariamSalah | AlphaNeural AI
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MariamSalah
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t5-base-sciq-mcq
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transformers
safetensors
t5
text2text-generation
generated_from_trainer
google-t5/t5-base
finetune
apache-2.0
text-generation-inference
endpoints_compatible
us
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t5-base-sciq-mcq
This model is a fine-tuned version of
t5-base
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.3514
Rouge1: 0.5538
Rouge2: 0.3511
Rougel: 0.5078
Exact Match: 0.0
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: 3e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
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
Training Loss
Epoch
Step
Validation Loss
Rouge1
Rouge2
Rougel
Exact Match
0.4033
1.0
1168
0.3653
0.5438
0.3322
0.4962
0.0
0.3747
2.0
2336
0.3533
0.5493
0.3449
0.5033
0.0
0.3671
3.0
3504
0.3514
0.5538
0.3511
0.5078
0.0
Framework versions
Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1
