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t5-base_qa_no_context-finetuned-xsum – AI Model by javedonline | AlphaNeural AI
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t5-base_qa_no_context-finetuned-xsum
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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_qa_no_context-finetuned-xsum
This model is a fine-tuned version of
google-t5/t5-base
on the None dataset. It achieves the following results on the evaluation set:
Loss: 1.6412
Rouge1: 43.3171
Rouge2: 22.7074
Rougel: 41.6763
Rougelsum: 41.7334
Gen Len: 26.3177
Bleu: 0.1819
Precisions: [0.487515762925599, 0.2675321199143469, 0.1598232107214143, 0.10471323978035388]
Brevity Penalty: 0.8416
Length Ratio: 0.8529
Translation Length: 15860
Reference Length: 18595
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: 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: 100
mixed_precision_training: Native AMP
Training results
Framework versions
Transformers 4.49.0
Pytorch 2.6.0+cu118
Datasets 3.3.1
Tokenizers 0.21.0
