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t5-small_qa_no_context-finetuned-xsum – AI Model by javedonline | AlphaNeural AI
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t5-small_qa_no_context-finetuned-xsum
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transformers
safetensors
t5
text2text-generation
generated_from_trainer
google-t5/t5-small
finetune
apache-2.0
text-generation-inference
endpoints_compatible
us
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t5-small_qa_no_context-finetuned-xsum
This model is a fine-tuned version of
google-t5/t5-small
on the None dataset. It achieves the following results on the evaluation set:
Loss: 1.9746
Rouge1: 19.7961
Rouge2: 10.0489
Rougel: 19.2238
Rougelsum: 19.2447
Gen Len: 30.4007
Bleu: 0.1028
Precisions: [0.3009342079109521, 0.16163349347975292, 0.09752972164875062, 0.05952380952380952]
Brevity Penalty: 0.7931
Length Ratio: 0.8118
Translation Length: 15093
Reference Length: 18591
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
