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t-finetuned-event-extract

Text2Text Generation
Transformers
PyTorch
t5
Generated from Trainer
text-generation-inference
Inference Endpoints
Model card Files Community

t-finetuned-event-extract

This model is a fine-tuned version of google/flan-t5-small on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6432
  • Bleu: 0.0000
  • Precisions: [0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 0.7468354430379747, 0.6621621621621622, 0.5942028985507246, 0.8690476190476191, 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0.5942028985507246]
  • Brevity Penalty: 0.0000
  • Length Ratio: 4.2275
  • Translation Length: 8400
  • Reference Length: 198700
  • Gen Len: 9.1818

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: 1
  • eval_batch_size: 1
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 6

Training results

Training Loss Epoch Step Validation Loss Bleu Precisions Brevity Penalty Length Ratio Translation Length Reference Length Gen Len
No log 1.0 59 1.3111 0.0001 [0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 0.6115107913669064, 0.421875, 0.28205128205128205, 0.16822429906542055, 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No log 2.0 118 0.9797 0.0012 [0.7797619047619048, 0.6815286624203821, 0.5821917808219178, 0.5, 0.7797619047619048, 0.6815286624203821, 0.5821917808219178, 0.5, 0.7797619047619048, 0.6815286624203821, 0.5821917808219178, 0.5, 0.7797619047619048, 0.6815286624203821, 0.5821917808219178, 0.5, 0.7797619047619048, 0.6815286624203821, 0.5821917808219178, 0.5, 0.7797619047619048, 0.6815286624203821, 0.5821917808219178, 0.5, 0.7797619047619048, 0.6815286624203821, 0.5821917808219178, 0.5, 0.7797619047619048, 0.6815286624203821, 0.5821917808219178, 0.5, 0.7797619047619048, 0.6815286624203821, 0.5821917808219178, 0.5, 0.7797619047619048, 0.6815286624203821, 0.5821917808219178, 0.5, 0.7797619047619048, 0.6815286624203821, 0.5821917808219178, 0.5, 0.7797619047619048, 0.6815286624203821, 0.5821917808219178, 0.5, 0.7797619047619048, 0.6815286624203821, 0.5821917808219178, 0.5, 0.7797619047619048, 0.6815286624203821, 0.5821917808219178, 0.5, 0.7797619047619048, 0.6815286624203821, 0.5821917808219178, 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0.5869565217391305] 0.0023 8.5556 17000 198700 17.5455
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0.6521739130434783, 0.5813953488372093] 0.0000 5.2340 10400 198700 10.8182
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0.6712328767123288, 0.6029411764705882] 0.0000 4.1772 8300 198700 9.1818
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0.6621621621621622, 0.5942028985507246] 0.0000 4.2275 8400 198700 9.1818

Framework versions

  • Transformers 4.34.1
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.1
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