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t5-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: 0.7393
  • Bleu: 0.0000
  • Precisions: [0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 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0.3684210526315789]
  • Brevity Penalty: 0.0000
  • Length Ratio: 1.9486
  • Translation Length: 4700
  • Reference Length: 241200
  • Gen Len: 5.9091

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 0.9967 0.0000 [0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 0.11842105263157894, 0.3364485981308411, 0.22916666666666666, 0.1744186046511628, 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0.3684210526315789] 0.0000 1.9486 4700 241200 5.9091
No log 6.0 354 0.7393 0.0000 [0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 0.5909090909090909, 0.4634146341463415, 0.3684210526315789, 0.723404255319149, 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0.3684210526315789] 0.0000 1.9486 4700 241200 5.9091

Framework versions

  • Transformers 4.34.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.1
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