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, 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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, 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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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No log	3.0	177	0.8205	0.0015	[0.788235294117647, 0.6981132075471698, 0.6418918918918919, 0.5869565217391305, 0.788235294117647, 0.6981132075471698, 0.6418918918918919, 0.5869565217391305, 0.788235294117647, 0.6981132075471698, 0.6418918918918919, 0.5869565217391305, 0.788235294117647, 0.6981132075471698, 0.6418918918918919, 0.5869565217391305, 0.788235294117647, 0.6981132075471698, 0.6418918918918919, 0.5869565217391305, 0.788235294117647, 0.6981132075471698, 0.6418918918918919, 0.5869565217391305, 0.788235294117647, 0.6981132075471698, 0.6418918918918919, 0.5869565217391305, 0.788235294117647, 0.6981132075471698, 0.6418918918918919, 0.5869565217391305, 0.788235294117647, 0.6981132075471698, 0.6418918918918919, 0.5869565217391305, 0.788235294117647, 0.6981132075471698, 0.6418918918918919, 0.5869565217391305, 0.788235294117647, 0.6981132075471698, 0.6418918918918919, 0.5869565217391305, 0.788235294117647, 0.6981132075471698, 0.6418918918918919, 0.5869565217391305, 0.788235294117647, 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0.5869565217391305]	0.0023	8.5556	17000	198700	17.5455
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Framework versions

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