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, 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Framework versions

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