t-finetuned-event-extract

deronDi/t-finetuned-event-extract

t-finetuned-event-extract is a machine learning model from deronDi. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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Repo size

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Repo makeup

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At a glance

Library: transformers
License: apache-2.0
Model type: t5
Access: Public
Created: Oct 18, 2023
Updated: Oct 20, 2023
SHA: d548503a

Base models

google/flan-t5-small

Model card

From the Hugging Face model README

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

Library: transformers
Type: t5
License: apache-2.0
Created: Oct 18, 2023
Updated: Oct 20, 2023