t5-large-finetuned-break-qdmr-decomposition – AI Model by MatthisHoules | AlphaNeural AI

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MatthisHoules/
t5-large-finetuned-break-qdmr-decomposition

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t5-large-finetuned-break-qdmr-decomposition

This model is a fine-tuned version of t5-large on the break_data dataset. It achieves the following results on the evaluation set:

Loss: 0.1729
Bleu: 0.2217
Brevity Penalty: 0.2926
Length Ratio: 0.4487
Translation Length: 108954
Reference Length: 242845

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: 0.0001
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 64
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Brevity Penalty	Length Ratio	Translation Length	Reference Length
No log	1.0	346	0.2217	0.2190	0.2973	0.4519	109738	242845
0.3597	2.0	692	0.1898	0.2213	0.2944	0.4499	109245	242845
0.1943	3.0	1038	0.1780	0.2213	0.2936	0.4494	109125	242845
0.1943	4.0	1385	0.1722	0.2209	0.2926	0.4486	108943	242845
0.1588	5.0	1731	0.1708	0.2221	0.2938	0.4495	109159	242845
0.1395	6.0	2077	0.1699	0.2209	0.2907	0.4473	108635	242845
0.1395	7.0	2423	0.1699	0.2219	0.2927	0.4487	108964	242845
0.1245	8.0	2770	0.1717	0.2215	0.2924	0.4485	108909	242845
0.1152	9.0	3116	0.1724	0.2215	0.2924	0.4485	108914	242845
0.1152	9.99	3460	0.1729	0.2217	0.2926	0.4487	108954	242845

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3