idt5-base-qaqg_v4 – AI Model by hawalurahman | AlphaNeural AI

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hawalurahman/
idt5-base-qaqg_v4

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text2text-generation

generated_from_trainer

muchad/idt5-base

text-generation-inference

endpoints_compatible

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idt5-base-qaqg_v4

This model is a fine-tuned version of muchad/idt5-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.4503
Rouge1: 0.3985
Rouge2: 0.2226
Rougel: 0.3803
Rougelsum: 0.3801
Bleu: 0.1821

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: 8
eval_batch_size: 8
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Bleu
1.7373	1.0	6000	1.5117	0.3682	0.1977	0.3508	0.3505	0.1687
1.5048	2.0	12000	1.4624	0.3865	0.2162	0.3694	0.3694	0.1709
1.399	3.0	18000	1.4520	0.3902	0.2156	0.3717	0.3715	0.1777
1.2412	4.0	24000	1.4497	0.3970	0.2220	0.3791	0.3790	0.1820
1.207	5.0	30000	1.4503	0.3985	0.2226	0.3803	0.3801	0.1821

Framework versions

Transformers 4.46.0
Pytorch 2.4.0a0+f70bd71a48.nv24.06
Datasets 3.0.2
Tokenizers 0.20.1