idt5-base-qaqg-v1

hawalurahman/idt5-base-qaqg-v1

idt5-base-qaqg-v1 is a machine learning model from hawalurahman. 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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At a glance

Library: transformers
License: apache-2.0
Model type: t5
Access: Public
Created: Oct 23, 2025
Updated: Oct 23, 2025
SHA: ff676957

Base models

muchad/idt5-base

Model card

From the Hugging Face model README

idt5-base-qaqg-v1

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: 1.1889
Rouge1: 0.4385
Rouge2: 0.2411
Rougel: 0.4038
Rougelsum: 0.4051
Bleu: 0.1979
Rouge All: {'rouge1': 0.43853815624504594, 'rouge2': 0.2410591718102328, 'rougeL': 0.4038362017785524, 'rougeLsum': 0.4051001210135863}
Bleu All: {'bleu': 0.1978974553206818, 'precisions': [0.573219536099314, 0.3739414195704704, 0.2699060273834184, 0.18617981714482246], 'brevity_penalty': 0.6142890519824143, 'length_ratio': 0.6723639683422225, 'translation_length': 97952, 'reference_length': 145683}

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 OptimizerNames.ADAMW_TORCH_FUSED 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	Rouge All	Bleu All
1.4554	1.0	4695	1.3151	0.4093	0.2121	0.3775	0.3784	0.1768	{'rouge1': 0.4093041877796644, 'rouge2': 0.21207626779497607, 'rougeL': 0.37746935047648256, 'rougeLsum': 0.37839370282560814}	{'bleu': 0.1767889269940612, 'precisions': [0.5558623306004594, 0.3530361152694611, 0.2517539480253304, 0.16820034770097717], 'brevity_penalty': 0.5855414855677461, 'length_ratio': 0.6513731869881867, 'translation_length': 94894, 'reference_length': 145683}
1.2531	2.0	9390	1.2397	0.4204	0.2235	0.3872	0.3885	0.1857	{'rouge1': 0.42044750389162094, 'rouge2': 0.22347084623485935, 'rougeL': 0.387219912292853, 'rougeLsum': 0.3885031022276384}	{'bleu': 0.1857088057007626, 'precisions': [0.5575373341704146, 0.3582422840736901, 0.2553870099383795, 0.1718580145811894], 'brevity_penalty': 0.6069146890574346, 'length_ratio': 0.6669480996410013, 'translation_length': 97163, 'reference_length': 145683}
1.1434	3.0	14085	1.2107	0.4327	0.2357	0.3982	0.3995	0.1933	{'rouge1': 0.43271354261393746, 'rouge2': 0.2356657979493153, 'rougeL': 0.3982235493671218, 'rougeLsum': 0.3995229814725741}	{'bleu': 0.19331241558234652, 'precisions': [0.5684670332493962, 0.36872760366304536, 0.2659619181048207, 0.18313200723327305], 'brevity_penalty': 0.6081505249707005, 'length_ratio': 0.6678541765339813, 'translation_length': 97295, 'reference_length': 145683}
1.0687	4.0	18780	1.1825	0.4373	0.2385	0.4024	0.4037	0.1969	{'rouge1': 0.43731414539071134, 'rouge2': 0.2385486676075438, 'rougeL': 0.40239411575563816, 'rougeLsum': 0.4036692475942502}	{'bleu': 0.19694384537679482, 'precisions': [0.5752430410026272, 0.374645211629143, 0.26901812937262, 0.18366760943534377], 'brevity_penalty': 0.6130854268297403, 'length_ratio': 0.6714784841059012, 'translation_length': 97823, 'reference_length': 145683}
0.999	5.0	23475	1.1889	0.4385	0.2411	0.4038	0.4051	0.1979	{'rouge1': 0.43853815624504594, 'rouge2': 0.2410591718102328, 'rougeL': 0.4038362017785524, 'rougeLsum': 0.4051001210135863}	{'bleu': 0.1978974553206818, 'precisions': [0.573219536099314, 0.3739414195704704, 0.2699060273834184, 0.18617981714482246], 'brevity_penalty': 0.6142890519824143, 'length_ratio': 0.6723639683422225, 'translation_length': 97952, 'reference_length': 145683}

Framework versions

Transformers 4.57.1
Pytorch 2.9.0+cu129
Datasets 4.2.0
Tokenizers 0.22.1

Library: transformers
Type: t5
License: apache-2.0
Created: Oct 23, 2025
Updated: Oct 23, 2025