squad-bn-qgen-mt5-all-metric – AI Model by jannatul17 | AlphaNeural AI

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jannatul17/
squad-bn-qgen-mt5-all-metric

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text2text-generation

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text-generation-inference

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squad-bn-qgen-mt5-all-metric

This model is a fine-tuned version of google/mt5-small on the squad_bn dataset. It achieves the following results on the evaluation set:

Loss: 0.7273
Rouge1 Precision: 35.8589
Rouge1 Recall: 29.7041
Rouge1 Fmeasure: 31.6373
Rouge2 Precision: 15.4203
Rouge2 Recall: 12.5155
Rouge2 Fmeasure: 13.3978
Rougel Precision: 34.4684
Rougel Recall: 28.5887
Rougel Fmeasure: 30.4627
Rougelsum Precision: 34.4252
Rougelsum Recall: 28.5362
Rougelsum Fmeasure: 30.4053
Sacrebleu: 6.4143
Meteor: 0.1416
Gen Len: 16.7199

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: 5e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1 Precision	Rouge1 Recall	Rouge1 Fmeasure	Rouge2 Precision	Rouge2 Recall	Rouge2 Fmeasure	Rougel Precision	Rougel Recall	Rougel Fmeasure	Rougelsum Precision	Rougelsum Recall	Rougelsum Fmeasure	Sacrebleu	Meteor	Gen Len
0.8449	1.0	16396	0.7340	31.6476	26.8901	28.2871	13.621	11.3545	11.958	30.3276	25.7754	27.1048	30.3426	25.7489	27.0991	5.9655	0.1336	16.8685
0.7607	2.0	32792	0.7182	33.7173	28.6115	30.1049	14.8227	12.2059	12.9453	32.149	27.2036	28.6617	32.2479	27.2261	28.7272	6.6093	0.138	16.8522
0.7422	3.0	49188	0.7083	34.6128	29.0223	30.7248	14.9888	12.3092	13.1021	33.2507	27.8154	29.4599	33.2848	27.812	29.5064	6.2407	0.1416	16.5806
0.705	4.0	65584	0.7035	34.156	29.0012	30.546	14.72	12.0251	12.8161	32.7527	27.6511	29.1955	32.7692	27.6627	29.231	6.1784	0.1393	16.7793
0.6859	5.0	81980	0.7038	35.1405	29.6033	31.2614	15.5108	12.6414	13.5059	33.8335	28.4264	30.0745	33.8782	28.4349	30.0901	6.5896	0.144	16.6651

Framework versions

Transformers 4.20.1
Pytorch 1.11.0
Datasets 2.1.0
Tokenizers 0.12.1