t5-large-slots – AI Model by jinlmsft | AlphaNeural AI

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jinlmsft/
t5-large-slots

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text2text-generation

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text-generation-inference

endpoints_compatible

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t5-large-slots

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

Loss: 0.0889
Acc: 0.76
True Num: 11167
Num: 14748

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
gradient_accumulation_steps: 16
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss	Acc	True Num	Num
0.3539	0.56	1000	0.2669	0.56	8264	14748
0.2523	1.13	2000	0.2031	0.56	8317	14748
0.2003	1.69	3000	0.1498	0.58	8496	14748
0.1609	2.25	4000	0.1284	0.58	8612	14748
0.1431	2.82	5000	0.1119	0.59	8675	14748
0.1236	3.38	6000	0.1054	0.59	8737	14748
0.1172	3.95	7000	0.0981	0.59	8773	14748
0.1027	4.51	8000	0.0955	0.6	8787	14748
0.0968	5.07	9000	0.0931	0.6	8807	14748
0.0911	5.64	10000	0.0895	0.6	8787	14748
0.0852	6.2	11000	0.0912	0.6	8840	14748
0.0823	6.76	12000	0.0880	0.6	8846	14748
0.0768	7.33	13000	0.0915	0.6	8879	14748
0.0758	7.89	14000	0.0892	0.6	8853	14748
0.0708	8.46	15000	0.0885	0.6	8884	14748
0.0701	9.02	16000	0.0884	0.6	8915	14748
0.0685	9.58	17000	0.0884	0.6	8921	14748

Framework versions

Transformers 4.12.5
Pytorch 1.10.0+cu102
Datasets 1.15.1
Tokenizers 0.10.3