100_randomization_model – AI Model by jvalline | AlphaNeural AI | AlphaNeural AI

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jvalline/
100_randomization_model

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text2text-generation

generated_from_trainer

google-t5/t5-base

text-generation-inference

endpoints_compatible

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100_randomization_model

This model is a fine-tuned version of t5-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.6576
Bleu: 0.0001
Wer: 0.9576
Rougel: 0.119
Gen Len: 18.9986

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: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Wer	Rougel	Gen Len
2.5767	0.16	1000	1.6626	0.0001	0.954	0.1251	18.9985
1.9533	0.32	2000	1.5147	0.0001	0.9524	0.1284	18.9986
1.8318	0.48	3000	1.4392	0.0001	0.9518	0.1297	18.9986
1.7626	0.64	4000	1.3857	0.0001	0.9514	0.1306	18.9986
1.7199	0.8	5000	1.3553	0.0001	0.951	0.1312	18.9988
1.6727	0.96	6000	1.3325	0.0001	0.9507	0.1319	18.9986
1.9628	1.12	7000	1.8528	0.0001	0.9524	0.1293	18.9988
2.9138	1.28	8000	2.6299	0.0001	0.9568	0.1205	18.9986
3.5506	1.44	9000	2.7483	0.0001	0.958	0.1181	18.9987
3.5214	1.6	10000	2.7007	0.0001	0.9578	0.1186	18.9986
3.4669	1.76	11000	2.6699	0.0001	0.9576	0.1189	18.9986
3.4448	1.92	12000	2.6576	0.0001	0.9576	0.119	18.9986

Framework versions

Transformers 4.37.1
Pytorch 2.3.0.dev20240122+cu121
Datasets 2.16.1
Tokenizers 0.15.1