t5-base-TEDxJP-0front-1body-6rear – AI Model by Padomin | AlphaNeural AI

You can deploy this model and start earning money today!

Padomin/
t5-base-TEDxJP-0front-1body-6rear

like

0

text2text-generation

generated_from_trainer

text-generation-inference

endpoints_compatible

Views

No views yet

t5-base-TEDxJP-0front-1body-6rear

This model is a fine-tuned version of sonoisa/t5-base-japanese on the te_dx_jp dataset. It achieves the following results on the evaluation set:

Loss: 0.4688
Wer: 0.1755
Mer: 0.1695
Wil: 0.2577
Wip: 0.7423
Hits: 55504
Substitutions: 6505
Deletions: 2578
Insertions: 2249
Cer: 0.1373

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Mer	Wil	Wip	Hits	Substitutions	Deletions	Insertions	Cer
0.6426	1.0	1457	0.4936	0.2128	0.2007	0.2903	0.7097	54742	6734	3111	3899	0.1791
0.5519	2.0	2914	0.4535	0.1970	0.1876	0.2747	0.7253	55096	6467	3024	3233	0.1567
0.5007	3.0	4371	0.4465	0.1819	0.1751	0.2628	0.7372	55359	6481	2747	2522	0.1435
0.4374	4.0	5828	0.4417	0.1761	0.1703	0.2582	0.7418	55399	6471	2717	2184	0.1373
0.3831	5.0	7285	0.4459	0.1755	0.1697	0.2570	0.7430	55465	6429	2693	2214	0.1383
0.352	6.0	8742	0.4496	0.1755	0.1697	0.2573	0.7427	55452	6450	2685	2202	0.1374
0.3955	7.0	10199	0.4527	0.1766	0.1707	0.2580	0.7420	55429	6429	2729	2251	0.1392
0.3132	8.0	11656	0.4629	0.1764	0.1703	0.2580	0.7420	55522	6472	2593	2329	0.1380
0.3116	9.0	13113	0.4652	0.1755	0.1695	0.2577	0.7423	55517	6505	2565	2264	0.1371
0.313	10.0	14570	0.4688	0.1755	0.1695	0.2577	0.7423	55504	6505	2578	2249	0.1373

Framework versions

Transformers 4.21.2
Pytorch 1.12.1+cu116
Datasets 2.4.0
Tokenizers 0.12.1