Rolv-Arild/xls-r-300m-npsc-seq2seq

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.2965
• Wer: 0.3144

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
• gradient_accumulation_steps: 8
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1000
• num_epochs: 20.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
2.888	0.51	400	3.7320	0.9440
3.1636	1.02	800	2.9188	1.1916
2.773	1.53	1200	2.3347	1.0134
0.7198	2.04	1600	0.6678	0.4826
0.5255	2.55	2000	0.4605	0.4135
0.3961	3.06	2400	0.4266	0.3955
0.3424	3.57	2800	0.3786	0.3741
0.3858	4.08	3200	0.3161	0.3552
0.3218	4.59	3600	0.3029	0.3510
0.199	5.1	4000	0.2988	0.3418
0.2054	5.61	4400	0.2873	0.3434
0.1704	6.12	4800	0.3129	0.3432
0.1805	6.63	5200	0.2963	0.3413
0.2091	7.14	5600	0.2755	0.3329
0.1971	7.65	6000	0.2706	0.3309
0.1237	8.16	6400	0.2823	0.3270
0.123	8.67	6800	0.2754	0.3246
0.103	9.18	7200	0.2917	0.3272
0.1143	9.69	7600	0.2885	0.3305
0.156	10.2	8000	0.2810	0.3288
0.167	10.71	8400	0.2689	0.3232
0.0815	11.22	8800	0.2899	0.3236
0.0844	11.73	9200	0.2798	0.3225
0.0775	12.24	9600	0.2894	0.3224
0.0677	12.75	10000	0.2838	0.3204
0.1383	13.27	10400	0.2959	0.3211
0.1233	13.77	10800	0.2922	0.3213
0.0688	14.29	11200	0.2903	0.3209
0.0655	14.8	11600	0.2868	0.3182
0.0449	15.31	12000	0.2959	0.3172
0.0421	15.82	12400	0.2966	0.3180
0.0858	16.33	12800	0.2941	0.3164
0.0859	16.84	13200	0.2980	0.3165
0.0561	17.35	13600	0.2965	0.3165
0.0506	17.86	14000	0.2935	0.3148
0.0312	18.37	14400	0.2964	0.3154
0.0403	18.88	14800	0.2967	0.3160
0.0924	19.39	15200	0.2955	0.3147
0.0585	19.9	15600	0.2965	0.3144

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.0+cu113
• Datasets 1.18.1
• Tokenizers 0.11.0