ssc-el-CY-mms-model-mix-adapt-max-lowlr

This model is a fine-tuned version of facebook/mms-1b-all on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.3754
• Cer: 0.2342
• Wer: 0.6115

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: 0.0003
• train_batch_size: 8
• eval_batch_size: 6
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 100
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cer	Wer
2.195	0.3265	200	1.4337	0.3110	0.8452
1.2374	0.6531	400	1.3694	0.2952	0.7816
1.1455	0.9796	600	1.2702	0.2693	0.7370
1.0345	1.3053	800	1.3263	0.2626	0.7055
1.0057	1.6318	1000	1.3104	0.2551	0.6832
0.9932	1.9584	1200	1.3295	0.2602	0.6882
0.9756	2.2841	1400	1.3080	0.2554	0.6862
1.0163	2.6106	1600	1.2471	0.2479	0.6666
0.8878	2.9371	1800	1.3343	0.2516	0.6589
0.8929	3.2629	2000	1.2979	0.2415	0.6425
0.8487	3.5894	2200	1.3063	0.2436	0.6481
0.8473	3.9159	2400	1.2850	0.2427	0.6404
0.8445	4.2416	2600	1.3180	0.2412	0.6341
0.8127	4.5682	2800	1.3677	0.2379	0.6313
0.8213	4.8947	3000	1.3400	0.2395	0.6272
0.8358	5.2204	3200	1.3674	0.2445	0.6351
0.8756	5.5469	3400	1.3329	0.2353	0.6206
0.8017	5.8735	3600	1.3204	0.2360	0.6200
0.7917	6.1992	3800	1.3267	0.2317	0.6140
0.7755	6.5257	4000	1.3517	0.2380	0.6243
0.7652	6.8522	4200	1.3130	0.2369	0.6213
0.7336	7.1780	4400	1.3622	0.2375	0.6227
0.7668	7.5045	4600	1.3729	0.2370	0.6239
0.8038	7.8310	4800	1.3460	0.2388	0.6230
0.7281	8.1567	5000	1.3815	0.2360	0.6129
0.7599	8.4833	5200	1.3735	0.2313	0.6047
0.668	8.8098	5400	1.3905	0.2330	0.6052
0.7421	9.1355	5600	1.3592	0.2311	0.6085
0.7173	9.4620	5800	1.3857	0.2356	0.6123
0.7326	9.7886	6000	1.3754	0.2342	0.6115

Framework versions

• Transformers 4.57.2
• Pytorch 2.9.1+cu128
• Datasets 3.6.0
• Tokenizers 0.22.0