MMS-Adapter-Testing

This model is a fine-tuned version of facebook/mms-1b-all on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.9386
• Wer: 0.6378

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: 0.001
• train_batch_size: 2
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 10
• num_epochs: 1
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
5.768	0.0150	100	2.1552	0.9390
2.3489	0.0299	200	1.1714	0.7209
1.7924	0.0449	300	1.1720	0.7586
1.7483	0.0598	400	1.0868	0.7237
1.8404	0.0748	500	1.0824	0.6963
1.8122	0.0897	600	1.0771	0.6866
1.7504	0.1047	700	1.0705	0.6970
1.6675	0.1196	800	1.0688	0.6913
1.6123	0.1346	900	1.0446	0.6888
1.6237	0.1495	1000	1.0586	0.7034
1.6714	0.1645	1100	1.0562	0.6866
1.8129	0.1795	1200	1.0363	0.6891
1.7839	0.1944	1300	1.0374	0.6631
1.7305	0.2094	1400	1.0211	0.6834
1.5496	0.2243	1500	1.0225	0.6856
1.5106	0.2393	1600	1.0387	0.7127
1.7517	0.2542	1700	1.0561	0.6898
1.7117	0.2692	1800	1.0303	0.6866
1.6854	0.2841	1900	1.0240	0.6888
1.5186	0.2991	2000	1.0207	0.6873
1.5631	0.3140	2100	0.9964	0.6677
1.6909	0.3290	2200	1.0090	0.6738
1.5698	0.3440	2300	1.0016	0.6809
1.6702	0.3589	2400	0.9996	0.6749
1.628	0.3739	2500	1.0074	0.6699
1.8025	0.3888	2600	1.0312	0.6934
1.5986	0.4038	2700	0.9871	0.6667
1.5687	0.4187	2800	0.9893	0.6567
1.6444	0.4337	2900	0.9943	0.6674
1.5869	0.4486	3000	0.9831	0.6706
1.443	0.4636	3100	1.0192	0.7045
1.569	0.4785	3200	0.9783	0.6635
1.5302	0.4935	3300	0.9898	0.6727
1.5879	0.5084	3400	0.9773	0.6670
1.5739	0.5234	3500	0.9837	0.6895
1.5684	0.5384	3600	0.9836	0.6667
1.6397	0.5533	3700	0.9673	0.6578
1.5639	0.5683	3800	0.9888	0.6599
1.6773	0.5832	3900	0.9788	0.6613
1.5069	0.5982	4000	0.9801	0.6542
1.4801	0.6131	4100	0.9587	0.6545
1.7308	0.6281	4200	0.9599	0.6706
1.4852	0.6430	4300	0.9728	0.6663
1.4654	0.6580	4400	0.9468	0.6417
1.801	0.6729	4500	0.9591	0.6556
2.0928	0.6879	4600	0.9857	0.6670
1.561	0.7029	4700	0.9550	0.6503
1.6623	0.7178	4800	0.9587	0.6524
1.5252	0.7328	4900	0.9551	0.6531
1.5539	0.7477	5000	0.9660	0.6513
1.5571	0.7627	5100	0.9557	0.6531
1.6584	0.7776	5200	0.9649	0.6563
1.5072	0.7926	5300	0.9604	0.6481
1.5362	0.8075	5400	0.9457	0.6314
1.4772	0.8225	5500	0.9491	0.6449
1.3731	0.8374	5600	0.9609	0.6478
1.5795	0.8524	5700	0.9568	0.6567
1.4013	0.8674	5800	0.9457	0.6406
1.5817	0.8823	5900	0.9437	0.6513
1.4211	0.8973	6000	0.9433	0.6381
1.4341	0.9122	6100	0.9420	0.6353
1.4818	0.9272	6200	0.9407	0.6456
1.5241	0.9421	6300	0.9400	0.6381
1.575	0.9571	6400	0.9374	0.6392
1.5232	0.9720	6500	0.9385	0.6364
1.8634	0.9870	6600	0.9386	0.6378

Framework versions

• Transformers 4.41.0.dev0
• Pytorch 2.1.2
• Datasets 2.18.0
• Tokenizers 0.19.1