xtreme_s_xlsr_300m_fleurs_langid_quicker_warmup

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the xtreme_s dataset. It achieves the following results on the evaluation set:

• Loss: 1.9765
• Accuracy: 0.6199

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: 4
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• total_eval_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1000
• num_epochs: 10.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Accuracy	Validation Loss
0.6644	0.26	1000	0.3071	3.2482
0.394	0.52	2000	0.5948	1.8833
0.1034	0.78	3000	0.6297	1.5852
0.1088	1.04	4000	0.5992	1.7903
0.0032	1.3	5000	0.6356	1.6219
0.1813	1.56	6000	0.5788	1.8168
0.0654	1.82	7000	0.6234	1.6089
0.0144	2.08	8000	0.6424	1.6071
0.0019	2.34	9000	0.5822	1.7820
0.0159	2.6	10000	0.6043	1.8407
0.0029	2.86	11000	0.5845	1.8600
0.0458	3.12	12000	0.6299	1.6591
0.013	3.38	13000	0.5903	2.0788
0.003	3.64	14000	0.6188	1.7645
0.0015	3.9	15000	0.6328	1.7739
0.0003	4.16	16000	0.6072	1.8742
0.0005	4.42	17000	0.6231	1.7102
0.006	4.68	18000	0.6122	1.6909
0.2367	4.93	19000	0.6029	1.9891
0.005	5.19	20000	0.6220	1.7245
0.0813	5.45	21000	0.5739	2.0495
0.1233	5.71	22000	0.6104	1.9601
0.0003	5.97	23000	0.5924	1.8881
0.0003	6.23	24000	0.6055	1.9568
0.0001	6.49	25000	0.6086	1.8489
0.2198	6.75	26000	0.6292	1.8048
0.0261	7.01	27000	2.0284	0.5989
0.0001	7.27	28000	1.7323	0.6431
0.0001	7.53	29000	1.9329	0.6310
0.0011	7.79	30000	1.9256	0.6107
0.0933	8.05	31000	2.3915	0.5896
0.0001	8.31	32000	1.9948	0.6021
0.0003	8.57	33000	1.9518	0.6126
0.0005	8.83	34000	1.8935	0.6243
0.0	9.09	35000	2.0177	0.6144
0.0002	9.35	36000	2.0234	0.6174
0.0	9.61	37000	1.9568	0.6216
0.0	9.87	38000	1.9765	0.6199

Framework versions

• Transformers 4.18.0.dev0
• Pytorch 1.11.0+cu113
• Datasets 1.18.4.dev0
• Tokenizers 0.11.6