xtreme_s_xlsr_300m_minds14

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the GOOGLE/XTREME_S - MINDS14.ALL dataset. It achieves the following results on the evaluation set:

• Accuracy: 0.9033
• Accuracy Cs-cz: 0.9164
• Accuracy De-de: 0.9477
• Accuracy En-au: 0.9235
• Accuracy En-gb: 0.9324
• Accuracy En-us: 0.9326
• Accuracy Es-es: 0.9177
• Accuracy Fr-fr: 0.9444
• Accuracy It-it: 0.9167
• Accuracy Ko-kr: 0.8649
• Accuracy Nl-nl: 0.9450
• Accuracy Pl-pl: 0.9146
• Accuracy Pt-pt: 0.8940
• Accuracy Ru-ru: 0.8667
• Accuracy Zh-cn: 0.7291
• F1: 0.9015
• F1 Cs-cz: 0.9154
• F1 De-de: 0.9467
• F1 En-au: 0.9199
• F1 En-gb: 0.9334
• F1 En-us: 0.9308
• F1 Es-es: 0.9158
• F1 Fr-fr: 0.9436
• F1 It-it: 0.9135
• F1 Ko-kr: 0.8642
• F1 Nl-nl: 0.9440
• F1 Pl-pl: 0.9159
• F1 Pt-pt: 0.8883
• F1 Ru-ru: 0.8646
• F1 Zh-cn: 0.7249
• Loss: 0.4119
• Loss Cs-cz: 0.3790
• Loss De-de: 0.2649
• Loss En-au: 0.3459
• Loss En-gb: 0.2853
• Loss En-us: 0.2203
• Loss Es-es: 0.2731
• Loss Fr-fr: 0.1909
• Loss It-it: 0.3520
• Loss Ko-kr: 0.5431
• Loss Nl-nl: 0.2515
• Loss Pl-pl: 0.4113
• Loss Pt-pt: 0.4798
• Loss Ru-ru: 0.6470
• Loss Zh-cn: 1.1216
• Predict Samples: 4086

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: 32
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• total_train_batch_size: 64
• total_eval_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1500
• num_epochs: 50.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Accuracy
2.6739	5.41	200	2.5687	0.0430	0.1190
1.4953	10.81	400	1.6052	0.5550	0.5692
0.6177	16.22	600	0.7927	0.8052	0.8011
0.3609	21.62	800	0.5679	0.8609	0.8609
0.4972	27.03	1000	0.5944	0.8509	0.8523
0.1799	32.43	1200	0.6194	0.8623	0.8621
0.1308	37.84	1400	0.5956	0.8569	0.8548
0.2298	43.24	1600	0.5201	0.8732	0.8743
0.0052	48.65	1800	0.3826	0.9106	0.9103

Framework versions

• Transformers 4.18.0.dev0
• Pytorch 1.10.2+cu113
• Datasets 2.0.1.dev0
• Tokenizers 0.11.6