xtreme_s_xlsr_300m_minds14

• 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

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• 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

Framework versions

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