xtreme_s_xlsr_300m_fleurs_langid

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

• Accuracy: 0.7271
• Accuracy Af Za: 0.3865
• Accuracy Am Et: 0.8818
• Accuracy Ar Eg: 0.9977
• Accuracy As In: 0.9858
• Accuracy Ast Es: 0.8362
• Accuracy Az Az: 0.8386
• Accuracy Be By: 0.4085
• Accuracy Bn In: 0.9989
• Accuracy Bs Ba: 0.2508
• Accuracy Ca Es: 0.6947
• Accuracy Ceb Ph: 0.9852
• Accuracy Cmn Hans Cn: 0.9799
• Accuracy Cs Cz: 0.5353
• Accuracy Cy Gb: 0.9716
• Accuracy Da Dk: 0.6688
• Accuracy De De: 0.7807
• Accuracy El Gr: 0.7692
• Accuracy En Us: 0.9815
• Accuracy Es 419: 0.9846
• Accuracy Et Ee: 0.5230
• Accuracy Fa Ir: 0.8462
• Accuracy Ff Sn: 0.2348
• Accuracy Fi Fi: 0.9978
• Accuracy Fil Ph: 0.9564
• Accuracy Fr Fr: 0.9852
• Accuracy Ga Ie: 0.8468
• Accuracy Gl Es: 0.5016
• Accuracy Gu In: 0.973
• Accuracy Ha Ng: 0.9163
• Accuracy He Il: 0.8043
• Accuracy Hi In: 0.9354
• Accuracy Hr Hr: 0.3654
• Accuracy Hu Hu: 0.8044
• Accuracy Hy Am: 0.9914
• Accuracy Id Id: 0.9869
• Accuracy Ig Ng: 0.9360
• Accuracy Is Is: 0.0217
• Accuracy It It: 0.8
• Accuracy Ja Jp: 0.7385
• Accuracy Jv Id: 0.5824
• Accuracy Ka Ge: 0.8611
• Accuracy Kam Ke: 0.4184
• Accuracy Kea Cv: 0.8692
• Accuracy Kk Kz: 0.8727
• Accuracy Km Kh: 0.7030
• Accuracy Kn In: 0.9630
• Accuracy Ko Kr: 0.9843
• Accuracy Ku Arab Iq: 0.9577
• Accuracy Ky Kg: 0.8936
• Accuracy Lb Lu: 0.8897
• Accuracy Lg Ug: 0.9253
• Accuracy Ln Cd: 0.9644
• Accuracy Lo La: 0.1580
• Accuracy Lt Lt: 0.4686
• Accuracy Luo Ke: 0.9922
• Accuracy Lv Lv: 0.6498
• Accuracy Mi Nz: 0.9613
• Accuracy Mk Mk: 0.7636
• Accuracy Ml In: 0.6962
• Accuracy Mn Mn: 0.8462
• Accuracy Mr In: 0.3911
• Accuracy Ms My: 0.3632
• Accuracy Mt Mt: 0.6188
• Accuracy My Mm: 0.9705
• Accuracy Nb No: 0.6891
• Accuracy Ne Np: 0.8994
• Accuracy Nl Nl: 0.9093
• Accuracy Nso Za: 0.8873
• Accuracy Ny Mw: 0.4691
• Accuracy Oci Fr: 0.1533
• Accuracy Om Et: 0.9512
• Accuracy Or In: 0.5447
• Accuracy Pa In: 0.8153
• Accuracy Pl Pl: 0.7757
• Accuracy Ps Af: 0.8105
• Accuracy Pt Br: 0.7715
• Accuracy Ro Ro: 0.4122
• Accuracy Ru Ru: 0.9794
• Accuracy Rup Bg: 0.9468
• Accuracy Sd Arab In: 0.5245
• Accuracy Sk Sk: 0.8624
• Accuracy Sl Si: 0.0300
• Accuracy Sn Zw: 0.8843
• Accuracy So So: 0.8803
• Accuracy Sr Rs: 0.0257
• Accuracy Sv Se: 0.0145
• Accuracy Sw Ke: 0.9199
• Accuracy Ta In: 0.9526
• Accuracy Te In: 0.9788
• Accuracy Tg Tj: 0.9883
• Accuracy Th Th: 0.9912
• Accuracy Tr Tr: 0.7887
• Accuracy Uk Ua: 0.0627
• Accuracy Umb Ao: 0.7863
• Accuracy Ur Pk: 0.0134
• Accuracy Uz Uz: 0.4014
• Accuracy Vi Vn: 0.7246
• Accuracy Wo Sn: 0.4555
• Accuracy Xh Za: 1.0
• Accuracy Yo Ng: 0.7353
• Accuracy Yue Hant Hk: 0.7985
• Accuracy Zu Za: 0.4696
• Loss: 1.3789
• Loss Af Za: 2.6778
• Loss Am Et: 0.4615
• Loss Ar Eg: 0.0149
• Loss As In: 0.0764
• Loss Ast Es: 0.4560
• Loss Az Az: 0.5677
• Loss Be By: 1.9231
• Loss Bn In: 0.0024
• Loss Bs Ba: 2.4954
• Loss Ca Es: 1.2632
• Loss Ceb Ph: 0.0426
• Loss Cmn Hans Cn: 0.0650
• Loss Cs Cz: 1.9334
• Loss Cy Gb: 0.1274
• Loss Da Dk: 1.4990
• Loss De De: 0.8820
• Loss El Gr: 0.9839
• Loss En Us: 0.0827
• Loss Es 419: 0.0516
• Loss Et Ee: 1.9264
• Loss Fa Ir: 0.6520
• Loss Ff Sn: 5.4283
• Loss Fi Fi: 0.0109
• Loss Fil Ph: 0.1706
• Loss Fr Fr: 0.0591
• Loss Ga Ie: 0.5174
• Loss Gl Es: 1.2657
• Loss Gu In: 0.0850
• Loss Ha Ng: 0.3234
• Loss He Il: 0.8299
• Loss Hi In: 0.4190
• Loss Hr Hr: 2.9754
• Loss Hu Hu: 0.8345
• Loss Hy Am: 0.0329
• Loss Id Id: 0.0529
• Loss Ig Ng: 0.2523
• Loss Is Is: 6.5153
• Loss It It: 0.8113
• Loss Ja Jp: 1.3968
• Loss Jv Id: 2.0009
• Loss Ka Ge: 0.6162
• Loss Kam Ke: 2.2192
• Loss Kea Cv: 0.5567
• Loss Kk Kz: 0.5592
• Loss Km Kh: 1.7358
• Loss Kn In: 0.1063
• Loss Ko Kr: 0.1519
• Loss Ku Arab Iq: 0.2075
• Loss Ky Kg: 0.4639
• Loss Lb Lu: 0.4454
• Loss Lg Ug: 0.3764
• Loss Ln Cd: 0.1844
• Loss Lo La: 3.8051
• Loss Lt Lt: 2.5054
• Loss Luo Ke: 0.0479
• Loss Lv Lv: 1.3713
• Loss Mi Nz: 0.1390
• Loss Mk Mk: 0.7952
• Loss Ml In: 1.2999
• Loss Mn Mn: 0.7621
• Loss Mr In: 3.7056
• Loss Ms My: 3.0192
• Loss Mt Mt: 1.5520
• Loss My Mm: 0.1514
• Loss Nb No: 1.1194
• Loss Ne Np: 0.4231
• Loss Nl Nl: 0.3291
• Loss Nso Za: 0.5106
• Loss Ny Mw: 2.7346
• Loss Oci Fr: 5.0983
• Loss Om Et: 0.2297
• Loss Or In: 2.5432
• Loss Pa In: 0.7753
• Loss Pl Pl: 0.7309
• Loss Ps Af: 1.0454
• Loss Pt Br: 0.9782
• Loss Ro Ro: 3.5829
• Loss Ru Ru: 0.0598
• Loss Rup Bg: 0.1695
• Loss Sd Arab In: 2.6198
• Loss Sk Sk: 0.5583
• Loss Sl Si: 6.0923
• Loss Sn Zw: 0.4465
• Loss So So: 0.4492
• Loss Sr Rs: 4.7575
• Loss Sv Se: 6.5858
• Loss Sw Ke: 0.4235
• Loss Ta In: 0.1818
• Loss Te In: 0.0808
• Loss Tg Tj: 0.0912
• Loss Th Th: 0.0462
• Loss Tr Tr: 0.7340
• Loss Uk Ua: 4.6777
• Loss Umb Ao: 1.4021
• Loss Ur Pk: 8.4067
• Loss Uz Uz: 4.3297
• Loss Vi Vn: 1.1304
• Loss Wo Sn: 2.2281
• Loss Xh Za: 0.0009
• Loss Yo Ng: 1.3345
• Loss Yue Hant Hk: 1.0728
• Loss Zu Za: 3.7279
• Predict Samples: 77960

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: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• 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: 2000
• num_epochs: 5.0
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.18.0.dev0
• Pytorch 1.10.1+cu111
• Datasets 1.18.4.dev0
• Tokenizers 0.11.6