metadata

language:
  - all
license: apache-2.0
tags:
  - fleurs-lang_id
  - google/xtreme_s
  - generated_from_trainer
datasets:
  - xtreme_s
metrics:
  - accuracy
model-index:
  - name: xtreme_s_xlsr_300m_fleurs_langid_truncated
    results: []

xtreme_s_xlsr_300m_fleurs_langid_truncated

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:

Epoch Af Za: 4.07
Epoch Am Et: 4.07
Epoch Ar Eg: 4.07
Epoch As In: 4.07
Epoch Ast Es: 4.07
Epoch Az Az: 4.07
Epoch Be By: 4.07
Epoch Bn In: 4.07
Epoch Bs Ba: 4.07
Epoch Ca Es: 4.07
Epoch Ceb Ph: 4.07
Epoch Cmn Hans Cn: 4.07
Epoch Cs Cz: 4.07
Epoch Cy Gb: 4.07
Epoch Da Dk: 4.07
Epoch De De: 4.07
Epoch El Gr: 4.07
Epoch En Us: 4.07
Epoch Es 419: 4.07
Epoch Et Ee: 4.07
Epoch Fa Ir: 4.07
Epoch Ff Sn: 4.07
Epoch Fi Fi: 4.07
Epoch Fil Ph: 4.07
Epoch Fr Fr: 4.07
Epoch Ga Ie: 4.07
Epoch Gl Es: 4.07
Epoch Gu In: 4.07
Epoch Ha Ng: 4.07
Epoch He Il: 4.07
Epoch Hi In: 4.07
Epoch Hr Hr: 4.07
Epoch Hu Hu: 4.07
Epoch Hy Am: 4.07
Epoch Id Id: 4.07
Epoch Ig Ng: 4.07
Epoch Is Is: 4.07
Epoch It It: 4.07
Epoch Ja Jp: 4.07
Epoch Jv Id: 4.07
Epoch Ka Ge: 4.07
Epoch Kam Ke: 4.07
Epoch Kea Cv: 4.07
Epoch Kk Kz: 4.07
Epoch Km Kh: 4.07
Epoch Kn In: 4.07
Epoch Ko Kr: 4.07
Epoch Ku Arab Iq: 4.07
Epoch Ky Kg: 4.07
Epoch Lb Lu: 4.07
Epoch Lg Ug: 4.07
Epoch Ln Cd: 4.07
Epoch Lo La: 4.07
Epoch Lt Lt: 4.07
Epoch Luo Ke: 4.07
Epoch Lv Lv: 4.07
Epoch Mi Nz: 4.07
Epoch Mk Mk: 4.07
Epoch Ml In: 4.07
Epoch Mn Mn: 4.07
Epoch Mr In: 4.07
Epoch Ms My: 4.07
Epoch Mt Mt: 4.07
Epoch My Mm: 4.07
Epoch Nb No: 4.07
Epoch Ne Np: 4.07
Epoch Nl Nl: 4.07
Epoch Nso Za: 4.07
Epoch Ny Mw: 4.07
Epoch Oci Fr: 4.07
Epoch Om Et: 4.07
Epoch Or In: 4.07
Epoch Pa In: 4.07
Epoch Pl Pl: 4.07
Epoch Ps Af: 4.07
Epoch Pt Br: 4.07
Epoch Ro Ro: 4.07
Epoch Ru Ru: 4.07
Epoch Rup Bg: 4.07
Epoch Sd Arab In: 4.07
Epoch Sk Sk: 4.07
Epoch Sl Si: 4.07
Epoch Sn Zw: 4.07
Epoch So So: 4.07
Epoch Sr Rs: 4.07
Epoch Sv Se: 4.07
Epoch Sw Ke: 4.07
Epoch Ta In: 4.07
Epoch Te In: 4.07
Epoch Tg Tj: 4.07
Epoch Th Th: 4.07
Epoch Tr Tr: 4.07
Epoch Uk Ua: 4.07
Epoch Umb Ao: 4.07
Epoch Ur Pk: 4.07
Epoch Uz Uz: 4.07
Epoch Vi Vn: 4.07
Epoch Wo Sn: 4.07
Epoch Xh Za: 4.07
Epoch Yo Ng: 4.07
Epoch Yue Hant Hk: 4.07
Epoch Zu Za: 4.07
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

Training Loss	Epoch	Step	Accuracy	Validation Loss
0.5296	0.26	1000	0.4016	2.6633
0.4252	0.52	2000	0.5751	1.8582
0.2989	0.78	3000	0.6332	1.6780
0.3563	1.04	4000	0.6799	1.4479
0.1617	1.3	5000	0.6679	1.5066
0.1409	1.56	6000	0.6992	1.4082
0.01	1.82	7000	0.7071	1.2448
0.0018	2.08	8000	0.7148	1.1996
0.0014	2.34	9000	0.6410	1.6505
0.0188	2.6	10000	0.6840	1.4050
0.0007	2.86	11000	0.6621	1.5831
0.1038	3.12	12000	0.6829	1.5441
0.0003	3.38	13000	0.6900	1.3483
0.0004	3.64	14000	0.6414	1.7070
0.0003	3.9	15000	0.7075	1.3198
0.0002	4.16	16000	0.7105	1.3118
0.0001	4.42	17000	0.7029	1.4099
0.0	4.68	18000	0.7180	1.3658
0.0001	4.93	19000	0.7236	1.3514

Framework versions

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