README.md

---
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: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# xtreme_s_xlsr_300m_fleurs_langid_truncated

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/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