--- language: - all license: apache-2.0 tags: - fleurs-lang_id - google/xtreme_s - generated_from_trainer datasets: - google/xtreme_s metrics: - accuracy model-index: - name: xtreme_s_xlsr_300m_fleurs_langid results: [] --- # xtreme_s_xlsr_300m_fleurs_langid 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: - 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