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--- |
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language: |
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- all |
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license: apache-2.0 |
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tags: |
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- fleurs-lang_id |
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- google/xtreme_s |
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- generated_from_trainer |
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datasets: |
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- google/xtreme_s |
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metrics: |
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- accuracy |
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model-index: |
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- name: xtreme_s_xlsr_300m_fleurs_langid |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# xtreme_s_xlsr_300m_fleurs_langid |
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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. |
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It achieves the following results on the evaluation set: |
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- Accuracy: 0.7271 |
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- Accuracy Af Za: 0.3865 |
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- Accuracy Am Et: 0.8818 |
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- Accuracy Ar Eg: 0.9977 |
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- Accuracy As In: 0.9858 |
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- Accuracy Ast Es: 0.8362 |
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- Accuracy Az Az: 0.8386 |
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- Accuracy Be By: 0.4085 |
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- Accuracy Bn In: 0.9989 |
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- Accuracy Bs Ba: 0.2508 |
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- Accuracy Ca Es: 0.6947 |
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- Accuracy Ceb Ph: 0.9852 |
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- Accuracy Cmn Hans Cn: 0.9799 |
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- Accuracy Cs Cz: 0.5353 |
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- Accuracy Cy Gb: 0.9716 |
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- Accuracy Da Dk: 0.6688 |
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- Accuracy De De: 0.7807 |
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- Accuracy El Gr: 0.7692 |
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- Accuracy En Us: 0.9815 |
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- Accuracy Es 419: 0.9846 |
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- Accuracy Et Ee: 0.5230 |
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- Accuracy Fa Ir: 0.8462 |
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- Accuracy Ff Sn: 0.2348 |
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- Accuracy Fi Fi: 0.9978 |
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- Accuracy Fil Ph: 0.9564 |
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- Accuracy Fr Fr: 0.9852 |
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- Accuracy Ga Ie: 0.8468 |
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- Accuracy Gl Es: 0.5016 |
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- Accuracy Gu In: 0.973 |
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- Accuracy Ha Ng: 0.9163 |
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- Accuracy He Il: 0.8043 |
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- Accuracy Hi In: 0.9354 |
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- Accuracy Hr Hr: 0.3654 |
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- Accuracy Hu Hu: 0.8044 |
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- Accuracy Hy Am: 0.9914 |
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- Accuracy Id Id: 0.9869 |
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- Accuracy Ig Ng: 0.9360 |
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- Accuracy Is Is: 0.0217 |
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- Accuracy It It: 0.8 |
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- Accuracy Ja Jp: 0.7385 |
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- Accuracy Jv Id: 0.5824 |
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- Accuracy Ka Ge: 0.8611 |
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- Accuracy Kam Ke: 0.4184 |
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- Accuracy Kea Cv: 0.8692 |
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- Accuracy Kk Kz: 0.8727 |
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- Accuracy Km Kh: 0.7030 |
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- Accuracy Kn In: 0.9630 |
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- Accuracy Ko Kr: 0.9843 |
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- Accuracy Ku Arab Iq: 0.9577 |
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- Accuracy Ky Kg: 0.8936 |
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- Accuracy Lb Lu: 0.8897 |
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- Accuracy Lg Ug: 0.9253 |
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- Accuracy Ln Cd: 0.9644 |
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- Accuracy Lo La: 0.1580 |
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- Accuracy Lt Lt: 0.4686 |
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- Accuracy Luo Ke: 0.9922 |
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- Accuracy Lv Lv: 0.6498 |
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- Accuracy Mi Nz: 0.9613 |
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- Accuracy Mk Mk: 0.7636 |
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- Accuracy Ml In: 0.6962 |
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- Accuracy Mn Mn: 0.8462 |
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- Accuracy Mr In: 0.3911 |
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- Accuracy Ms My: 0.3632 |
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- Accuracy Mt Mt: 0.6188 |
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- Accuracy My Mm: 0.9705 |
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- Accuracy Nb No: 0.6891 |
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- Accuracy Ne Np: 0.8994 |
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- Accuracy Nl Nl: 0.9093 |
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- Accuracy Nso Za: 0.8873 |
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- Accuracy Ny Mw: 0.4691 |
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- Accuracy Oci Fr: 0.1533 |
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- Accuracy Om Et: 0.9512 |
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- Accuracy Or In: 0.5447 |
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- Accuracy Pa In: 0.8153 |
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- Accuracy Pl Pl: 0.7757 |
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- Accuracy Ps Af: 0.8105 |
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- Accuracy Pt Br: 0.7715 |
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- Accuracy Ro Ro: 0.4122 |
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- Accuracy Ru Ru: 0.9794 |
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- Accuracy Rup Bg: 0.9468 |
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- Accuracy Sd Arab In: 0.5245 |
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- Accuracy Sk Sk: 0.8624 |
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- Accuracy Sl Si: 0.0300 |
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- Accuracy Sn Zw: 0.8843 |
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- Accuracy So So: 0.8803 |
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- Accuracy Sr Rs: 0.0257 |
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- Accuracy Sv Se: 0.0145 |
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- Accuracy Sw Ke: 0.9199 |
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- Accuracy Ta In: 0.9526 |
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- Accuracy Te In: 0.9788 |
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- Accuracy Tg Tj: 0.9883 |
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- Accuracy Th Th: 0.9912 |
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- Accuracy Tr Tr: 0.7887 |
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- Accuracy Uk Ua: 0.0627 |
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- Accuracy Umb Ao: 0.7863 |
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- Accuracy Ur Pk: 0.0134 |
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- Accuracy Uz Uz: 0.4014 |
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- Accuracy Vi Vn: 0.7246 |
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- Accuracy Wo Sn: 0.4555 |
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- Accuracy Xh Za: 1.0 |
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- Accuracy Yo Ng: 0.7353 |
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- Accuracy Yue Hant Hk: 0.7985 |
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- Accuracy Zu Za: 0.4696 |
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- Loss: 1.3789 |
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- Loss Af Za: 2.6778 |
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- Loss Am Et: 0.4615 |
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- Loss Ar Eg: 0.0149 |
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- Loss As In: 0.0764 |
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- Loss Ast Es: 0.4560 |
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- Loss Az Az: 0.5677 |
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- Loss Be By: 1.9231 |
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- Loss Bn In: 0.0024 |
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- Loss Bs Ba: 2.4954 |
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- Loss Ca Es: 1.2632 |
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- Loss Ceb Ph: 0.0426 |
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- Loss Cmn Hans Cn: 0.0650 |
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- Loss Cs Cz: 1.9334 |
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- Loss Cy Gb: 0.1274 |
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- Loss Da Dk: 1.4990 |
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- Loss De De: 0.8820 |
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- Loss El Gr: 0.9839 |
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- Loss En Us: 0.0827 |
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- Loss Es 419: 0.0516 |
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- Loss Et Ee: 1.9264 |
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- Loss Fa Ir: 0.6520 |
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- Loss Ff Sn: 5.4283 |
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- Loss Fi Fi: 0.0109 |
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- Loss Fil Ph: 0.1706 |
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- Loss Fr Fr: 0.0591 |
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- Loss Ga Ie: 0.5174 |
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- Loss Gl Es: 1.2657 |
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- Loss Gu In: 0.0850 |
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- Loss Ha Ng: 0.3234 |
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- Loss He Il: 0.8299 |
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- Loss Hi In: 0.4190 |
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- Loss Hr Hr: 2.9754 |
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- Loss Hu Hu: 0.8345 |
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- Loss Hy Am: 0.0329 |
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- Loss Id Id: 0.0529 |
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- Loss Ig Ng: 0.2523 |
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- Loss Is Is: 6.5153 |
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- Loss It It: 0.8113 |
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- Loss Ja Jp: 1.3968 |
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- Loss Jv Id: 2.0009 |
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- Loss Ka Ge: 0.6162 |
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- Loss Kam Ke: 2.2192 |
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- Loss Kea Cv: 0.5567 |
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- Loss Kk Kz: 0.5592 |
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- Loss Km Kh: 1.7358 |
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- Loss Kn In: 0.1063 |
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- Loss Ko Kr: 0.1519 |
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- Loss Ku Arab Iq: 0.2075 |
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- Loss Ky Kg: 0.4639 |
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- Loss Lb Lu: 0.4454 |
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- Loss Lg Ug: 0.3764 |
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- Loss Ln Cd: 0.1844 |
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- Loss Lo La: 3.8051 |
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- Loss Lt Lt: 2.5054 |
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- Loss Luo Ke: 0.0479 |
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- Loss Lv Lv: 1.3713 |
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- Loss Mi Nz: 0.1390 |
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- Loss Mk Mk: 0.7952 |
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- Loss Ml In: 1.2999 |
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- Loss Mn Mn: 0.7621 |
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- Loss Mr In: 3.7056 |
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- Loss Ms My: 3.0192 |
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- Loss Mt Mt: 1.5520 |
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- Loss My Mm: 0.1514 |
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- Loss Nb No: 1.1194 |
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- Loss Ne Np: 0.4231 |
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- Loss Nl Nl: 0.3291 |
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- Loss Nso Za: 0.5106 |
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- Loss Ny Mw: 2.7346 |
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- Loss Oci Fr: 5.0983 |
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- Loss Om Et: 0.2297 |
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- Loss Or In: 2.5432 |
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- Loss Pa In: 0.7753 |
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- Loss Pl Pl: 0.7309 |
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- Loss Ps Af: 1.0454 |
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- Loss Pt Br: 0.9782 |
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- Loss Ro Ro: 3.5829 |
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- Loss Ru Ru: 0.0598 |
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- Loss Rup Bg: 0.1695 |
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- Loss Sd Arab In: 2.6198 |
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- Loss Sk Sk: 0.5583 |
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- Loss Sl Si: 6.0923 |
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- Loss Sn Zw: 0.4465 |
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- Loss So So: 0.4492 |
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- Loss Sr Rs: 4.7575 |
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- Loss Sv Se: 6.5858 |
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- Loss Sw Ke: 0.4235 |
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- Loss Ta In: 0.1818 |
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- Loss Te In: 0.0808 |
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- Loss Tg Tj: 0.0912 |
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- Loss Th Th: 0.0462 |
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- Loss Tr Tr: 0.7340 |
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- Loss Uk Ua: 4.6777 |
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- Loss Umb Ao: 1.4021 |
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- Loss Ur Pk: 8.4067 |
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- Loss Uz Uz: 4.3297 |
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- Loss Vi Vn: 1.1304 |
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- Loss Wo Sn: 2.2281 |
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- Loss Xh Za: 0.0009 |
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- Loss Yo Ng: 1.3345 |
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- Loss Yue Hant Hk: 1.0728 |
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- Loss Zu Za: 3.7279 |
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- Predict Samples: 77960 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 8 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 2000 |
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- num_epochs: 5.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy | Validation Loss | |
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| 0.5296 | 0.26 | 1000 | 0.4016 | 2.6633 | |
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| 0.4252 | 0.52 | 2000 | 0.5751 | 1.8582 | |
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| 0.2989 | 0.78 | 3000 | 0.6332 | 1.6780 | |
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| 0.3563 | 1.04 | 4000 | 0.6799 | 1.4479 | |
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| 0.1617 | 1.3 | 5000 | 0.6679 | 1.5066 | |
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| 0.1409 | 1.56 | 6000 | 0.6992 | 1.4082 | |
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| 0.01 | 1.82 | 7000 | 0.7071 | 1.2448 | |
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| 0.0018 | 2.08 | 8000 | 0.7148 | 1.1996 | |
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| 0.0014 | 2.34 | 9000 | 0.6410 | 1.6505 | |
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| 0.0188 | 2.6 | 10000 | 0.6840 | 1.4050 | |
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| 0.0007 | 2.86 | 11000 | 0.6621 | 1.5831 | |
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| 0.1038 | 3.12 | 12000 | 0.6829 | 1.5441 | |
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| 0.0003 | 3.38 | 13000 | 0.6900 | 1.3483 | |
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| 0.0004 | 3.64 | 14000 | 0.6414 | 1.7070 | |
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| 0.0003 | 3.9 | 15000 | 0.7075 | 1.3198 | |
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| 0.0002 | 4.16 | 16000 | 0.7105 | 1.3118 | |
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| 0.0001 | 4.42 | 17000 | 0.7029 | 1.4099 | |
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| 0.0 | 4.68 | 18000 | 0.7180 | 1.3658 | |
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| 0.0001 | 4.93 | 19000 | 0.7236 | 1.3514 | |
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### Framework versions |
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- Transformers 4.18.0.dev0 |
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- Pytorch 1.10.1+cu111 |
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- Datasets 1.18.4.dev0 |
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- Tokenizers 0.11.6 |
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