đ 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 offers language identification capabilities with specific performance metrics on various languages.
đ Quick Start
This section provides a high - level overview of the model. The model xtreme_s_xlsr_300m_fleurs_langid
is fine - tuned on the GOOGLE/XTREME_S - FLEURS.ALL dataset based on the pre - trained model facebook/wav2vec2-xls-r-300m
.
đ Documentation
Evaluation Results
The model achieves the following results on the evaluation set:
Language |
Accuracy |
Loss |
General |
0.7271 |
1.3789 |
Af Za |
0.3865 |
2.6778 |
Am Et |
0.8818 |
0.4615 |
Ar Eg |
0.9977 |
0.0149 |
As In |
0.9858 |
0.0764 |
Ast Es |
0.8362 |
0.4560 |
Az Az |
0.8386 |
0.5677 |
Be By |
0.4085 |
1.9231 |
Bn In |
0.9989 |
0.0024 |
Bs Ba |
0.2508 |
2.4954 |
Ca Es |
0.6947 |
1.2632 |
Ceb Ph |
0.9852 |
0.0426 |
Cmn Hans Cn |
0.9799 |
0.0650 |
Cs Cz |
0.5353 |
1.9334 |
Cy Gb |
0.9716 |
0.1274 |
Da Dk |
0.6688 |
1.4990 |
De De |
0.7807 |
0.8820 |
El Gr |
0.7692 |
0.9839 |
En Us |
0.9815 |
0.0827 |
Es 419 |
0.9846 |
0.0516 |
Et Ee |
0.5230 |
1.9264 |
Fa Ir |
0.8462 |
0.6520 |
Ff Sn |
0.2348 |
5.4283 |
Fi Fi |
0.9978 |
0.0109 |
Fil Ph |
0.9564 |
0.1706 |
Fr Fr |
0.9852 |
0.0591 |
Ga Ie |
0.8468 |
0.5174 |
Gl Es |
0.5016 |
1.2657 |
Gu In |
0.973 |
0.0850 |
Ha Ng |
0.9163 |
0.3234 |
He Il |
0.8043 |
0.8299 |
Hi In |
0.9354 |
0.4190 |
Hr Hr |
0.3654 |
2.9754 |
Hu Hu |
0.8044 |
0.8345 |
Hy Am |
0.9914 |
0.0329 |
Id Id |
0.9869 |
0.0529 |
Ig Ng |
0.9360 |
0.2523 |
Is Is |
0.0217 |
6.5153 |
It It |
0.8 |
0.8113 |
Ja Jp |
0.7385 |
1.3968 |
Jv Id |
0.5824 |
2.0009 |
Ka Ge |
0.8611 |
0.6162 |
Kam Ke |
0.4184 |
2.2192 |
Kea Cv |
0.8692 |
0.5567 |
Kk Kz |
0.8727 |
0.5592 |
Km Kh |
0.7030 |
1.7358 |
Kn In |
0.9630 |
0.1063 |
Ko Kr |
0.9843 |
0.1519 |
Ku Arab Iq |
0.9577 |
0.2075 |
Ky Kg |
0.8936 |
0.4639 |
Lb Lu |
0.8897 |
0.4454 |
Lg Ug |
0.9253 |
0.3764 |
Ln Cd |
0.9644 |
0.1844 |
Lo La |
0.1580 |
3.8051 |
Lt Lt |
0.4686 |
2.5054 |
Luo Ke |
0.9922 |
0.0479 |
Lv Lv |
0.6498 |
1.3713 |
Mi Nz |
0.9613 |
0.1390 |
Mk Mk |
0.7636 |
0.7952 |
Ml In |
0.6962 |
1.2999 |
Mn Mn |
0.8462 |
0.7621 |
Mr In |
0.3911 |
3.7056 |
Ms My |
0.3632 |
3.0192 |
Mt Mt |
0.6188 |
1.5520 |
My Mm |
0.9705 |
0.1514 |
Nb No |
0.6891 |
1.1194 |
Ne Np |
0.8994 |
0.4231 |
Nl Nl |
0.9093 |
0.3291 |
Nso Za |
0.8873 |
0.5106 |
Ny Mw |
0.4691 |
2.7346 |
Oci Fr |
0.1533 |
5.0983 |
Om Et |
0.9512 |
0.2297 |
Or In |
0.5447 |
2.5432 |
Pa In |
0.8153 |
0.7753 |
Pl Pl |
0.7757 |
0.7309 |
Ps Af |
0.8105 |
1.0454 |
Pt Br |
0.7715 |
0.9782 |
Ro Ro |
0.4122 |
3.5829 |
Ru Ru |
0.9794 |
0.0598 |
Rup Bg |
0.9468 |
0.1695 |
Sd Arab In |
0.5245 |
2.6198 |
Sk Sk |
0.8624 |
0.5583 |
Sl Si |
0.0300 |
6.0923 |
Sn Zw |
0.8843 |
0.4465 |
So So |
0.8803 |
0.4492 |
Sr Rs |
0.0257 |
4.7575 |
Sv Se |
0.0145 |
6.5858 |
Sw Ke |
0.9199 |
0.4235 |
Ta In |
0.9526 |
0.1818 |
Te In |
0.9788 |
0.0808 |
Tg Tj |
0.9883 |
0.0912 |
Th Th |
0.9912 |
0.0462 |
Tr Tr |
0.7887 |
0.7340 |
Uk Ua |
0.0627 |
4.6777 |
Umb Ao |
0.7863 |
1.4021 |
Ur Pk |
0.0134 |
8.4067 |
Uz Uz |
0.4014 |
4.3297 |
Vi Vn |
0.7246 |
1.1304 |
Wo Sn |
0.4555 |
2.2281 |
Xh Za |
1.0 |
0.0009 |
Yo Ng |
0.7353 |
1.3345 |
Yue Hant Hk |
0.7985 |
1.0728 |
Zu Za |
0.4696 |
3.7279 |
Predict Samples |
- |
77960 |
Training Procedure
Training Hyperparameters
The following hyperparameters were used during training:
Hyperparameter |
Value |
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
đ License
The model is licensed under the Apache - 2.0 license.