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papluca/xlm-roberta-base-language-detection

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xlm-roberta-base-language-detection

This model is a fine-tuned version of xlm-roberta-base on the Language Identification dataset.


Model description

This model is an XLM-RoBERTa transformer model with a classification head on top (i.e. a linear layer on top of the pooled output).
For additional information please refer to the xlm-roberta-base model card or to the paper Unsupervised Cross-lingual Representation Learning at Scale by Conneau et al.


Intended uses & limitations

You can directly use this model as a language detector, i.e. for sequence classification tasks. Currently, it supports the following 20 languages:
arabic (ar), bulgarian (bg), german (de), modern greek (el), english (en), spanish (es), french (fr), hindi (hi), italian (it), japanese (ja), dutch (nl), polish (pl), portuguese (pt), russian (ru), swahili (sw), thai (th), turkish (tr), urdu (ur), vietnamese (vi), and chinese (zh)


Training and evaluation data

The model was fine-tuned on the Language Identification dataset, which consists of text sequences in 20 languages. The training set contains 70k samples, while the validation and test sets 10k each. The average accuracy on the test set is 99.6% (this matches the average macro/weighted F1-score being the test set perfectly balanced). A more detailed evaluation is provided by the following table.

Language Precision Recall F1-score support
ar 0.998 0.996 0.997 500
bg 0.998 0.964 0.981 500
de 0.998 0.996 0.997 500
el 0.996 1.000 0.998 500
en 1.000 1.000 1.000 500
es 0.967 1.000 0.983 500
fr 1.000 1.000 1.000 500
hi 0.994 0.992 0.993 500
it 1.000 0.992 0.996 500
ja 0.996 0.996 0.996 500
nl 1.000 1.000 1.000 500
pl 1.000 1.000 1.000 500
pt 0.988 1.000 0.994 500
ru 1.000 0.994 0.997 500
sw 1.000 1.000 1.000 500
th 1.000 0.998 0.999 500
tr 0.994 0.992 0.993 500
ur 1.000 1.000 1.000 500
vi 0.992 1.000 0.996 500
zh 1.000 1.000 1.000 500


papluca/xlm-roberta-base-language-detection
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