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.

LanguagePrecisionRecallF1-scoresupport
ar0.9980.9960.997500
bg0.9980.9640.981500
de0.9980.9960.997500
el0.9961.0000.998500
en1.0001.0001.000500
es0.9671.0000.983500
fr1.0001.0001.000500
hi0.9940.9920.993500
it1.0000.9920.996500
ja0.9960.9960.996500
nl1.0001.0001.000500
pl1.0001.0001.000500
pt0.9881.0000.994500
ru1.0000.9940.997500
sw1.0001.0001.000500
th1.0000.9980.999500
tr0.9940.9920.993500
ur1.0001.0001.000500
vi0.9921.0000.996500
zh1.0001.0001.000500

数据统计

数据评估

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