Create README.md
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README.md
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---
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language:
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- ru
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- uk
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- be
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- kk
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- az
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- hy
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- ka
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- he
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- en
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- de
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tags:
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- language
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- classification
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datasets:
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- Open Subtitles
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- Tatoeba
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- Oscar
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---
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Model for Single Language Classification in texts. Supports 10 languages: ru, uk, be, kk, az, hy, ka, he, en, de.
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Model trained on small parts of Open Subtitles, Oscar and Tatoeba datasets (~9k samples per language).
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The metrics obtained from validation part of dataset (~1k samples per language).
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| eval_accuracy | eval_az_f1-score | eval_az_precision | eval_az_recall | eval_az_support | eval_be_f1-score | eval_be_precision | eval_be_recall | eval_be_support | eval_de_f1-score | eval_de_precision | eval_de_recall | eval_de_support | eval_en_f1-score | eval_en_precision | eval_en_recall | eval_en_support | eval_he_f1-score | eval_he_precision | eval_he_recall | eval_he_support | eval_hy_f1-score | eval_hy_precision | eval_hy_recall | eval_hy_support | eval_ka_f1-score | eval_ka_precision | eval_ka_recall | eval_ka_support | eval_kk_f1-score | eval_kk_precision | eval_kk_recall | eval_kk_support | eval_loss | eval_macro avg_f1-score | eval_macro avg_precision | eval_macro avg_recall | eval_macro avg_support | eval_ru_f1-score | eval_ru_precision | eval_ru_recall | eval_ru_support | eval_uk_f1-score | eval_uk_precision | eval_uk_recall | eval_uk_support | eval_weighted avg_f1-score | eval_weighted avg_precision | eval_weighted avg_recall | eval_weighted avg_support |
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| ------------- | ---------------- | ----------------- | -------------- | --------------- | ------------------ | ----------------- | ------------------ | --------------- | ------------------ | ----------------- | ------------------ | --------------- | ------------------ | ----------------- | ------------------ | --------------- | ------------------ | ----------------- | ----------------- | --------------- | ------------------ | ----------------- | ------------------ | --------------- | ---------------- | ----------------- | -------------- | --------------- | ------------------ | ----------------- | ------------------ | --------------- | ------------------- | ----------------------- | ------------------------ | --------------------- | ---------------------- | ------------------ | ----------------- | ------------------ | --------------- | ------------------ | ----------------- | ------------------ | --------------- | -------------------------- | --------------------------- | ------------------------ | ------------------------- |
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| 0.99 | 0.99849774661993 | 0.997 | 1 | 997 | 0.9960079840319361 | 0.998 | 0.9940239043824701 | 1004 | 0.9762506316321374 | 0.966 | 0.9867211440245148 | 979 | 0.9762376237623762 | 0.986 | 0.9666666666666667 | 1020 | 0.9995002498750626 | 1 | 0.999000999000999 | 1001 | 0.9944806823883593 | 0.991 | 0.9979859013091642 | 993 | 0.999 | 0.999 | 0.999 | 1000 | 0.9955112219451371 | 0.998 | 0.9930348258706467 | 1005 | 0.04831727221608162 | 0.9899994666596248 | 0.99 | 0.9901305007950791 | 10000 | 0.9822425164890917 | 0.968 | 0.9969104016477858 | 971 | 0.9822660098522168 | 0.997 | 0.9679611650485437 | 1030 | 0.9900005333403753 | 0.9901326000000001 | 0.99 | 10000 |
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