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Mike0307
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multilingual-e5-language-detection

Text Classification
Transformers
PyTorch
38 languages
xlm-roberta
language-detection
Frisian
Dhivehi
Hakha_Chin
Kabyle
Sakha
Inference Endpoints
Model card Files Community
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Update README.md

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add index of language

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@@ -114,58 +114,57 @@ print(topk_prob, topk_labels)
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  ### Evaluation Results
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  The test datasets refers to the **common_language** test datasets.
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- |language | precision | recall | f1-score | support |
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- | --- | --- | ---| --- | --- |
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- |Arabic|1.00|1.00|1.00|151|
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- | Basque | 0.99 | 1.00 | 1.00 | 111|
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- | Breton | 1.00 | 0.90 | 0.95 | 252|
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- | Catalan | 0.96 | 0.99 | 0.97 | 96|
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- | Chinese_China | 0.98 | 1.00 | 0.99 | 100|
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- | Chinese_Hongkong | 0.97 | 0.87 | 0.92 | 115|
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- | Chinese_Taiwan | 0.92 | 0.98 | 0.95 | 170|
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- | Chuvash | 0.98 | 1.00 | 0.99 | 137|
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- | Czech | 0.98 | 1.00 | 0.99 | 128|
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- | Dhivehi | 1.00 | 1.00 | 1.00 | 111|
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- | Dutch | 0.99 | 1.00 | 0.99 | 144|
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- | English | 0.96 | 1.00 | 0.98 | 98|
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- | Esperanto | 0.98 | 0.98 | 0.98 | 107|
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- | Estonian | 1.00 | 0.99 | 0.99 | 93|
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- | French | 0.95 | 1.00 | 0.98 | 106|
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- | Frisian | 1.00 | 0.98 | 0.99 | 117|
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- | Georgian | 1.00 | 1.00 | 1.00 | 110|
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- | German | 1.00 | 1.00 | 1.00 | 101|
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- | Greek | 1.00 | 1.00 | 1.00 | 153|
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- | Hakha_Chin | 0.99 | 1.00 | 0.99 | 202|
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- | Indonesian | 0.99 | 0.99 | 0.99 | 150|
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- | Interlingua | 0.96 | 0.97 | 0.96 | 182|
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- | Italian | 0.99 | 0.94 | 0.96 | 100|
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- | Japanese | 1.00 | 1.00 | 1.00 | 144|
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- | Kabyle | 1.00 | 0.96 | 0.98 | 156|
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- | Kinyarwanda | 0.97 | 1.00 | 0.99 | 103|
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- | Kyrgyz | 0.98 | 1.00 | 0.99 | 129|
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- | Latvian | 0.98 | 0.98 | 0.98 | 171|
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- | Maltese | 0.99 | 0.98 | 0.98 | 152|
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- | Mongolian | 1.00 | 1.00 | 1.00 | 112|
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- | Persian | 1.00 | 1.00 | 1.00 | 123|
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- | Polish | 0.91 | 0.99 | 0.95 | 128|
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- | Portuguese | 0.94 | 0.99 | 0.96 | 124|
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- | Romanian | 1.00 | 1.00 | 1.00 | 152|
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- |Romansh_Sursilvan | 0.99 | 0.95 | 0.97 | 106|
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- | Russian | 0.99 | 0.99 | 0.99 | 100|
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- | Sakha | 0.99 | 1.00 | 1.00 | 105|
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- | Slovenian | 0.99 | 1.00 | 1.00 | 166|
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- | Spanish | 0.96 | 0.95 | 0.95 | 94|
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- | Swedish | 0.99 | 1.00 | 0.99 | 190|
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- | Tamil | 1.00 | 1.00 | 1.00 | 135|
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- | Tatar | 1.00 | 0.96 | 0.98 | 173|
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- | Turkish | 1.00 | 1.00 | 1.00 | 137|
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- | Ukranian | 0.99 | 1.00 | 1.00 | 126|
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- | Welsh | 0.98 | 1.00 | 0.99 | 103|
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  ||
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- | *macro avg* | 0.98 | 0.99 | 0.98 | 5963|
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- | *weighted avg* | 0.98 | 0.98 | 0.98 | 5963|
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- ||
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- | *overall accuracy* | | | 0.9837 | 5963|
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-
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  ### Evaluation Results
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  The test datasets refers to the **common_language** test datasets.
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+ |index| language | precision | recall | f1-score | support |
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+ | --- | --- | --- | ---| --- | --- |
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+ |0|Arabic|1.00|1.00|1.00|151|
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+ |1| Basque | 0.99 | 1.00 | 1.00 | 111|
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+ |2| Breton | 1.00 | 0.90 | 0.95 | 252|
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+ |3| Catalan | 0.96 | 0.99 | 0.97 | 96|
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+ |4| Chinese_China | 0.98 | 1.00 | 0.99 | 100|
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+ |5| Chinese_Hongkong | 0.97 | 0.87 | 0.92 | 115|
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+ |6| Chinese_Taiwan | 0.92 | 0.98 | 0.95 | 170|
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+ |7| Chuvash | 0.98 | 1.00 | 0.99 | 137|
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+ |8| Czech | 0.98 | 1.00 | 0.99 | 128|
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+ |9| Dhivehi | 1.00 | 1.00 | 1.00 | 111|
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+ |10| Dutch | 0.99 | 1.00 | 0.99 | 144|
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+ |11| English | 0.96 | 1.00 | 0.98 | 98|
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+ |12| Esperanto | 0.98 | 0.98 | 0.98 | 107|
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+ |13| Estonian | 1.00 | 0.99 | 0.99 | 93|
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+ |14| French | 0.95 | 1.00 | 0.98 | 106|
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+ |15| Frisian | 1.00 | 0.98 | 0.99 | 117|
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+ |16| Georgian | 1.00 | 1.00 | 1.00 | 110|
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+ |17| German | 1.00 | 1.00 | 1.00 | 101|
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+ |18| Greek | 1.00 | 1.00 | 1.00 | 153|
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+ |19| Hakha_Chin | 0.99 | 1.00 | 0.99 | 202|
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+ |20| Indonesian | 0.99 | 0.99 | 0.99 | 150|
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+ |21| Interlingua | 0.96 | 0.97 | 0.96 | 182|
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+ |22| Italian | 0.99 | 0.94 | 0.96 | 100|
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+ |23| Japanese | 1.00 | 1.00 | 1.00 | 144|
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+ |24| Kabyle | 1.00 | 0.96 | 0.98 | 156|
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+ |25| Kinyarwanda | 0.97 | 1.00 | 0.99 | 103|
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+ |26| Kyrgyz | 0.98 | 1.00 | 0.99 | 129|
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+ |27| Latvian | 0.98 | 0.98 | 0.98 | 171|
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+ |28| Maltese | 0.99 | 0.98 | 0.98 | 152|
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+ |29| Mongolian | 1.00 | 1.00 | 1.00 | 112|
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+ |30| Persian | 1.00 | 1.00 | 1.00 | 123|
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+ |31| Polish | 0.91 | 0.99 | 0.95 | 128|
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+ |32| Portuguese | 0.94 | 0.99 | 0.96 | 124|
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+ |33| Romanian | 1.00 | 1.00 | 1.00 | 152|
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+ |34|Romansh_Sursilvan | 0.99 | 0.95 | 0.97 | 106|
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+ |35| Russian | 0.99 | 0.99 | 0.99 | 100|
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+ |36| Sakha | 0.99 | 1.00 | 1.00 | 105|
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+ |37| Slovenian | 0.99 | 1.00 | 1.00 | 166|
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+ |38| Spanish | 0.96 | 0.95 | 0.95 | 94|
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+ |39| Swedish | 0.99 | 1.00 | 0.99 | 190|
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+ |40| Tamil | 1.00 | 1.00 | 1.00 | 135|
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+ |41| Tatar | 1.00 | 0.96 | 0.98 | 173|
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+ |42| Turkish | 1.00 | 1.00 | 1.00 | 137|
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+ |43| Ukranian | 0.99 | 1.00 | 1.00 | 126|
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+ |44| Welsh | 0.98 | 1.00 | 0.99 | 103|
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  ||
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+ || *macro avg* | 0.98 | 0.99 | 0.98 | 5963|
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+ || *weighted avg* | 0.98 | 0.98 | 0.98 | 5963|
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+ |||
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+ || *overall accuracy* | | | 0.9837 | 5963|
 
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