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--- |
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license: apache-2.0 |
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datasets: |
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- common_language |
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language: |
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- ar |
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- eu |
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- br |
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- ca |
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- zh |
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- cv |
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- cs |
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- nl |
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- en |
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- eo |
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- et |
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- fr |
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- ka |
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- de |
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- el |
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- id |
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- ia |
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- it |
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- ja |
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- rw |
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- ky |
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- lv |
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- mt |
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- mn |
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- fa |
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- pl |
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- pt |
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- ro |
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- rm |
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- ru |
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- sl |
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- es |
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- sv |
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- ta |
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- tt |
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- tr |
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- uk |
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- cy |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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tags: |
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- language-detection |
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- Frisian |
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- Dhivehi |
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- Hakha_Chin |
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- Kabyle |
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- Sakha |
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--- |
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### Overview |
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This model supports the detection of **45** languages, and it's fine-tuned using **multilingual-e5-base** model on the **common-language** dataset.<br> |
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The overall accuracy is **98.37%**, and more evaluation results are shown the below. |
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### Download the model |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained('Mike0307/multilingual-e5-language-detection') |
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model = AutoModelForSequenceClassification.from_pretrained('Mike0307/multilingual-e5-language-detection', num_labels=45) |
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``` |
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### Example of language detection |
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```python |
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import torch |
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languages = [ |
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"Arabic", "Basque", "Breton", "Catalan", "Chinese_China", "Chinese_Hongkong", |
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"Chinese_Taiwan", "Chuvash", "Czech", "Dhivehi", "Dutch", "English", |
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"Esperanto", "Estonian", "French", "Frisian", "Georgian", "German", "Greek", |
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"Hakha_Chin", "Indonesian", "Interlingua", "Italian", "Japanese", "Kabyle", |
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"Kinyarwanda", "Kyrgyz", "Latvian", "Maltese", "Mongolian", "Persian", "Polish", |
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"Portuguese", "Romanian", "Romansh_Sursilvan", "Russian", "Sakha", "Slovenian", |
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"Spanish", "Swedish", "Tamil", "Tatar", "Turkish", "Ukranian", "Welsh" |
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] |
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def predict(text, model, tokenizer, device = torch.device('cpu')): |
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model.to(device) |
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model.eval() |
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tokenized = tokenizer(text, padding='max_length', truncation=True, max_length=128, return_tensors="pt") |
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input_ids = tokenized['input_ids'] |
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attention_mask = tokenized['attention_mask'] |
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with torch.no_grad(): |
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input_ids = input_ids.to(device) |
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attention_mask = attention_mask.to(device) |
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outputs = model(input_ids=input_ids, attention_mask=attention_mask) |
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logits = outputs.logits |
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probabilities = torch.nn.functional.softmax(logits, dim=1) |
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return probabilities |
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def get_topk(probabilities, languages, k=3): |
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topk_prob, topk_indices = torch.topk(probabilities, k) |
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topk_prob = topk_prob.cpu().numpy()[0].tolist() |
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topk_indices = topk_indices.cpu().numpy()[0].tolist() |
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topk_labels = [languages[index] for index in topk_indices] |
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return topk_prob, topk_labels |
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text = "你的測試句子" |
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probabilities = predict(text, model, tokenizer) |
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topk_prob, topk_labels = get_topk(probabilities, languages) |
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print(topk_prob, topk_labels) |
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# [0.999620258808, 0.00025940246996469, 2.7690215574693e-05] |
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# ['Chinese_Taiwan', 'Chinese_Hongkong', 'Chinese_China'] |
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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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|| *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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|| *overall accuracy* | | | 0.9837 | 5963| |
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