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---
license: apache-2.0
datasets:
- common_language
language:
- ar
- eu
- br
- ca
- zh
- cv
- cs
- nl
- en
- eo
- et
- fr
- ka
- de
- el
- id
- ia
- it
- ja
- rw
- ky
- lv
- mt
- mn
- fa
- pl
- pt
- ro
- rm
- ru
- sl
- es
- sv
- ta
- tt
- tr
- uk
- cy
metrics:
- accuracy
- precision
- recall
- f1
tags:
- language-detection
- Frisian
- Dhivehi
- Hakha_Chin
- Kabyle
- Sakha
---
### Overview
This model supports the detection of **45** languages, and it's fine-tuned using **multilingual-e5-base** model on the **common-language** dataset.<br>
The overall accuracy is **98.37%**, and more evaluation results are shown the below.
### Download the model
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained('Mike0307/multilingual-e5-language-detection')
model = AutoModelForSequenceClassification.from_pretrained('Mike0307/multilingual-e5-language-detection', num_labels=45)
```
### Example of language detection
```python
import torch
languages = [
"Arabic", "Basque", "Breton", "Catalan", "Chinese_China", "Chinese_Hongkong",
"Chinese_Taiwan", "Chuvash", "Czech", "Dhivehi", "Dutch", "English",
"Esperanto", "Estonian", "French", "Frisian", "Georgian", "German", "Greek",
"Hakha_Chin", "Indonesian", "Interlingua", "Italian", "Japanese", "Kabyle",
"Kinyarwanda", "Kyrgyz", "Latvian", "Maltese", "Mongolian", "Persian", "Polish",
"Portuguese", "Romanian", "Romansh_Sursilvan", "Russian", "Sakha", "Slovenian",
"Spanish", "Swedish", "Tamil", "Tatar", "Turkish", "Ukranian", "Welsh"
]
def predict(text, model, tokenizer, device = torch.device('cpu')):
model.to(device)
model.eval()
tokenized = tokenizer(text, padding='max_length', truncation=True, max_length=128, return_tensors="pt")
input_ids = tokenized['input_ids']
attention_mask = tokenized['attention_mask']
with torch.no_grad():
input_ids = input_ids.to(device)
attention_mask = attention_mask.to(device)
outputs = model(input_ids=input_ids, attention_mask=attention_mask)
logits = outputs.logits
probabilities = torch.nn.functional.softmax(logits, dim=1)
return probabilities
def get_topk(probabilities, languages, k=3):
topk_prob, topk_indices = torch.topk(probabilities, k)
topk_prob = topk_prob.cpu().numpy()[0].tolist()
topk_indices = topk_indices.cpu().numpy()[0].tolist()
topk_labels = [languages[index] for index in topk_indices]
return topk_prob, topk_labels
text = "你的測試句子"
probabilities = predict(text, model, tokenizer)
topk_prob, topk_labels = get_topk(probabilities, languages)
print(topk_prob, topk_labels)
# [0.999620258808, 0.00025940246996469, 2.7690215574693e-05]
# ['Chinese_Taiwan', 'Chinese_Hongkong', 'Chinese_China']
```
### Evaluation Results
The test datasets refers to the **common_language** test datasets.
|index| language | precision | recall | f1-score | support |
| --- | --- | --- | ---| --- | --- |
|0|Arabic|1.00|1.00|1.00|151|
|1| Basque | 0.99 | 1.00 | 1.00 | 111|
|2| Breton | 1.00 | 0.90 | 0.95 | 252|
|3| Catalan | 0.96 | 0.99 | 0.97 | 96|
|4| Chinese_China | 0.98 | 1.00 | 0.99 | 100|
|5| Chinese_Hongkong | 0.97 | 0.87 | 0.92 | 115|
|6| Chinese_Taiwan | 0.92 | 0.98 | 0.95 | 170|
|7| Chuvash | 0.98 | 1.00 | 0.99 | 137|
|8| Czech | 0.98 | 1.00 | 0.99 | 128|
|9| Dhivehi | 1.00 | 1.00 | 1.00 | 111|
|10| Dutch | 0.99 | 1.00 | 0.99 | 144|
|11| English | 0.96 | 1.00 | 0.98 | 98|
|12| Esperanto | 0.98 | 0.98 | 0.98 | 107|
|13| Estonian | 1.00 | 0.99 | 0.99 | 93|
|14| French | 0.95 | 1.00 | 0.98 | 106|
|15| Frisian | 1.00 | 0.98 | 0.99 | 117|
|16| Georgian | 1.00 | 1.00 | 1.00 | 110|
|17| German | 1.00 | 1.00 | 1.00 | 101|
|18| Greek | 1.00 | 1.00 | 1.00 | 153|
|19| Hakha_Chin | 0.99 | 1.00 | 0.99 | 202|
|20| Indonesian | 0.99 | 0.99 | 0.99 | 150|
|21| Interlingua | 0.96 | 0.97 | 0.96 | 182|
|22| Italian | 0.99 | 0.94 | 0.96 | 100|
|23| Japanese | 1.00 | 1.00 | 1.00 | 144|
|24| Kabyle | 1.00 | 0.96 | 0.98 | 156|
|25| Kinyarwanda | 0.97 | 1.00 | 0.99 | 103|
|26| Kyrgyz | 0.98 | 1.00 | 0.99 | 129|
|27| Latvian | 0.98 | 0.98 | 0.98 | 171|
|28| Maltese | 0.99 | 0.98 | 0.98 | 152|
|29| Mongolian | 1.00 | 1.00 | 1.00 | 112|
|30| Persian | 1.00 | 1.00 | 1.00 | 123|
|31| Polish | 0.91 | 0.99 | 0.95 | 128|
|32| Portuguese | 0.94 | 0.99 | 0.96 | 124|
|33| Romanian | 1.00 | 1.00 | 1.00 | 152|
|34|Romansh_Sursilvan | 0.99 | 0.95 | 0.97 | 106|
|35| Russian | 0.99 | 0.99 | 0.99 | 100|
|36| Sakha | 0.99 | 1.00 | 1.00 | 105|
|37| Slovenian | 0.99 | 1.00 | 1.00 | 166|
|38| Spanish | 0.96 | 0.95 | 0.95 | 94|
|39| Swedish | 0.99 | 1.00 | 0.99 | 190|
|40| Tamil | 1.00 | 1.00 | 1.00 | 135|
|41| Tatar | 1.00 | 0.96 | 0.98 | 173|
|42| Turkish | 1.00 | 1.00 | 1.00 | 137|
|43| Ukranian | 0.99 | 1.00 | 1.00 | 126|
|44| Welsh | 0.98 | 1.00 | 0.99 | 103|
||
|| *macro avg* | 0.98 | 0.99 | 0.98 | 5963|
|| *weighted avg* | 0.98 | 0.98 | 0.98 | 5963|
|||
|| *overall accuracy* | | | 0.9837 | 5963|
|