Mike0307/multilingual-e5-language-detection
13571
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
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
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.