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| from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| from languages import LANGUANGE_MAP | |
| import gradio as gr | |
| import torch | |
| model_ckpt = "ivanlau/language-detection-fine-tuned-on-xlm-roberta-base" | |
| model = AutoModelForSequenceClassification.from_pretrained(model_ckpt) | |
| tokenizer = AutoTokenizer.from_pretrained(model_ckpt) | |
| def detect_language(sentence): | |
| tokenized_sentence = tokenizer(sentence, return_tensors='pt') | |
| output = model(**tokenized_sentence) | |
| predictions = torch.nn.functional.softmax(output.logits, dim=-1) | |
| _, preds = torch.max(predictions, dim=-1) | |
| return LANGUANGE_MAP[preds.item()] | |
| examples = [ | |
| "I've been waiting for a HuggingFace course my whole life.", | |
| "恭喜发财!", | |
| "Jumpa lagi, saya pergi kerja.", | |
| "你食咗飯未呀?", | |
| "もう食べましたか?", | |
| "as-tu mangé", | |
| "أريد أن ألعب كرة الريشة" | |
| ] | |
| inputs=gr.inputs.Textbox(placeholder="Enter your text here", label="Text content", lines=5) | |
| outputs=gr.outputs.Label(label="Language detected:") | |
| article = """ | |
| Fine-tuned on xlm-roberta-base model.\n | |
| Supported languages:\n | |
| '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', | |
| 'Mangolian', 'Persian', 'Polish', 'Portuguese', 'Romanian', 'Romansh_Sursilvan', 'Russian', 'Sakha', 'Slovenian', | |
| 'Spanish', 'Swedish', 'Tamil', 'Tatar', 'Turkish', 'Ukranian', 'Welsh' | |
| """ | |
| gr.Interface( | |
| fn=detect_language, | |
| inputs=inputs, | |
| outputs=outputs, | |
| verbose=True, | |
| examples = examples, | |
| title="Language Detector 🔠", | |
| description="A simple interface to detect 45 languages.", | |
| article=article, | |
| theme="huggingface" | |
| ).launch() | |