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| from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| import torch | |
| import gradio as gr | |
| model_path = "jb2k/bert-base-multilingual-cased-language-detection" | |
| model = AutoModelForSequenceClassification.from_pretrained(model_path) | |
| tokenizer = AutoTokenizer.from_pretrained(model_path) | |
| language_dict = {0: 'Arabic', | |
| 1: 'Basque', | |
| 2: 'Breton', | |
| 3: 'Catalan', | |
| 4: 'Chinese_China', | |
| 5: 'Chinese_Hongkong', | |
| 6: 'Chinese_Taiwan', | |
| 7: 'Chuvash', | |
| 8: 'Czech', | |
| 9: 'Dhivehi', | |
| 10: 'Dutch', | |
| 11: 'English', | |
| 12: 'Esperanto', | |
| 13: 'Estonian', | |
| 14: 'French', | |
| 15: 'Frisian', | |
| 16: 'Georgian', | |
| 17: 'German', | |
| 18: 'Greek', | |
| 19: 'Hakha_Chin', | |
| 20: 'Indonesian', | |
| 21: 'Interlingua', | |
| 22: 'Italian', | |
| 23: 'Japanese', | |
| 24: 'Kabyle', | |
| 25: 'Kinyarwanda', | |
| 26: 'Kyrgyz', | |
| 27: 'Latvian', | |
| 28: 'Maltese', | |
| 29: 'Mongolian', | |
| 30: 'Persian', | |
| 31: 'Polish', | |
| 32: 'Portuguese', | |
| 33: 'Romanian', | |
| 34: 'Romansh_Sursilvan', | |
| 35: 'Russian', | |
| 36: 'Sakha', | |
| 37: 'Slovenian', | |
| 38: 'Spanish', | |
| 39: 'Swedish', | |
| 40: 'Tamil', | |
| 41: 'Tatar', | |
| 42: 'Turkish', | |
| 43: 'Ukranian', | |
| 44: 'Welsh'} | |
| examples = ['Transformers are really cool!', 'Трансформеры действительно классные!', '¡Los transformadores son realmente geniales!'] | |
| def inference(sentence): | |
| tokenized_sentence = tokenizer(sentence, return_tensors='pt') | |
| output = model(**tokenized_sentence) | |
| predictions = torch.nn.functional.softmax(output.logits, dim=-1) | |
| certainy, highest_value = torch.max(predictions, dim=-1, keepdim=False, out=None) | |
| highest_value_int = highest_value.item() | |
| language = language_dict[highest_value_int] | |
| return language | |
| if __name__ == '__main__': | |
| interFace = gr.Interface(fn=inference, | |
| inputs=gr.inputs.Textbox(placeholder="Enter text here", label="Text content", lines=5), | |
| outputs=gr.outputs.Label(num_top_classes=6, label="Language of this text is "), | |
| verbose=True, | |
| examples = examples, | |
| title="Language Detector", | |
| description="Language detector with support for 45 languages. Created as part of the huggingface course community event.", | |
| theme="grass") | |
| interFace.launch() | |