--- language: - ur tags: - ner --- # NER in Urdu ## muril_base_cased_urdu_ner Base model is [google/muril-base-cased](https://huggingface.co./google/muril-base-cased), a BERT model pre-trained on 17 Indian languages and their transliterated counterparts. Urdu NER dataset is translated from the Hindi NER dataset from [HiNER](https://github.com/cfiltnlp/HiNER). ## Usage ### example: ```python from transformers import AutoModelForTokenClassification, AutoTokenizer import torch model = AutoModelForTokenClassification.from_pretrained("MichaelHuang/muril_base_cased_urdu_ner") tokenizer = AutoTokenizer.from_pretrained("google/muril-base-cased") # Define the labels dictionary labels_dict = { 0: "B-FESTIVAL", 1: "B-GAME", 2: "B-LANGUAGE", 3: "B-LITERATURE", 4: "B-LOCATION", 5: "B-MISC", 6: "B-NUMEX", 7: "B-ORGANIZATION", 8: "B-PERSON", 9: "B-RELIGION", 10: "B-TIMEX", 11: "I-FESTIVAL", 12: "I-GAME", 13: "I-LANGUAGE", 14: "I-LITERATURE", 15: "I-LOCATION", 16: "I-MISC", 17: "I-NUMEX", 18: "I-ORGANIZATION", 19: "I-PERSON", 20: "I-RELIGION", 21: "I-TIMEX", 22: "O" } def ner_predict(sentence, model, tokenizer, labels_dict): # Tokenize the input sentence inputs = tokenizer(sentence, return_tensors="pt", padding=True, truncation=True, max_length=128) # Perform inference with torch.no_grad(): outputs = model(**inputs) # Get the predicted labels predicted_labels = torch.argmax(outputs.logits, dim=2) # Convert tokens and labels to lists tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0]) labels = predicted_labels.squeeze().tolist() # Map numeric labels to string labels predicted_labels = [labels_dict[label] for label in labels] # Combine tokens and labels result = list(zip(tokens, predicted_labels)) return result test_sentence = "امیتابھ اور ریکھا کی فلم 'گنگا کی سوگندھ' 10 فروری سنہ 1978 کو ریلیز ہوئی تھی۔ اس کے بعد راکھی، رندھیر کپور اور نیتو سنگھ کے ساتھ 'قسمے وعدے' 21 اپریل 1978 کو ریلیز ہوئی۔" predictions = ner_predict(test_sentence, model, tokenizer, labels_dict) for token, label in predictions: print(f"{token}: {label}") ```