| # Part of Speech tagging Model for Telugu | |
| #### How to use | |
| Use the below script from your python terminal as the web interface for inference has few encoding issues for Telugu | |
| PS: If you find my model useful, I would appreciate a note from you as it would encourage me to continue improving it and also add new models. | |
| ```python | |
| from simpletransformers.ner import NERModel | |
| model = NERModel('bert', | |
| 'kuppuluri/telugu_bertu_pos', | |
| args={"use_multiprocessing": False}, | |
| labels=[ | |
| 'QC', 'JJ', 'NN', 'QF', 'RDP', 'O', | |
| 'NNO', 'PRP', 'RP', 'VM', 'WQ', | |
| 'PSP', 'UT', 'CC', 'INTF', 'SYMP', | |
| 'NNP', 'INJ', 'SYM', 'CL', 'QO', | |
| 'DEM', 'RB', 'NST', ], | |
| use_cuda=False) | |
| text = "విరాట్ కోహ్లీ కూడా అదే నిర్లక్ష్యాన్ని ప్రదర్శించి కేవలం ఒక పరుగుకే రనౌటై పెవిలియన్ చేరాడు ." | |
| results = model.predict([text]) | |
| ``` | |
| ## Training data | |
| Training data is from https://github.com/anikethjr/NER_Telugu | |
| ## Eval results | |
| On the test set my results were | |
| eval_loss = 0.0036797842364565416 | |
| f1_score = 0.9983795127912227 | |
| precision = 0.9984325602401637 | |
| recall = 0.9983264709788816 | |