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--- |
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language: mr |
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datasets: |
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- wikiann |
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examples: |
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widget: |
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- text: "राज्यसभा निवडणुकांसाठी उद्या मुंबईत मतदान होणार आहे." |
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example_title: "Sentence_1" |
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- text: "विराट कोहली भारताकडून खेळतो." |
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example_title: "Sentence_2" |
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- text: "नवी दिल्ली ही भारताची राजधानी आहे" |
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example_title: "Sentence_3" |
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--- |
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<h1>Marathi Named Entity Recognition Model trained using transfer learning</h1> |
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Fine-tuning bert-base-multilingual-cased on Wikiann dataset for performing NER on Marathi language. |
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## Label ID and its corresponding label name |
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Label list: (0), B-PER (1), I-PER (2), B-ORG (3), I-ORG (4), B-LOC (5), I-LOC (6) |
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Example |
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```py |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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from transformers import pipeline |
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tokenizer = AutoTokenizer.from_pretrained("lakshaywadhwa1993/ner_marathi_bert") |
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model = AutoModelForTokenClassification.from_pretrained("lakshaywadhwa1993/ner_marathi_bert") |
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nlp = pipeline("ner", model=model, tokenizer=tokenizer) |
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example = ["राज्यसभा","निवडणुकांसाठी","मुंबईत","भाजपचे" ,"चिंचवडचे", "आमदार", "लक्ष्मण", "जगताप"] |
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results = nlp(example) |
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results |
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``` |
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