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--- |
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language: bn |
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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: "মারভিন দি মারসিয়ান" |
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example_title: "Sentence_1" |
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- text: "লিওনার্দো দা ভিঞ্চি" |
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example_title: "Sentence_2" |
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- text: "বসনিয়া ও হার্জেগোভিনা" |
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example_title: "Sentence_3" |
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- text: "সাউথ ইস্ট ইউনিভার্সিটি" |
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example_title: "Sentence_4" |
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- text: "মানিক বন্দ্যোপাধ্যায় লেখক" |
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example_title: "Sentence_5" |
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--- |
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<h1>Bengali Named Entity Recognition</h1> |
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Fine-tuning bert-base-multilingual-cased on Wikiann dataset for performing NER on Bengali language. |
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## Label ID and its corresponding label name |
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| Label ID | Label Name| |
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| -------- | ----- | |
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|0 | O | |
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| 1 | B-PER | |
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| 2 | I-PER | |
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| 3 | B-ORG| |
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| 4 | I-ORG | |
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| 5 | B-LOC | |
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| 6 | I-LOC | |
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<h1>Results</h1> |
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| Name | Overall F1 | LOC F1 | ORG F1 | PER F1 | |
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| ---- | -------- | ----- | ---- | ---- | |
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| Train set | 0.997927 | 0.998246 | 0.996613 | 0.998769 | |
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| Validation set | 0.970187 | 0.969212 | 0.956831 | 0.982079 | |
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| Test set | 0.9673011 | 0.967120 | 0.963614 | 0.970938 | |
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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("Suchandra/bengali_language_NER") |
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model = AutoModelForTokenClassification.from_pretrained("Suchandra/bengali_language_NER") |
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nlp = pipeline("ner", model=model, tokenizer=tokenizer) |
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example = "মারভিন দি মারসিয়ান" |
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ner_results = nlp(example) |
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ner_results |
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``` |
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