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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: "রিয়াল মাদ্রিদ ফুটবল ক্লাব" |
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example_title: "Sentence_1" |
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- text: "উত্তরবঙ্গ কৃষি বিশ্ববিদ্যালয়" |
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example_title: "Sentence_2" |
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- text: "বাংলাদেশ জাতীয় ক্রিকেট দল" |
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example_title: "Sentence_3" |
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- text: "বাংলাদেশ টেলিকমিউনিকেশন্স কোম্পানী লিমিটেড" |
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example_title: "Sentence_4" |
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- text: "রোমিও অ্যান্ড জুলিয়েট" |
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example_title: "Sentence_5" |
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--- |
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<h1>Named Entity Recognition on Bangla Language</h1> |
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Fine Tuning BERT for NER on Bengali Language Tagging using HuggingFace |
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## Correspondence Label ID and 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>Evaluation and Validation</h1> |
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| Name | Precision | Recall | F1 | Accuracy | |
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| ---- | -------- | ----- | ---- | ---- | |
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| Train/Val set | 0.963899 | 0.964770 | 0.964334 | 0.981252 | |
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| Test set | 0.952855 | 0.965105 | 0.958941 | 0.981349 | |
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Transformers AutoModelForTokenClassification |
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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("engineersakibcse47/NER_on_Bangla_Language") |
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model_ner = AutoModelForTokenClassification.from_pretrained("engineersakibcse47/NER_on_Bangla_Language") |
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pipe = pipeline("ner", model=model_ner, tokenizer=tokenizer) |
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sample = "বসনিয়া ও হার্জেগোভিনা" |
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result = pipe(sample) |
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result |
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``` |
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