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Bengali Named Entity Recognition

Fine-tuning bert-base-multilingual-cased on Wikiann dataset for performing NER on Bengali language.

Label ID and its corresponding label name

Label ID Label Name
0 O
1 B-PER
2 I-PER
3 B-ORG
4 I-ORG
5 B-LOC
6 I-LOC

Results

Name Overall F1 LOC F1 ORG F1 PER F1
Train set 0.997927 0.998246 0.996613 0.998769
Validation set 0.970187 0.969212 0.956831 0.982079
Test set 0.9673011 0.967120 0.963614 0.970938

Example

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("Suchandra/bengali_language_NER")
model = AutoModelForTokenClassification.from_pretrained("Suchandra/bengali_language_NER")

nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "মারভিন দি মারসিয়ান"

ner_results = nlp(example)
ner_results
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Dataset used to train Suchandra/bengali_language_NER