Multi-lingual BERT Bengali Name Entity Recognition
mBERT-Bengali-NER is a transformer-based Bengali NER model build with bert-base-multilingual-uncased model and Wikiann Datasets.
How to Use
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("sagorsarker/mbert-bengali-ner")
model = AutoModelForTokenClassification.from_pretrained("sagorsarker/mbert-bengali-ner")
nlp = pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True)
example = "আমি জাহিদ এবং আমি ঢাকায় বাস করি।"
ner_results = nlp(example)
print(ner_results)
Label and ID Mapping
| Label ID | Label |
|---|---|
| 0 | O |
| 1 | B-PER |
| 2 | I-PER |
| 3 | B-ORG |
| 4 | I-ORG |
| 5 | B-LOC |
| 6 | I-LOC |
Training Details
- mBERT-Bengali-NER trained with Wikiann datasets
- mBERT-Bengali-NER trained with transformers-token-classification script
- mBERT-Bengali-NER total trained 5 epochs.
- Trained in Kaggle GPU
Evaluation Results
| Model | F1 | Precision | Recall | Accuracy | Loss |
|---|---|---|---|---|---|
| mBert-Bengali-NER | 0.97105 | 0.96769 | 0.97443 | 0.97682 | 0.12511 |
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