| ---
|
| language:
|
| - bn
|
| license: apache-2.0
|
| tags:
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| - token-classification
|
| - ner
|
| - bangla
|
| - bengali
|
| - electra
|
| - transformers
|
| datasets:
|
| - custom
|
| metrics:
|
| - f1
|
| - precision
|
| - recall
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| - accuracy
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| base_model: csebuetnlp/banglabert
|
| pipeline_tag: token-classification
|
| model-index:
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| - name: bangla-ner
|
| results:
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| - task:
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| type: token-classification
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| name: Named Entity Recognition
|
| metrics:
|
| - type: f1
|
| value: 0.7493
|
| - type: precision
|
| value: 0.7582
|
| - type: recall
|
| value: 0.7406
|
| - type: accuracy
|
| value: 0.9341
|
| ---
|
|
|
| # Bangla NER — Named Entity Recognition for Bengali
|
|
|
| A fine-tuned token classification model for **Bengali (Bangla) Named Entity Recognition** using the BIO tagging scheme.
|
| Built on top of [csebuetnlp/banglabert](https://huggingface.co/csebuetnlp/banglabert) (ELECTRA-based).
|
|
|
| ---
|
|
|
| ## Entity Types
|
|
|
| | Tag | Description | Example |
|
| |---|---|---|
|
| | `PER` | Person names | একেএম শহীদুল হক |
|
| | `LOC` | Locations, cities, countries | বাংলাদেশ, ঢাকা |
|
| | `ORG` | Organizations, companies | টুইটার, রিয়াল মাদ্রিদ |
|
| | `POL` | Political entities / parties | আওয়ামী লীগ |
|
| | `DATE` | Calendar dates | সোমবার, ২০২৪ সালে |
|
| | `TIME` | Times of day | সকাল ৮টায় |
|
| | `EVENT` | Named events | রোহিঙ্গা সঙ্কট |
|
| | `CRIME` | Crime-related entities | হত্যা মামলা |
|
| | `TITLE` | Titles, designations | মহাপরিদর্শক |
|
| | `NUM` | Numbers, quantities | ৯৩ শতাংশ |
|
| | `SYMBOL` | Symbols, currencies | ৳, % |
|
| | `CONSTITUENCY` | Electoral constituencies | ঢাকা-১৮ |
|
| | `INST` | Institutions | তথ্য অধিদপ্তর |
|
|
|
| All tags follow **BIO** format: `B-` (beginning), `I-` (inside), `O` (outside).
|
|
|
| ---
|
|
|
| ## Training Details
|
|
|
| | Parameter | Value |
|
| |---|---|
|
| | Base model | `csebuetnlp/banglabert` |
|
| | Architecture | ELECTRA (discriminator) |
|
| | Task | Token Classification (NER) |
|
| | Dataset size | 22,144 sentences |
|
| | Train split | 85% (18,822) |
|
| | Validation split | 7.5% (1,661) |
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| | Test split | 7.5% (1,661) |
|
| | Max sequence length | 256 tokens |
|
| | Batch size | 16 |
|
| | Epochs | 8 (early stopping, patience=2) |
|
| | Best epoch | 7 |
|
| | Learning rate | 2e-5 |
|
| | LR scheduler | Linear with warmup |
|
| | Warmup steps | 10% of total steps |
|
| | Weight decay | 0.01 |
|
| | Optimizer | AdamW |
|
| | Mixed precision | fp16 |
|
| | Framework | PyTorch + HuggingFace Transformers |
|
| | Hardware | NVIDIA GeForce RTX 4070 Ti SUPER (16 GB) |
|
|
|
| ---
|
|
|
| ## Test Set Results (Overall)
|
|
|
| | Metric | Score |
|
| |---|---|
|
| | **F1** | **74.93%** |
|
| | **Precision** | **75.82%** |
|
| | **Recall** | **74.06%** |
|
| | **Token Accuracy** | **93.41%** |
|
|
|
| ---
|
|
|
| ## Per-Entity Results (Test Set)
|
|
|
| | Entity | Precision | Recall | F1 | Support |
|
| |---|---|---|---|---|
|
| | CONSTITUENCY | 0.8333 | 0.7500 | 0.7895 | 20 |
|
| | CRIME | 0.9489 | 0.9489 | 0.9489 | 137 |
|
| | DATE | 0.7730 | 0.7552 | 0.7640 | 478 |
|
| | EVENT | 0.6827 | 0.6514 | 0.6667 | 109 |
|
| | INST | 0.7119 | 0.7636 | 0.7368 | 55 |
|
| | LOC | 0.7451 | 0.7245 | 0.7347 | 795 |
|
| | NUM | 0.6949 | 0.8913 | 0.7810 | 46 |
|
| | ORG | 0.5617 | 0.5686 | 0.5652 | 408 |
|
| | PER | 0.7654 | 0.7260 | 0.7452 | 719 |
|
| | POL | 0.8182 | 0.8333 | 0.8257 | 54 |
|
| | SYMBOL | 1.0000 | 0.8750 | 0.9333 | 8 |
|
| | TIME | 0.9839 | 0.8472 | 0.9104 | 144 |
|
| | TITLE | 0.9532 | 0.9645 | 0.9588 | 169 |
|
| | **micro avg** | **0.7582** | **0.7406** | **0.7493** | 3142 |
|
| | **macro avg** | 0.8056 | 0.7923 | 0.7969 | 3142 |
|
|
|
| ---
|
|
|
| ## Usage
|
|
|
| ### With pipeline (recommended)
|
|
|
| ```python
|
| from transformers import pipeline
|
|
|
| ner = pipeline(
|
| "ner",
|
| model="arafatfahim/BanglaTag",
|
| aggregation_strategy="simple",
|
| )
|
|
|
| text = "একেএম শহীদুল হক বাংলাদেশে কক্সবাজার এলাকায় সোমবার সংবাদ সম্মেলন করেন"
|
| print(ner(text))
|
| ```
|
|
|
| ### Manual inference
|
|
|
| ```python
|
| from transformers import AutoTokenizer, AutoModelForTokenClassification
|
| import torch
|
|
|
| model_name = "arafatfahim/BanglaTag"
|
| tokenizer = AutoTokenizer.from_pretrained(model_name)
|
| model = AutoModelForTokenClassification.from_pretrained(model_name)
|
| model.eval()
|
|
|
| tokens = ["একেএম", "শহীদুল", "হক", "বাংলাদেশে", "এসেছেন"]
|
| inputs = tokenizer(tokens, is_split_into_words=True, return_tensors="pt")
|
|
|
| with torch.no_grad():
|
| logits = model(**inputs).logits
|
|
|
| predictions = logits.argmax(-1)[0]
|
| word_ids = inputs.word_ids()
|
|
|
| prev = None
|
| for word_id, pred_id in zip(word_ids, predictions):
|
| if word_id is None or word_id == prev:
|
| continue
|
| print(f"{tokens[word_id]:20s} → {model.config.id2label[pred_id.item()]}")
|
| prev = word_id
|
| ```
|
|
|
| ---
|
|
|
| ## Citation
|
|
|
| If you use this model, please cite:
|
|
|
| ```bibtex
|
| @misc{bangla-ner-2026,
|
| title = {Bangla NER: Fine-tuned BanglaBERT for Bengali Named Entity Recognition},
|
| year = {2026},
|
| url = {https://huggingface.co/arafatfahim/BanglaTag}
|
| }
|
| ```
|
|
|
