Add fine-tuned Bangla NER model (v1, F1=74.93%)

README.md

@@ -1,3 +1,183 @@
 ---
-license: mit
+language:
+- bn
+license: apache-2.0
+tags:
+- token-classification
+- ner
+- bangla
+- bengali
+- electra
+- transformers
+datasets:
+- custom
+metrics:
+- f1
+- precision
+- recall
+- accuracy
+base_model: csebuetnlp/banglabert
+pipeline_tag: token-classification
+model-index:
+- name: bangla-ner
+  results:
+  - task:
+      type: token-classification
+      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) |
+| 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="kaiserahmed/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 = "kaiserahmed/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/kaiserahmed/BanglaTag}
+}
+```

classification_report.txt

@@ -0,0 +1,19 @@
+              precision    recall  f1-score   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
+weighted avg     0.7602    0.7406    0.7498      3142

config.json

@@ -0,0 +1,91 @@
+{
+  "add_cross_attention": false,
+  "architectures": [
+    "ElectraForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": null,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "embedding_size": 768,
+  "eos_token_id": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "O",
+    "1": "B-PER",
+    "2": "I-PER",
+    "3": "B-LOC",
+    "4": "I-LOC",
+    "5": "B-ORG",
+    "6": "I-ORG",
+    "7": "B-POL",
+    "8": "I-POL",
+    "9": "B-DATE",
+    "10": "I-DATE",
+    "11": "B-TIME",
+    "12": "I-TIME",
+    "13": "B-EVENT",
+    "14": "I-EVENT",
+    "15": "B-CRIME",
+    "16": "I-CRIME",
+    "17": "B-TITLE",
+    "18": "I-TITLE",
+    "19": "B-NUM",
+    "20": "I-NUM",
+    "21": "B-SYMBOL",
+    "22": "I-SYMBOL",
+    "23": "B-CONSTITUENCY",
+    "24": "I-CONSTITUENCY",
+    "25": "B-INST",
+    "26": "I-INST"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "is_decoder": false,
+  "label2id": {
+    "B-CONSTITUENCY": 23,
+    "B-CRIME": 15,
+    "B-DATE": 9,
+    "B-EVENT": 13,
+    "B-INST": 25,
+    "B-LOC": 3,
+    "B-NUM": 19,
+    "B-ORG": 5,
+    "B-PER": 1,
+    "B-POL": 7,
+    "B-SYMBOL": 21,
+    "B-TIME": 11,
+    "B-TITLE": 17,
+    "I-CONSTITUENCY": 24,
+    "I-CRIME": 16,
+    "I-DATE": 10,
+    "I-EVENT": 14,
+    "I-INST": 26,
+    "I-LOC": 4,
+    "I-NUM": 20,
+    "I-ORG": 6,
+    "I-PER": 2,
+    "I-POL": 8,
+    "I-SYMBOL": 22,
+    "I-TIME": 12,
+    "I-TITLE": 18,
+    "O": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "electra",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "summary_activation": "gelu",
+  "summary_last_dropout": 0.1,
+  "summary_type": "first",
+  "summary_use_proj": true,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.0",
+  "type_vocab_size": 2,
+  "use_cache": false,
+  "vocab_size": 32000
+}

model.safetensors

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+size 440213956

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "full_tokenizer_file": null,
+  "is_local": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": false,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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