Update README.md

README.md

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 ---
+language:
+  - bn
 license: cc-by-4.0
+library_name: transformers
+pipeline_tag: fill-mask
+tags:
+  - bengali
+  - bangla
+  - BnLM-F
+  - bnlm
+  - bangla-model
+  - small-bangla-model
+  - bengali-nlp
+  - bnlp
+  - low-resource
+  - masked-language-modeling
+  - pretrained
+  - language-model
+  - distilbert
+datasets:
+  - nahid-hub/B-CORE-bengali-corpus
+model-index:
+  - name: BnLM-F
+    results: []
 ---
+
+# BnLM-F: Bangla Language Model (135M)
+
+**BnLM-F** is a 135M-parameter Bangla-specific pretrained language model, one of three models in the **BnLM** suite (**B**e**n**gali **L**anguage **M**odel) introduced alongside the **BLUGE** benchmark and **B-CORE** pretraining corpus. BnLM models are pretrained from scratch on Bangla text using the Masked Language Modeling (MLM) objective, and are designed for efficient, low-resource NLP without relying on large multilingual models.
+
+See the [BLUGE collection](https://huggingface.co/collections/nahid-hub/bluge) for the full release — evaluation tasks, pretraining corpus, tokenizers, and all three BnLM variants (BnLM-F, BnLM-M, BnLM-C).
+
+## Model Details
+
+| | |
+|---|---|
+| Base architecture | DistilBERT-multilingual |
+| Parameters | 135M |
+| Tokenizer | Custom Bangla WordPiece, 120K vocabulary |
+| Objective | Masked Language Modeling (MLM) |
+| Max sequence length | 512 tokens |
+| Pretraining data | [B-CORE](https://huggingface.co/datasets/nahid-hub/B-CORE-bengali-corpus) — 4.32B tokens, 52GB |
+
+Despite its compact size, BnLM-F achieves state-of-the-art results across BLUGE and four cross-lingual/Indic benchmarks, while requiring 44–91% fewer FLOPs and ~2x faster inference than competitive multilingual baselines. Full training configuration and hyperparameters are available in the paper.
+
+## Usage
+
+### Fine-tuning for sentiment classification
+
+Example using [BLUGE-TSC](https://huggingface.co/datasets/nahid-hub/BLUGE-bengali-sentiment-classification):
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("nahid-hub/BnLM-F-135m")
+model = AutoModelForSequenceClassification.from_pretrained(
+    "nahid-hub/BnLM-F-135m",
+    num_labels=3  # 0: neutral, 1: positive, 2: negative
+)
+
+text = "খাবারটা অসাধারণ ছিল, আমি খুব খুশি!"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+predicted_class = logits.argmax(dim=-1).item()
+labels = {0: "neutral", 1: "positive", 2: "negative"}
+print(labels[predicted_class])
+```
+
+> Note: `AutoModelForSequenceClassification` attaches a randomly initialized classification head on top of the pretrained BnLM-F encoder — you'll need to fine-tune on labeled data (e.g. BLUGE-TSC) before this produces meaningful predictions. Skip straight to inference only if loading your own fine-tuned checkpoint.
+
+### Loading the base model (for masked-language-modeling or custom heads)
+
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+
+tokenizer = AutoTokenizer.from_pretrained("nahid-hub/BnLM-F-135m")
+model = AutoModelForMaskedLM.from_pretrained("nahid-hub/BnLM-F-135m")
+```
+
+## Training Data
+
+Pretrained exclusively on **B-CORE**, a 16.5M-document, 4.32-billion-token Bangla corpus built via a reproducible multi-stage pipeline achieving a 22.4% reduction in corpus volume through quality filtering and cross-corpus deduplication.
+
+## Evaluation
+
+Evaluated on **BLUGE** (7-task Bangla NLU benchmark) and 4 cross-lingual/Indic benchmarks. See the paper for full results tables.
+
+| Task | Metric | Score |
+|---|---|---|
+| [fill in from your paper's results tables] | | |
+
+## Related Models
+
+- [BnLM-M](https://huggingface.co/nahid-hub/BnLM-M-135m) — 135M parameters, larger-vocabulary variant tuned for different training regime
+- [BnLM-C](https://huggingface.co/nahid-hub/BnLM-C-66m) — 66M parameters, compact variant with 30.5K-vocabulary tokenizer
+
+## Limitations
+
+[e.g. domain coverage of B-CORE, max sequence length, any known biases — fill in from your paper's limitations section]
+
+## License
+
+Released under **CC BY 4.0**.
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@ARTICLE{BnLM-BLUGE-B-CORE,
+  author={Hossain, Nahid and Faisal Kabir, Md.},
+  journal={IEEE Access}, 
+  title={Efficient Monolingual Pretraining in Low-Resource Settings Through Morphology-Aware Tokenization, Principled Corpus Denoising, and Benchmark-Driven Evaluation}, 
+  year={2026},
+  volume={14},
+  number={},
+  pages={91979-92003},
+  keywords={Modeling;Multilingual;Training;Cleaning;Vocabulary;Labeling;Tokenization;Computational linguistics;Pipelines;Conferences;B-CORE;BLUGE;BnLM;corpus;evaluation benchmark;pretrained models},
+  doi={10.1109/ACCESS.2026.3701520}
+}
+```