resume-summerization-classification/bangla_ai_detector

Bangla AI vs Human Writing Detector — BanglaBERT

Fine-tuned BanglaBERT for binary classification of Bangla text as AI-generated or human-written. Developed as part of the Onneshon thesis project at Bangladesh University of Professionals (BUP).

Given the full text of a Bangla resume, the model predicts whether it was written by a human or generated by an AI system, along with a confidence score.

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Model Details

Property	Value
Base model	csebuetnlp/banglabert
Architecture	ElectraForSequenceClassification (ELECTRA-based BERT)
Parameters	12 layers, 768 hidden dim, 12 attention heads, vocab 32,000
Language	Bengali (bn)
Task	Binary sequence classification
Labels	0 → Human, 1 → AI
Max input tokens	512
Training data	70 resumes (35 AI + 35 Human)
Validation data	~15 resumes (stratified)
Test data	~15 resumes (stratified)
Epochs	5 (with EarlyStoppingCallback, patience=2)
Learning rate	2e-5
Batch size	8
Warmup ratio	0.1
Weight decay	0.01
Mixed precision	fp16
Best model metric	F1 (binary)

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Dataset

Trained on Onneshon — an original Bangla resume dataset:

• Human resumes: 50 resumes written by Bangladeshi professionals (resume_51–resume_100)
• AI resumes: 50 resumes generated by AI systems (resume_1–resume_50)
• Total: 100 resumes, split 70/15/15 (train/val/test), stratified

Published on Mendeley Data: DOI: 10.17632/4md7bx6fd7.1

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Labels

ID	Label	Description
0	Human	Resume written by a human
1	AI	Resume generated by an AI system

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Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("your-username/bangla-ai-detector")
model     = AutoModelForSequenceClassification.from_pretrained("your-username/bangla-ai-detector")
model.eval()

def predict(text):
    inputs  = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
    with torch.no_grad():
        logits = model(**inputs).logits
    probs  = torch.softmax(logits, dim=1)[0]
    label  = model.config.id2label[logits.argmax().item()]
    return {
        "label":        label,
        "confidence":   f"{probs.max().item()*100:.1f}%",
        "P(Human)":     f"{probs[0].item()*100:.1f}%",
        "P(AI)":        f"{probs[1].item()*100:.1f}%",
    }

# Example
resume_text = "আমি একজন অভিজ্ঞ সফ্টওয়্যার ইঞ্জিনিয়ার। গত পাঁচ বছর ধরে জাভা এবং স্প্রিং বুট দিয়ে কাজ করছি।"
print(predict(resume_text))
# Output: {'label': 'Human', 'confidence': '87.3%', 'P(Human)': '87.3%', 'P(AI)': '12.7%'}

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Preprocessing

Before passing text to the model, strip any annotation tags if present (these are specific to the Onneshon dataset format):

import re

def clean_resume(text):
    text = re.sub(r'\[Info_Start\].*?\[Info_End\]', '', text, flags=re.DOTALL)
    text = re.sub(r'\[(Objective|Experience|Expericence|Education|Skill|section)\]', '', text)
    text = re.sub(r'\s+', ' ', text).strip()
    return text

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Limitations

• Small dataset: Only 100 resumes (70 training). Binary accuracy estimates have high variance — results should be interpreted with caution.
• Domain-specific: Trained exclusively on Bangla resumes. Performance on other Bangla document types (news, social media, etc.) is untested.
• AI source unknown: The AI resumes were generated by a specific AI system. The model may not generalize to resumes generated by different LLMs not seen during training.
• Token limit: Resumes longer than 512 tokens are truncated. Very long resumes may lose tail content.
• Binary only: Cannot distinguish which AI system generated the text — only Human vs AI.

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Citation

@misc{onneshon2026,
  title     = {Onneshon: A Bangla Resume NLP Dataset},
  author    = {Tanvir and Shruti Khisa and Shaira Akther Diba and Fazli Rabbi Noor},
  year      = {2026},
  doi       = {10.17632/4md7bx6fd7.1},
  publisher = {Mendeley Data}
}

@inproceedings{bhattacharjee-etal-2022-banglabert,
  title     = {BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla},
  author    = {Bhattacharjee, Abhik and Hasan, Tahmid and Ahmad, Wasi and Mubasshir, Kazi Samin and Islam, Md Saiful and Iqbal, Anindya and Rahman, M. Sohel and Shahriyar, Rifat},
  booktitle = {Findings of the Association for Computational Linguistics: NAACL 2022},
  year      = {2022},
  pages     = {1318--1327}
}

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Project

Part of the Onneshon thesis project — a Bangla NLP pipeline for resume processing.

• Dataset: Mendeley Data DOI: 10.17632/4md7bx6fd7.1
• Institution: Bangladesh University of Professionals (BUP), Dhaka, Bangladesh
• Supervisor: Rumana Yasmin, Lecturer, CSE Department, BUP