---
language: bn
license: mit
tags:
  - summarization
  - bangla
  - bengali
  - resume
  - seq2seq
  - fine-tuned
  - multilingual
base_model: csebuetnlp/mT5_multilingual_XLSum
metrics:
  - rouge
pipeline_tag: summarization
---

# Bangla Resume Summarizer — mT5

Fine-tuned [mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) for **abstractive summarization of Bangla-language resumes**, section by section. Developed as part of the *Onneshon* thesis project at Bangladesh University of Professionals (BUP).

Given the raw sentences of a resume section (Objective, Experience, Education, or Skill), the model generates a fluent 2–3 sentence Bangla summary with strong semantic alignment to human-verified references.

---

## Model Details

| Property | Value |
|---|---|
| Base model | `csebuetnlp/mT5_multilingual_XLSum` |
| Architecture | MT5ForConditionalGeneration (encoder-decoder) |
| Parameters | 12 encoder layers, 12 decoder layers, 768 hidden dim, vocab 250,112 |
| Language | Bengali (bn) |
| Task | Abstractive summarization (per section) |
| Language token | `<2bn>` prepended to all inputs (required by mT5_multilingual_XLSum) |
| Training data | 317 resume-summary pairs (80% of Onneshon dataset) |
| Validation data | 79 resume-summary pairs (20% of Onneshon dataset) |
| Epochs | 5 (with early stopping, patience=2) |
| Learning rate | 5e-4 |
| Batch size | 4 |
| Max input tokens | 512 |
| Max output tokens | 128 |

---

## Dataset

Trained on **Onneshon** — an original Bangla resume dataset of 100 annotated resumes spanning 20+ professions.  
Published on Mendeley Data: [DOI: 10.17632/4md7bx6fd7.1](https://doi.org/10.17632/4md7bx6fd7.1)

Reference summaries were generated using GPT-OSS-120B (via OpenRouter) and human-verified, producing 396 section-level summary pairs across 4 categories: Objective, Experience, Education, Skill.

---

## Evaluation Results

Evaluated against human-verified abstractive reference summaries using a Bengali-aware ROUGE tokenizer and semantic similarity:

| Metric | Score |
|---|---|
| ROUGE-1 | 0.6171 |
| ROUGE-2 | 0.4361 |
| ROUGE-L | 0.5384 |
| Semantic Similarity (paraphrase-multilingual-MiniLM-L12-v2) | **0.8184** |

> **Note on ROUGE scores:** ROUGE measures exact word overlap. Since references are abstractive paraphrases and Bangla has rich morphology, ROUGE can still underestimate quality relative to semantic similarity. Semantic similarity of **0.818** — the highest among all 4 methods evaluated — confirms this model produces summaries most aligned in *meaning* with human-verified references.

**Per-section ROUGE-1 and Semantic Similarity:**

| Section | ROUGE-1 | Semantic Sim |
|---|---|---|
| Objective | 0.6697 | **0.9120** |
| Experience | 0.4892 | 0.7209 |
| Education | 0.7557 | 0.8585 |
| Skill | 0.5487 | 0.7782 |

---

## Comparison: BanglaT5 vs mT5

| Metric | BanglaT5 | mT5 | Better for |
|---|---|---|---|
| ROUGE-1 | **0.620** | 0.617 | BanglaT5 (marginal) |
| Semantic Sim | 0.808 | **0.818** | **mT5** |
| Objective Sem | 0.848 | **0.912** | **mT5** |
| Experience R1 | **0.504** | 0.489 | BanglaT5 |

**mT5 produces more fluent, semantically natural summaries** (higher semantic sim overall). BanglaT5 has higher word-level fidelity (higher ROUGE) for factual sections. Use mT5 when fluency and meaning alignment are priorities.

---

## Usage

> ⚠️ **Important:** This model requires the Bengali language token `<2bn>` prepended to every input. Without it, the model defaults to non-Bengali output.

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

LANG_TOKEN = "<2bn>"

tokenizer = AutoTokenizer.from_pretrained("your-username/bangla-resume-summarizer-mt5", use_fast=False)
model     = AutoModelForSeq2SeqLM.from_pretrained("your-username/bangla-resume-summarizer-mt5")
model.eval()

def summarize(text):
    # Prepend Bengali language token — required for this model
    input_text = LANG_TOKEN + " " + text.strip()
    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=512)
    outputs = model.generate(
        inputs["input_ids"],
        max_length=128,
        min_length=20,
        num_beams=4,
        length_penalty=1.2,
        repetition_penalty=2.0,
        no_repeat_ngram_size=3,
        early_stopping=True
    )
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example — Objective section
text = "অভিজ্ঞতা সম্পন্ন সফ্টওয়্যার ইঞ্জিনিয়ার হিসেবে একটি ডায়নামিক টিমে যোগদান করা যেখানে আমার জাভা এবং স্প্রিং ফ্রেমওয়ার্কের দক্ষতা কাজে লাগিয়ে প্রতিষ্ঠানের সাফল্যে অবদান রাখতে পারি"
print(summarize(text))
```

---

## Input Format

Feed one section at a time. Join multiple sentences with ` । ` (Bengali danda). The `<2bn>` token is added automatically in the usage example above:

```
<2bn> sentence_1 । sentence_2 । sentence_3
```

---

## Limitations

- Trained on only 317 pairs — a small dataset by deep learning standards. Outputs may be generic for unusual professions.
- Optimized for resume text. Performance on other Bangla document types is untested.
- Not suitable for very long inputs (>512 tokens); truncation will occur.
- The `<2bn>` language token is mandatory — omitting it will cause incorrect (non-Bengali) output.
- Compared to BanglaT5, this model has lower ROUGE on factual sections (Experience, Skill) but higher semantic similarity overall.

---

## Citation

If you use this model, please cite the Onneshon dataset and XL-Sum:

```bibtex
@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{hasan-etal-2021-xl,
  title     = {XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages},
  author    = {Hasan, Tahmid and Bhattacharjee, Abhik and Islam, Md. Saiful and Mubasshir, Kazi and Li, Yuan-Fang and Kang, Yong-Bin and Rahman, M. Sohel and Shahriyar, Rifat},
  booktitle = {Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021},
  year      = {2021},
  pages     = {4693--4703}
}
```

---

## Project

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

- Dataset: [Mendeley Data DOI: 10.17632/4md7bx6fd7.1](https://doi.org/10.17632/4md7bx6fd7.1)
- Institution: Bangladesh University of Professionals (BUP), Dhaka, Bangladesh
- Supervisor: Rumana Yasmin, Lecturer, CSE Department, BUP