---
license: mit
language:
- hi
metrics:
- exact_match
base_model:
- google/muril-base-cased
pipeline_tag: token-classification
library_name: transformers
tags:
- word-grouping
- indic-nlp
- hindi
- token-classification
- local-word-groups
- bio-tagging
---

# WG-GoogleMuril

A token classification model fine-tuned from [MuRIL](https://huggingface.co/google/muril-base-cased) for **Indic Word Grouping** (Local Word Group identification). Developed as part of the BHASHA 2025 Shared Task 2: IndicWG.

**🏆 Ranked 1st among all participating teams at BHASHA 2025.**

- **Developed by:** Manav Dhamecha, Gaurav Damor, Sunil Choudhary, Pruthwik Mishra
- **License:** MIT
- **Base model:** [google/muril-base-cased](https://huggingface.co/google/muril-base-cased)
- **Paper:** [Team Horizon at BHASHA Task 2](https://aclanthology.org/2025.bhasha-1.18/)
- **Repository:** [manavdhamecha77/IndicWG2025](https://github.com/manavdhamecha77/IndicWG2025)
- **GitHub.io:** [Indic Word Grouping](https://manavdhamecha77.github.io/wg/)


---

## What it does

Given an input sentence in Hindi, the model identifies **Local Word Groups (LWGs)** — semantically cohesive sequences of words that convey a single complete meaning (e.g., noun compounds, postpositional groups, verb groups with auxiliaries, light verb constructions).

The task is modeled as **BIO token classification** with three labels: `B` (beginning of a group), `I` (inside a group), `O` (outside / delimiter). The output is reconstructed into grouped sentences using `__` as the group boundary separator.

**Exact Match Accuracy: 58.18% on the official test set (post-challenge refined model)**

---

## Quick Start

```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch

model_name = "manavdhamecha77/WG-GoogleMuril"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

sentence = "राम ने बाजार से सब्जियां खरीदीं।"

inputs = tokenizer(sentence, return_tensors="pt", truncation=True, max_length=128).to(device)

with torch.no_grad():
    outputs = model(**inputs)

predictions = torch.argmax(outputs.logits, dim=-1)[0].tolist()
tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])

# Label map: {0: "B", 1: "I", 2: "O"}
id2label = {0: "B", 1: "I", 2: "O"}

for token, pred in zip(tokens, predictions):
    if token not in ["[CLS]", "[SEP]", "[PAD]"]:
        print(f"{token:20s} {id2label[pred]}")
```

---

## Training Details

The model is fine-tuned using `AutoModelForTokenClassification` with a class-weighted cross-entropy loss to address the dominant `O`-label imbalance (inverse-frequency weights upweight `B` and `I` labels). Labels are aligned to subword tokens using the tokenizer's `word_ids()` helper; only the first subword of each word is labeled, with subsequent subwords set to `-100`.

| Parameter | Value |
|---|---|
| Optimizer | AdamW |
| Learning Rate | 3×10⁻⁵ |
| Batch Size | 8 (train/eval) |
| Epochs | 20 |
| Weight Decay | 0.01 |
| Label Map | B:0, I:1, O:2 |
| Hardware | H100 GPU (94GB) |

**Training data:** Official BHASHA/IndicWG Hindi dataset (550 train / 100 dev / 226 test sentences), supplemented with 5K augmented sentences from a rule-based LWG finder applied to IndicCorp.

---

## Evaluation

| Model | Dev EM (%) | Test EM (%) |
|---|---|---|
| **MuRIL (this model)** | **46.58** | **58.18** |
| XLM-Roberta | 39.00 | 53.36 |
| IndicBERT v2 | 35.40 | 52.73 |

Evaluation metric: **Exact Match Accuracy** — a prediction is correct only if the entire reconstructed grouped sentence matches the gold output exactly.

---

## Limitations

- Trained and evaluated on Hindi only; generalization to other Indic languages is not guaranteed.
- Performance degrades on longer sentences (>40 words: ~20% EM).
- Sensitive to subword tokenization boundaries, which can cause off-by-one grouping errors.
- Gold annotations contain inconsistencies in multiword expressions and light-verb constructions, which caps achievable exact-match accuracy.

---

## Citation

```bibtex
@inproceedings{dhamecha2025horizonwg,
  title     = {Team Horizon at {BHASHA} Task 2: Fine-tuning Multilingual Transformers for Indic Word Grouping},
  author    = {Dhamecha, Manav and Damor, Gaurav and Choudhary, Sunil and Mishra, Pruthwik},
  booktitle = {Proceedings of the 1st Workshop on Benchmarks, Harmonization, Annotation, and Standardization for Human-Centric AI in Indian Languages (BHASHA 2025)},
  year      = {2025},
  url       = {https://aclanthology.org/2025.bhasha-1.18/}
}
```