GEC-mT5-Small-Tamil

mt5-small-indic-gec-tamil

A multilingual Grammatical Error Correction (GEC) model fine-tuned from mT5-small for Tamil. Developed as part of the BHASHA 2025 Shared Task 1: IndicGEC.

• Developed by: Manav Dhamecha, Gaurav Damor, Sunil Choudhary, Pruthwik Mishra
• License: MIT
• Base model: google/mt5-small
• Paper: Team Horizon at BHASHA Task 1
• Repository: manavdhamecha77/IndicGEC2025
• GitHub.io: Multilingual IndicGEC

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What it does

Given a grammatically incorrect Tamil sentence, the model outputs a corrected version. It handles errors across spelling, morphology, tense, word order, punctuation, missing/extra words, and semantic issues. Tamil morphological and word-order errors are a primary focus.

GLEU Score on Tamil test set: 86.03 (5th rank at BHASHA 2025)

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Quick Start

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "manavdhamecha77/GEC-mT5-Small-Tamil"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

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

sentences = [
    "நான் பள்ளிக்கு போகிறேன்",   # example Tamil sentence
]

inputs = ["correct this: " + s for s in sentences]

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

outputs = model.generate(**encoded, max_length=128, num_beams=4)
corrected = tokenizer.batch_decode(outputs, skip_special_tokens=True)

for orig, corr in zip(sentences, corrected):
    print(f"Input:     {orig}")
    print(f"Corrected: {corr}\n")

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Training Details

The model was fine-tuned using a sequence-to-sequence objective on parallel noisy–clean sentence pairs. Tamil had the smallest official dataset (~91 training pairs), which was expanded to ~10k pairs using a synthetic error injection pipeline covering 10 linguistic error categories.

Input format: "correct this: <incorrect sentence>"

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Evaluation

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Limitations

• Performance may degrade on heavy code-mixing, informal slang, or dialectal text.
• Trained primarily on formal written Tamil; may not generalize to all domains.
• Evaluation uses automatic metrics (GLEU) only; human evaluation not conducted.
• Original annotated dataset for Tamil is very small (~91 pairs); most training data is synthetic.

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Citation

@inproceedings{dhamecha2025horizon,
  title     = {Team Horizon at {BHASHA} Task 1: Multilingual {IndicGEC} with Transformer-based Grammatical Error Correction Models},
  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-1.14/}
}