Instructions to use manavdhamecha77/GEC-mT5-Small-Hindi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use manavdhamecha77/GEC-mT5-Small-Hindi with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="manavdhamecha77/GEC-mT5-Small-Hindi")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("manavdhamecha77/GEC-mT5-Small-Hindi")
model = AutoModelForMultimodalLM.from_pretrained("manavdhamecha77/GEC-mT5-Small-Hindi")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use manavdhamecha77/GEC-mT5-Small-Hindi with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "manavdhamecha77/GEC-mT5-Small-Hindi"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "manavdhamecha77/GEC-mT5-Small-Hindi",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/manavdhamecha77/GEC-mT5-Small-Hindi

SGLang

How to use manavdhamecha77/GEC-mT5-Small-Hindi with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "manavdhamecha77/GEC-mT5-Small-Hindi" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "manavdhamecha77/GEC-mT5-Small-Hindi",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "manavdhamecha77/GEC-mT5-Small-Hindi" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "manavdhamecha77/GEC-mT5-Small-Hindi",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use manavdhamecha77/GEC-mT5-Small-Hindi with Docker Model Runner:
```
docker model run hf.co/manavdhamecha77/GEC-mT5-Small-Hindi
```

GEC-mT5-Small-Hindi / README.md

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metadata

license: mit
language:
  - hi
metrics:
  - google_bleu
base_model:
  - google/mt5-small
pipeline_tag: text2text-generation
library_name: transformers
tags:
  - grammatical-error-correction
  - indic-nlp
  - hindi
  - gec

mt5-small-indic-gec-hindi

A multilingual Grammatical Error Correction (GEC) model fine-tuned from mT5-small for Hindi. 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

What it does

Given a grammatically incorrect Hindi sentence, the model outputs a corrected version. It handles errors across spelling, grammar (tense, person, number, gender, case), punctuation, missing/extra words, and semantic issues.

GLEU Score on Hindi test set: 80.44

Quick Start

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "manavdhamecha77/GEC-mT5-Small-Hindi"  # update with your HF repo name

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 = [
    "मैं स्कूल जाती है।",
    "राम ने ने खाना खाया।",
    "वे किताबें पढ़ता है।",
]

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")

Training Details

The model was fine-tuned using a sequence-to-sequence objective on parallel noisy–clean sentence pairs. Training data was expanded from ~599 annotated pairs to ~10k pairs using a synthetic error injection pipeline that introduces realistic errors across 10 linguistic categories.

Parameter	Value
Optimizer	AdamW
Learning Rate	5e-5
Batch Size	16–32
Epochs	10–15
Max Sequence Length	128
Early Stopping	Based on GLEU (dev set)

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

Evaluation

Language	Model	GLEU
Hindi	mT5-small	80.44

Limitations

Performance may degrade on heavy code-mixing, informal slang, or dialectal text.
Trained primarily on formal written Hindi; may not generalize to all domains.
Evaluation uses automatic metrics (GLEU) only; human evaluation not conducted.

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/}
}