Upload README.md

README.md

@@ -0,0 +1,121 @@
+---
+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](https://huggingface.co/google/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](https://huggingface.co/google/mt5-small)
+- **Paper:** [Team Horizon at BHASHA Task 1](https://aclanthology.org/2025.bhasha-1.14/)
+- **Repository:** [manavdhamecha77/IndicGEC2025](https://github.com/manavdhamecha77/IndicGEC2025)
+
+---
+
+## 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
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+
+model_name = "manavdhamecha77/mt5-small-indic-gec-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
+
+```bibtex
+@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/}
+}
+```