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README.md

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- grammar-correction
+- gec
+- flan-t5
+- seq2seq
+pipeline_tag: text2text-generation
+widget:
+- text: "grammar: She go to school yesterday."
+  example_title: "Verb Tense Correction"
+- text: "grammar: He don't likes playing football."
+  example_title: "Subject-Verb Agreement"
+- text: "grammar: I has a meeting tomorrow."
+  example_title: "Auxiliary Verb"
+---
+
+# AI Grammar Correction — Fine-tuned FLAN-T5
+
+## Model Description
+This is a sequence-to-sequence transformer model fine-tuned for **Grammatical Error Correction (GEC)** based on [`google/flan-t5-base`](https://huggingface.co/google/flan-t5-base).
+
+It automatically detects and corrects grammatical, syntactical, and stylistic errors in English sentences, returning fluent, natural corrections.
+
+## Task Details
+- **Task**: Grammatical Error Correction (GEC)
+- **Base Architecture**: `google/flan-t5-base`
+- **Input Prefix**: `"grammar: <input_text>"`
+- **Max Sequence Length**: 128 tokens
+
+## Dataset Specifications
+Fine-tuned on a curated dataset of **100,000 sentence pairs**:
+- **Train Set**: 80,000 pairs (80%)
+- **Validation Set**: 10,000 pairs (10%)
+- **Test Set**: 10,000 pairs (10%)
+
+## Evaluation Results (10,000 Unseen Test Examples)
+
+| Metric | Test Score |
+| :--- | :--- |
+| **BLEU** | `0.6869` |
+| **GLEU** | `0.8311` |
+| **ROUGE-1** | `0.8819` |
+| **ROUGE-2** | `0.7698` |
+| **ROUGE-L** | `0.8748` |
+| **Exact Match Accuracy** | `0.0886` |
+| **Word Accuracy** | `0.5018` |
+
+## How to Use
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+MODEL_ID = "anuvid/ai-grammar-correction-model"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_ID)
+
+text = "She go to school yesterday."
+inputs = tokenizer(f"grammar: {text}", return_tensors="pt")
+
+outputs = model.generate(**inputs, max_length=128, num_beams=4, early_stopping=True)
+corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+print("Original :", text)
+print("Corrected:", corrected_text)
+# Output: "She went to school yesterday."
+```
+
+## Example Corrections
+
+| Original Input | Model Output |
+| :--- | :--- |
+| `She go to school yesterday.` | `She went to school yesterday.` |
+| `He don't likes playing football.` | `He doesn't like playing football.` |
+| `I has a meeting tomorrow.` | `I have a meeting tomorrow.` |
+| `They was going to the market.` | `They were going to the market.` |
+| `This are a very good book.` | `This is a very good book.` |