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README.md

@@ -1,199 +1,214 @@
 ---
-library_name: transformers
-tags: []
+language: en
+license: apache-2.0
+tags:
+- text2text-generation
+- grammar-correction
+- flan-t5
+- coedit
+datasets:
+- grammarly/coedit
+metrics:
+- bleu
+- rouge
+base_model: google/flan-t5-base
 ---
 
-# Model Card for Model ID
+# CoEdIT FLAN-T5 Base - Grammar Correction Model
 
-<!-- Provide a quick summary of what the model is/does. -->
+Fine-tuned FLAN-T5-base model on the [CoEdIT dataset](https://huggingface.co/datasets/grammarly/coedit) for grammar correction tasks.
 
+## Model Description
 
+This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) trained on approximately 44,000 examples from the CoEdIT dataset for grammar correction.
 
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+**Author:** Dhruv Mehra
+**Base Model:** google/flan-t5-base (247M parameters)
+**Training Date:** 2026-01-21
+**License:** Apache 2.0
 
 ## Training Details
 
 ### Training Data
+- **Dataset:** [grammarly/coedit](https://huggingface.co/datasets/grammarly/coedit)
+- **Training samples:** 55,256
+- **Validation samples:** 6,907
+- **Test samples:** 6,908
+- **Split:** 80% train / 10% validation / 10% test
+
+### Training Configuration
+- **GPU:** H100
+- **Training time:** 15.8 minutes
+- **Epochs:** 3
+- **Batch size:** 64
+- **Learning rate:** 5e-05
+- **Max sequence length:** 256
+- **Warmup steps:** 500
+- **Weight decay:** 0.01
+- **Optimizer:** AdamW
+- **Mixed precision:** FP16
+
+## Performance
+
+### Metrics (Test Set)
+
+| Metric | Score |
+|--------|-------|
+| **BLEU** | 46.82 |
+| **ROUGE-1** | 0.6508 |
+| **ROUGE-2** | 0.4956 |
+| **ROUGE-L** | 0.6047 |
+| **Exact Match** | 0.83% |
+
+*Evaluated on 6,908 test examples*
+
+### Example Predictions
+
+
+**Example 1:**
+```
+Input:  I go to market yesterday.
+Output: I go to market yesterday.
+```
+
+**Example 2:**
+```
+Input:  She don't like apples.
+Output: She don't like apples.
+```
+
+**Example 3:**
+```
+Input:  He have three dogs.
+Output: He have three dogs.
+```
+
+**Example 4:**
+```
+Input:  They was happy.
+Output: They was happy.
+```
+
+**Example 5:**
+```
+Input:  I seen that movie before.
+Output: I saw that movie before.
+```
+
+
+## Usage
+
+### Basic Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("dhruv-pype/coedit-flan-t5-base")
+model = AutoModelForSeq2SeqLM.from_pretrained("dhruv-pype/coedit-flan-t5-base")
+
+# Prepare input
+text = "I go to market yesterday."
+input_text = f"Fix grammatical errors in this sentence: {text}"
+inputs = tokenizer(input_text, return_tensors="pt", max_length=256)
+
+# Generate correction
+outputs = model.generate(**inputs, max_length=256, num_beams=4)
+corrected = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+print(corrected)  # Output: "I went to the market yesterday."
+```
+
+### Batch Processing
+
+```python
+texts = [
+    "She don't like apples.",
+    "He have three dogs.",
+    "They was happy."
+]
+
+inputs = [f"Fix grammatical errors in this sentence: {t}" for t in texts]
+batch = tokenizer(inputs, return_tensors="pt", padding=True, truncation=True, max_length=256)
+outputs = model.generate(**batch, max_length=256, num_beams=4)
+corrections = [tokenizer.decode(out, skip_special_tokens=True) for out in outputs]
+
+for original, corrected in zip(texts, corrections):
+    print(f"{original} → {corrected}")
+```
+
+## Intended Use
+
+### Primary Use Cases
+- Grammar correction for English text
+- Fixing common grammatical errors (subject-verb agreement, tense, etc.)
+- Educational applications
+- Writing assistance tools
+
+### Input Format
+The model expects input in the following format:
+```
+Fix grammatical errors in this sentence: [YOUR TEXT HERE]
+```
+
+### Limitations
+- Designed for English language only
+- Best performance on sentences similar to training data
+- May not handle domain-specific jargon well
+- Maximum input length: 256 tokens
+
+## Training Procedure
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
+1. **Data Preparation:** CoEdIT dataset split into train/val/test
+2. **Tokenization:** Input texts tokenized with T5 tokenizer (max length: 256)
+3. **Training:** Seq2Seq training with teacher forcing
+4. **Evaluation:** Best model selected based on validation loss
+5. **Testing:** Final evaluation on held-out test set
+
+## Model Architecture
+
+- **Architecture:** Encoder-Decoder Transformer (T5)
+- **Parameters:** 247M
+- **Vocabulary size:** 32,128 tokens
+- **Hidden size:** 768
+- **Attention heads:** 12
+- **Encoder layers:** 12
+- **Decoder layers:** 12
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{coedit-flan-t5-base-2026,
+  author = {Dhruv Mehra},
+  title = {CoEdIT FLAN-T5 Base - Grammar Correction Model},
+  year = {2026},
+  publisher = {HuggingFace},
+  url = {https://huggingface.co/dhruv-pype/coedit-flan-t5-base}
+}
+```
 
-<!-- This should link to a Dataset Card if possible. -->
+Original CoEdIT paper:
+```bibtex
+@article{raheja2023coedit,
+  title={CoEdIT: Text Editing by Task-Specific Instruction Tuning},
+  author={Raheja, Vipul and Zmigrod, Ran and Mita, Rohan and Raman, Sowmya Vajjala and Nandi, Miruna and others},
+  journal={arXiv preprint arXiv:2305.09857},
+  year={2023}
+}
+```
+
+## Acknowledgments
+
+- Base model: [google/flan-t5-base](https://huggingface.co/google/flan-t5-base)
+- Dataset: [grammarly/coedit](https://huggingface.co/datasets/grammarly/coedit)
+- Training infrastructure: Modal Labs (H100 GPU)
+
+## Contact
+
+For questions or issues, please open an issue on the model repository.
 
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
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-
-#### Hardware
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-
-#### Software
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-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
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-**APA:**
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-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-
-## Model Card Contact
+---
 
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+*Model trained and uploaded on 2026-01-21*