Summary of the Model:
The Grammar Correction T5 Model is based on the T5 (Text-to-Text Transfer Transformer) architecture, leveraging the power of pre-trained models from Hugging Face. The model has been fine-tuned on grammar correction tasks, enabling it to take input text with grammatical errors and provide corrected output, along with a detailed list of corrections and their count.

Uses of the Model:
The primary use case for this model is to enhance the grammatical correctness of input text. It serves as a valuable tool for content creators, writers, and individuals seeking to improve the quality of written content. The model is particularly useful in applications where clear and error-free communication is essential, such as in document preparation, content editing, and educational materials.

How to Use It:
Using the Grammar Correction T5 Model is straightforward:

-Input Format:
Provide a text input that contains grammatical errors. The model is designed to handle a variety of grammatical issues, including syntax, tense, and word usage errors.

-Output:
The model generates corrected text, highlighting the corrections made. Additionally, it provides a list of words that were corrected and the overall count of corrections.

-Model Deployment:
Deploy the model easily using the Hugging Face inference API. Users can leverage the API to integrate the grammar correction capability into their applications, websites, or text processing pipelines.

By incorporating the Grammar Correction T5 Model, users can enhance the accuracy and clarity of written content, ultimately improving the overall quality of text-based communication.


## Usage

```python
from transformers import pipeline
# Load the Grammar Correction T5 Model from Hugging Face
grammar_correction_model = pipeline(task="text2text-generation", model="hassaanik/grammar-correction-model")
# Input text with grammatical errors
input_text = "They is going to spent time together."
# Get corrected output and details
result = grammar_correction_model(input_text, max_length=200, num_beams=5, no_repeat_ngram_size=2)
# Print the corrected output
print(result)

{"language": ["en"], "datasets": ["jfleg"], "metrics": ["accuracy"], "pipeline_tag": "text2text-generation"}
transformers safetensors t5 text2text-generation en dataset:jfleg autotrain_compatible endpoints_compatible has_space text-generation-inference region:us