Create README.md

README.md

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+---
+datasets:
+- jfleg
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: text2text-generation
+---
+1) 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.
+
+2) 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.
+
+3) 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.
+
+Example Code:
+ from transformers import pipeline
+
+# Load the Grammar Correction T5 Model from Hugging Face
+grammar_correction_model = pipeline(task="text2text-generation", model="your-model-name")
+
+# Input text with grammatical errors
+input_text = "They is going to spent time together."
+
+# Get corrected output and details
+corrections = grammar_correction_model(input_text, max_length=200, num_beams=5, no_repeat_ngram_size=2)
+
+# Print corrected text and details
+print("Corrected Text:", corrections[0]["generated_text"])
+print("Corrections:", corrections[0]["generated_text"].split())
+print("Count of Corrections:", len(corrections[0]["generated_text"].split()) - len(input_text.split()))
+
+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.
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