Update README.md

README.md

@@ -8,45 +8,24 @@ metrics:
 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:
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 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:
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 Using the Grammar Correction T5 Model is straightforward:
 
 Input Format:
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 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:
 
+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:
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 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.