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--- |
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datasets: |
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- jfleg |
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language: |
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- en |
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metrics: |
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- accuracy |
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pipeline_tag: text2text-generation |
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--- |
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Summary of the Model: |
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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. |
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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. |
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How to Use It: |
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Using the Grammar Correction T5 Model is straightforward: |
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-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. |
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-Output: |
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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. |
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-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. |
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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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## Usage |
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```python |
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from transformers import pipeline |
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# Load the Grammar Correction T5 Model from Hugging Face |
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grammar_correction_model = pipeline(task="text2text-generation", model="hassaanik/grammar-correction-model") |
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# Input text with grammatical errors |
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input_text = "They is going to spent time together." |
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# Get corrected output and details |
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result = grammar_correction_model(input_text, max_length=200, num_beams=5, no_repeat_ngram_size=2) |
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# Print the corrected output |
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print(result) |
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