https://github.com/saeidsaadatigero/summarizer

this code provides a straightforward way to summarize large blocks of text using a pre-trained machine learning model. By simply replacing the placeholder text with your own content, you can quickly obtain a concise summary of that content. The script is useful for applications where quick comprehension of lengthy documents is needed.
https://github.com/saeidsaadatigero/summarizer

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this code provides a straightforward way to summarize large blocks of text using a pre-trained machine learning model. By simply replacing the placeholder text with your own content, you can quickly obtain a concise summary of that content. The script is useful for applications where quick comprehension of lengthy documents is needed.

• Host: GitHub
• URL: https://github.com/saeidsaadatigero/summarizer
• Owner: saeidsaadatigero
• Created: 2024-07-30T13:44:19.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-07-30T13:45:39.000Z (5 months ago)
• Last Synced: 2024-07-30T17:25:50.333Z (5 months ago)
• Language: Jupyter Notebook
• Size: 12.7 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
:star::star::star::star::star:This code is a Python script that utilizes the Hugging Face Transformers library to summarize a given input text. Here’s a detailed breakdown of its components and functionality:

### Breakdown of the Code

1. **Importing the Pipeline**:

   - `from transformers import pipeline`: This line imports the `pipeline` function from the Transformers library, which simplifies the use of pre-trained models for various tasks, including summarization.

2. **Defining the Summarization Function**:

   - **`def summarize_text(text, max_length=130, min_length=30):`**: This function takes in a string `text` and optional parameters `max_length` and `min_length` to control the length of the summary.

   - **Loading the Summarization Model**:

     - `summarizer = pipeline("summarization", model="facebook/bart-large-cnn")`: This line initializes a summarization pipeline using the BART model, specifically the `facebook/bart-large-cnn`, which is designed for summarizing text.

   - **Generating the Summary**:

     - `summary = summarizer(text, max_length=max_length, min_length=min_length, do_sample=False)`: This line calls the summarizer on the input text and generates a summary based on the specified length constraints.

   - **Returning the Summary**:

     - `return summary[0]['summary_text']`: This returns the summarized text from the output of the summarization pipeline.

3. **Main Execution Block**:

   - `if __name__ == "__main__":` checks if the script is being run directly (not imported as a module).

   - **Input Text**:

     - `input_text = """..."""`: Here, you can replace the placeholder text with any long text that you want to summarize.

   - **Calling the Summarization Function**:

     - `summary = summarize_text(input_text)`: This line calls the `summarize_text` function with the provided input text and stores the result in the `summary` variable.

   - **Printing the Summary**:

     - `print("Summary:")` and `print(summary)`: These lines print the generated summary to the console.

### Summary

In summary, this code provides a straightforward way to summarize large blocks of text using a pre-trained machine learning model. By simply replacing the placeholder text with your own content, you can quickly obtain a concise summary of that content. The script is useful for applications where quick comprehension of lengthy documents is needed.