Update README.md

README.md

@@ -13,36 +13,51 @@ library_name: diffusers
 tags:
 - legal
 ---
- Model Overview Section:
+Model Overview Section:
+ 
 Add a brief paragraph summarizing the model’s purpose, what makes it unique, and its intended users.
+
 For example:
 vbnet
 Copy code
 This model, developed by Rithu Paran, is designed to provide high-quality text summarization, making it ideal for applications in content curation, news summarization, and document analysis. Leveraging the Meta-Llama architecture, it delivers accurate, concise summaries while maintaining key information, and is optimized for general-purpose use.
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 2. Model Description:
 Under Model Type, clarify the model's focus on general text summarization or a specific summarization task (e.g., long-form content, news).
 Update Language(s) with more detail on the model's primary language capabilities.
-3. Model Use Cases:
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+4. Model Use Cases:
 Expand Direct Use and Out-of-Scope Use with specific examples to guide users.
 Direct Use: News article summarization, summarizing reports for quick insights, content summarization for educational purposes.
 Out-of-Scope Use: Avoid using it for legal or medical content without specialized training.
-4. Bias, Risks, and Limitations:
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+6. Bias, Risks, and Limitations:
 Include any known biases related to the datasets used. For example, “The model may reflect certain cultural or societal biases present in the training data.”
 Add a note on limitations in terms of accuracy for complex technical summaries or if the model occasionally generates nonsensical summaries.
-5. How to Get Started with the Model:
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+8. How to Get Started with the Model:
 Add more usage tips, such as how to adjust parameters for different summary lengths.
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 Example:
 python
 Copy code
 summary = summarizer(text, max_length=150, min_length=50, do_sample=False)
-6. Training Details:
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+10. Training Details:
 In Training Hyperparameters, provide a rationale for the chosen batch size and learning rate.
 If you have insights into why AdamW was chosen as the optimizer, it would be helpful to include that too.
-7. Environmental Impact:
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+12. Environmental Impact:
 Add a short sentence on the steps taken to minimize the environmental impact, if applicable.
-8. Evaluation:
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+14. Evaluation:
 If possible, include the exact ROUGE and BLEU scores to show the model’s summarization performance.
-9. Additional Information:
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+15. Additional Information:
 You could add a Future Work or Planned Improvements section if you plan to enhance the model further.
 In the Contact section, you might mention if you are open to feedback, bug reports, or contributions.
 Here’s a short sample revision for the Model Details section: