Update README.md

README.md

@@ -13,66 +13,56 @@ library_name: diffusers
 tags:
 - legal
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+ 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.
+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:
+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:
+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:
+Add more usage tips, such as how to adjust parameters for different summary lengths.
+Example:
+python
+Copy code
+summary = summarizer(text, max_length=150, min_length=50, do_sample=False)
+6. 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:
+Add a short sentence on the steps taken to minimize the environmental impact, if applicable.
+8. Evaluation:
+If possible, include the exact ROUGE and BLEU scores to show the model’s summarization performance.
+9. 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:
 
-
-Model Card for Rithu Paran's Summarization Model
 Model Details
 Model Description
-Purpose: This model is designed for text summarization, specifically built to condense long-form content into concise, meaningful summaries.
-Developed by: Rithu Paran
-Model type: Transformer-based Language Model for Summarization
+This model by Rithu Paran focuses on text summarization, reducing lengthy content into concise summaries. Built on the Meta-Llama architecture, it has been finetuned to effectively capture key points from general text sources.
+
+Purpose: General-purpose text summarization
+Developer: Rithu Paran
+Architecture: Transformer-based Llama-3
+Language: Primarily English
+Model Versions
+
 Base Model: Meta-Llama/Llama-3.2-11B-Vision-Instruct
-Finetuned Model Version: Meta-Llama/Llama-3.1-8B-Instruct
-Language(s): Primarily English, with limited support for other languages.
-License: MIT License
-Model Sources
-Repository: Available on Hugging Face Hub under Rithu Paran
-Datasets Used: fka/awesome-chatgpt-prompts, gopipasala/fka-awesome-chatgpt-prompts
-Uses
-Direct Use
-This model can be directly employed for summarizing various types of content, such as news articles, reports, and other informational documents.
-Out-of-Scope Use
-It is not recommended for highly technical or specialized documents without additional fine-tuning or adaptation.
-Bias, Risks, and Limitations
-While this model was designed to be general-purpose, there may be inherent biases due to the training data. Users should be cautious when using the model for sensitive content or in applications where accuracy is crucial.
+Current Finetuned Model: Meta-Llama/Llama-3.1-8B-Instruct
+For the full model card, keep these ideas in mind and feel free to customize it further to fit your style! Let me know if you’d like more specific revisions.
+
+
 
-How to Get Started with the Model
-Here's a quick example of how to start using the model for summarization:
 
-python
-Copy code
-from transformers import pipeline
 
-summarizer = pipeline("summarization", model="rithu-paran/your-summarization-model")
-text = "Insert long-form text here."
-summary = summarizer(text, max_length=100, min_length=30)
-print(summary)
-Training Details
-Training Data
-Datasets: fka/awesome-chatgpt-prompts, gopipasala/fka-awesome-chatgpt-prompts
-Preprocessing: Data was tokenized and normalized for better model performance.
-Training Procedure
-Hardware: Trained on GPUs with Hugging Face API resources.
-Precision: Mixed-precision (fp16) was utilized to enhance training efficiency.
-Training Hyperparameters
-Batch Size: 16
-Learning Rate: 5e-5
-Epochs: 3
-Optimizer: AdamW
-Evaluation
-Metrics
-Metrics Used: ROUGE Score, BLEU Score
-Evaluation Datasets: Evaluated on a subset of fka/awesome-chatgpt-prompts for summarization performance.
-Technical Specifications
-Model Architecture
-Based on Llama-3 architecture, optimized for summarization through attention-based mechanisms.
-Compute Infrastructure
-Hardware: Nvidia A100 GPUs were used for training.
-Software: Hugging Face’s transformers library along with the diffusers library.
-Environmental Impact
-Hardware Type: Nvidia A100 GPUs
-Training Duration: ~10 hours
-Estimated Carbon Emission: Approximate emissions calculated using Machine Learning Impact calculator.
-Contact
-For any questions or issues, please reach out to Rithu Paran via the Hugging Face Forum.