Post
2277
Hello Community,
I am seeking guidance on solving a complex problem related to tender categorization. Here's a detailed overview of my requirements and challenges:
Problem Statement:
I need to categorize tenders based on their descriptions on a daily basis.
I process over 35,000 tenders daily, each potentially belonging to a vast range of categories. Predicting the exact category for each tender is challenging due to the diversity and complexity of categories.
Requirements:
Model Selection: I require a model that can accurately predict the category of each tender based on its description. I plan to create a dataset from 1 million records in the required format to train the model. Post-training, I also need to optimize the model for performance.
Training and Optimization: Guidance on the best practices for training and optimizing the model is essential. I am considering Hugging Face’s tools but am unsure about how to effectively use them or any alternatives.
Local Deployment: If I opt for a local deployment, I need to know what kind of infrastructure or applications are required to run the model on my own servers efficiently.
Performance: The model must provide responses quickly, given the high volume of tenders.
Questions for the Community:
Model Recommendations:
Which model or method would be most suitable for categorizing tenders with diverse categories?
What are the best practices for training and optimizing such a model?
Infrastructure and Deployment:
What infrastructure or software is needed to deploy a model locally for this purpose?
How can I ensure the model performs efficiently on my server?
Performance Optimization:
What strategies or techniques can I use to achieve quick response times from the model?
I appreciate any advice, suggestions, or resources you can provide to help address these challenges.
Thank you!
Best regards,
Sachin Vaishnav
I am seeking guidance on solving a complex problem related to tender categorization. Here's a detailed overview of my requirements and challenges:
Problem Statement:
I need to categorize tenders based on their descriptions on a daily basis.
I process over 35,000 tenders daily, each potentially belonging to a vast range of categories. Predicting the exact category for each tender is challenging due to the diversity and complexity of categories.
Requirements:
Model Selection: I require a model that can accurately predict the category of each tender based on its description. I plan to create a dataset from 1 million records in the required format to train the model. Post-training, I also need to optimize the model for performance.
Training and Optimization: Guidance on the best practices for training and optimizing the model is essential. I am considering Hugging Face’s tools but am unsure about how to effectively use them or any alternatives.
Local Deployment: If I opt for a local deployment, I need to know what kind of infrastructure or applications are required to run the model on my own servers efficiently.
Performance: The model must provide responses quickly, given the high volume of tenders.
Questions for the Community:
Model Recommendations:
Which model or method would be most suitable for categorizing tenders with diverse categories?
What are the best practices for training and optimizing such a model?
Infrastructure and Deployment:
What infrastructure or software is needed to deploy a model locally for this purpose?
How can I ensure the model performs efficiently on my server?
Performance Optimization:
What strategies or techniques can I use to achieve quick response times from the model?
I appreciate any advice, suggestions, or resources you can provide to help address these challenges.
Thank you!
Best regards,
Sachin Vaishnav