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Rasa NLU Model Fine Tuning
LANCER
Lancer :
Ideal for quick, focused engagements
Team: ≤ 2 resources
Duration: ≤ 3 weeks
Indicative Budget: ≤ $3,000 USD*
Use Cases: MVPs, prototypes, simple websites, bug-fix sprints
Indicative Pricing: ~$15–$25/hr per resource*
Growth :
Best suited for growing products or structured builds
Team: 2–5 resources
Duration: Up to 4 months
Indicative Budget: Up to $30,000 USD*
Use Cases: Feature-rich web apps, integrations, custom dashboards
Indicative Pricing: ~$15–$35/hr per resource*
Enterprise :
Tailored for high-scale, complex or long-term initiatives
Team: 5+ resources
Duration: More than 4 months
Indicative Budget: Starts from $30,000+ USD*
Use Cases: End-to-end platforms, SaaS products, compliance-heavy systems
Indicative Pricing: ~$25–$50/hr depending on roles*
(Development)
Project Description
A renowned organization in the chatbot development industry required assistance with improving a Rasa-based model, facing challenges with insufficient training data and cold-start issues. The client needed to optimize a pre-built model to provide better results, particularly for single utterance queries. Oodles offered its expertise in machine learning, NLP, and model tuning to resolve these challenges.
Scope Of Work
The project focused on addressing the cold-start problem by enhancing the chatbot's ability to process single utterance inputs with minimal training data. The client needed a solution that would fine-tune the existing Rasa model to achieve better NLP performance, improve accuracy, and handle a broader range of user queries. Oodles provided services in model optimization, NLP enhancement, and machine learning algorithm refinement to meet these requirements.
Our Solution
To meet the client’s needs, Oodles implemented a range of advanced strategies aimed at improving the Rasa chatbot's performance in environments with limited data. The solution involved:
- Cold-Start Problem Resolution: Used synthetic data generation and reinforcement learning techniques to train the model effectively with minimal data, tackling the cold-start challenge head-on.
- Model Optimization: Tuned the Rasa model, fine-tuning NLP components to enhance the accuracy and efficiency of the chatbot, enabling it to understand and respond more accurately to user inputs.
- NLP Enhancements: Improved the NLU pipeline, ensuring better intent classification and entity recognition for single-utterance queries, allowing the chatbot to process a wide range of user requests.
- Comprehensive Testing: Conducted rigorous testing and validation with real-world scenarios, ensuring that the chatbot could handle varying user queries with precision.
