Building and Training NLP Models: Episode 2
Introduction: Following the foundational work in Episode 1, we now delve into creating query sets, annotating data, and training our NLP model. This episode focuses on utilizing a well-classified dataset to develop an AI capable of analyzing business email requests.
Objective: Train AI to understand and respond to RFQs within emails by building and enriching subsets of data.
Building Subsets: Creating specific queries helps in extracting pertinent data from diverse business domains, ensuring comprehensive training material.
Data Annotation and Enrichment: Using tools like Prodigy, we meticulously annotate the dataset. Each email is categorized, with requests identified and key entities extracted. This detailed annotation is essential for accurate AI training. By generating variations of examples, we ensure the dataset covers all possible scenarios the AI might encounter.
Training the Model: Standard NLP models are employed for the initial training phase. We utilize confusion matrices to evaluate model performance, identifying areas needing refinement. Adjustments are made to fine-tune entity recognition and intention understanding, enhancing the model’s accuracy.
Model Adjustment: Post-initial training, we continuously refine the model. This involves re-evaluating annotated data, adjusting training parameters, and incorporating new data to cover edge cases and improve overall performance.
Conclusion: Episode 2 details the critical steps of data preparation, annotation, and model training. These steps lay the groundwork for developing a robust NLP model capable of accurately analyzing business requests within emails. Stay tuned for Episode 3, where we will test the model in real-world conditions.
For a more detailed guide on advanced NLP preparation, refer to Terranoha’s Episode 2.
