Building AI powered Prompt-Driven Apps with NER (Named Entity Recognition)

Imagine an app that understands your natural language instructions and performs actions accordingly. "Show me restaurants near my current location" brings up dining options, while "Schedule a meeting with John next Tuesday" adds an event to your calendar. This is the magic of prompt-driven apps, and building one with Named Entity Recognition (NER) is surprisingly achievable.

In this article, we'll delve into creating an app that interprets user prompts and takes action based on the extracted information. We'll leverage the power of transformers, specifically the roberta-base model, for NER and explore how to integrate it with a user-friendly frontend.

The Power of Transformers and Named Entity Recognition

Transformers are a class of deep learning models revolutionizing Natural Language Processing (NLP). They excel at understanding complex relationships within text, making them ideal for NER. NER identifies and classifies named entities like people, places, organizations, dates, and quantities within a sentence.

Here's how NER empowers prompt-driven apps:

• Extracting Key Information: When a user enters a prompt like "Find flights to Paris in December," NER isolates "Paris" (location) and "December" (date). This extracted information becomes the foundation for your app's action.
• Structured Data for Action: By identifying entities, you convert the user's natural language into structured data the app understands. This structured data can be used to trigger actions like searching for flights or adding appointments.

Leveraging the roberta-base Model

The roberta-base model is a pre-trained transformer specifically designed for various NLP tasks, including NER. Its pre-trained nature allows you to tap into its capabilities without extensive training from scratch. Here's how to integrate roberta-base for NER:

• Fine-tuning the Model: While roberta-base has general NER capabilities, you can fine-tune it on a specific dataset relevant to your app's domain. This enhances its accuracy in recognizing entities relevant to your app's functionality.
• Libraries for Easy Implementation: Popular libraries like Simpletransformers or Huggingface Serving offer pre-trained roberta-base models with NER functionalities. These libraries simplify integration, allowing you to focus on building your app's core logic.

Building the User Interface: Bridging the Gap

The user interface (UI) is the bridge between the user's prompt and the app's actions. Here's how to design an effective UI:

• Natural Language Input: Provide a text field where users can enter their prompts. Consider features like auto-complete or voice recognition for a seamless experience.
• Actionable Feedback: Once the prompt is processed, visually represent the extracted entities and the intended action. This transparency builds user trust and avoids misunderstandings.
• Error Handling and Refinement: Incorporate error handling mechanisms to address ambiguous prompts or missing information. Allow users to refine their prompts for better results.

Putting it All Together: A Template for Success

Here's a high-level template to guide you through building your app:

1. Define the App's Functionality: Determine the types of prompts your app will handle and the corresponding actions it will perform.
2. Data Acquisition and Preprocessing: Gather relevant training data for your chosen domain and pre-process it for the NER model.
3. Fine-tune the roberta-base Model: Fine-tune the model with your prepared data to enhance NER accuracy for your specific use case.
4. Develop the Backend: Integrate the fine-tuned model into your backend to process user prompts, extract entities, and trigger actions.
5. Design the Frontend: Build a user-friendly interface that facilitates natural language input, displays extracted information, and presents actionable results.
6. Testing and Refinement: Rigorously test your app with various prompts and user scenarios. Refine the model and UI based on test results.

Let's create AI powered Todo app

• Define the App's Functionality: Here we want to create Todo app so we can create basic Todo functionality. I am choosing vue3 to create frontend. There are three actions i have kept in mind: Add, Remove and mark Todo as complete.
• Data Acquisition and Preprocessing: To train our roberta-base model we need some data. i have added sample data where i am converting entire sentence to some relevant token. There are different types of entity that entire sentence will be divided into O, B-RT, I-RT, B-CNT, I-CNT, B-CT, I-CT, B-AT, I-AT Where 'B-' represents beginning of entity, 'I-' represents next entity part and 'O' represents non categorized word.

• Fine-tune the roberta-base Model: Using simpletransformers for fine-tuning is an excellent choice. The included model accuracy is valuable for gauging performance.. Experiment with different hyperparameters (learning rate, batch size) to potentially improve accuracy further.

• Develop the Backend: We now have model so we can create backend with route that can accept input prompt and return classified entities. I am using flask for backend. Implement error handling mechanisms for scenarios where NER fails to identify entities or encounters ambiguous prompts.
• Design the Frontend: We can design frontend with any UI framework of your choice and i have chosen vue3 for this. Design an intuitive interface for users to enter prompts, view existing Todos, and interact with the app's functionalities. Utilize an HTTP client in Vue.js to communicate with the Flask backend, sending prompts and receiving classified entities.
• Testing and Refinement: Based on testing results, refine the model by adjusting hyperparameters, gathering more training data, or potentially using an active learning approach where the model queries the user for clarification on ambiguous prompts.

Now you can play with you app and feel power of AI on your palm. You can checkout training process, frontend and backend on github repository here.

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