Text Classification
Assign a category or label to a piece of text using a consistent, backend-powered inference workflow.
NLP Showcase
One interface for text classification, NER, summarization, translation, and question answering.
This page is designed as a practical NLP workspace. Instead of a static description, it presents a task-oriented surface where multiple language capabilities can be explored through a consistent interaction model.
5Core NLP tasks in one workspace
1 UIConsistent surface across workflows
APIInference connected through backend services
What this page is for
A unified interface for interacting with multiple NLP capabilities without switching between disconnected demos.
ClassificationNERSummarizationTranslationQA
Design goal
The showcase should feel like a real product workspace, not a collection of raw inputs and buttons.
System thinking
Tasks share a common frontend pattern while inference remains powered by backend model services and server-side configuration.
Task selection
NLP capabilities available in the showcase
Each task is presented as part of the same product surface, making it easier to compare workflows and maintain a consistent user experience across the NLP stack.
Named Entity Recognition
Identify entities such as people, organizations, locations, and structured mentions inside the input text.
Summarization
Compress longer passages into concise summaries while preserving the key ideas and topical structure.
Translation
Translate text across languages through the same workspace pattern used by the other NLP tasks.
Question Answering
Provide answers to user questions over supplied text input using a shared, task-oriented interface.
Task control
Select a task and open the workspace
The page uses one task picker to switch between NLP workflows. This keeps the interaction model simple while still exposing multiple language capabilities.
Active task
Text Classification is selected by default, matching the screenshots you shared.
Interaction pattern
Choose a task, enter text, optionally provide labels or task-specific input, then submit for inference.
Why this matters
It turns a plain demo into a reusable NLP product surface with consistent visual hierarchy and workflow structure.
Active task workspace
Ready for inferenceClick one of the tasks on the left to open the workspace and run inference (classification, NER, summarization, translation, or question answering).
Task explanation
Text Classification
Provide a text and optional labels. The model assigns a category or label to the text through the shared inference pipeline exposed by the backend.
Single interfaceOptional labelsBackend inference
Model info
How inference is wired
Inference is powered by a language model through the backend. API key management and model configuration remain server-side, keeping sensitive integration details outside the browser.
Server-side configShared backend routeReusable NLP service
Design principles
What makes this showcase stronger than a raw demo
The objective is not just to expose NLP tasks, but to organize them in a way that feels coherent, scalable, and ready to evolve into a larger AI product surface.
Unified task modelMultiple NLP capabilities are organized through one visual interaction pattern instead of isolated mini-pages.
Clear workspace hierarchyTask selector, input area, explanation, and model notes are visually separated for readability and flow.
Backend-aligned UIThe interface is designed with the assumption that inference, secrets, and model switching remain controlled server-side.
Task switchingConsistent UXTyped interfacesBackend inferenceReusable NLP servicesScalable showcase design
Continue through the portfolio
Move from NLP workflows into ML systems, RAG, and broader AI products.
This showcase demonstrates the NLP side of the portfolio through a task-oriented workspace. Continue to ML systems, open the RAG assistant, or browse projects to see the rest of the AI system landscape.