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nlp-expert

Build NLP pipelines for preprocessing, classification, embedding generation, and retrieval.

Updated Apr 16, 2026
Search Tags:#embeddings#nlp#chatbot#semantic-search#transformers#sentiment-analysis#named-entity-recognition
One-click install
npx skills add https://github.com/duylinhdang1998/claude-template-agent --skill nlp-expert
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: nlp-expert
Source: https://github.com/duylinhdang1998/claude-template-agent/tree/main/plugins/vfm-agent-company/skills/nlp-expert
Command: npx skills add https://github.com/duylinhdang1998/claude-template-agent --skill nlp-expert

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The NLP Expert provides practical guidance and ready-to-run patterns to convert raw text into structured, semantically meaningful representations for search, classification, summarization, and conversational agents, reducing experimentation time and integration friction.

Core Features & Use Cases

  • Text Preprocessing: standardized tokenization, stopword removal, and lemmatization to prepare corpora for downstream models.
  • Classification & Sentiment: examples for using transformer-based classifiers and fine-tuning for domain sentiment analysis.
  • NER, QA & Summarization: recipes for named entity extraction, question answering pipelines, and abstractive summarization.
  • Embeddings & Semantic Search: sentence-transformer embeddings, cosine-similarity search, and RAG-based chatbot patterns with vector stores.
  • Text Generation & Chatbots: generation examples and retrieval-augmented generation flows for conversational agents.
  • Use Case: preprocess customer reviews, compute embeddings, run semantic search to surface policy-related comments, and classify sentiment for prioritization.

Quick Start

Use the nlp-expert to preprocess a corpus of customer reviews, generate sentence-transformer embeddings, and run a semantic search for "refund policy".

Frequently Asked Questions about nlp-expert

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a semantic search pipeline using sentence-transformer embeddings?▼

Generate sentence-transformer embeddings from your text corpus, store them in a vector store, and retrieve matching documents using cosine-similarity search to build a semantic search pipeline.

What is the best way to preprocess text for transformer-based classification?▼

Standardized tokenization, stopword removal, and lemmatization are the best ways to preprocess text for transformer-based classification. This ensures your corpus is clean and structured for downstream classification models.

Can I use transformers for named entity recognition and question answering pipelines?▼

Yes, transformers support named entity recognition and question answering pipelines. This Skill provides recipes for extracting named entities and building question answering pipelines from your text data.

Does this approach support GPU-accelerated inference and fine-tuning for sentiment analysis?▼

Yes, GPU-accelerated inference and optional fine-tuning are supported for sentiment analysis. You can use transformer-based classifiers and apply fine-tuning to adapt the models to your specific domain sentiment analysis needs.

How do I create a RAG chatbot system for semantic search across documents?▼

Create a RAG chatbot system by combining sentence-transformer embeddings with a vector store for semantic search, then integrating retrieval-augmented generation flows to power conversational agents across your document corpus.

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