Xing4.0-Intent

🦉 GitHub • 🤗 Hugging Face • 🤖 ModelScope • 🪐 Modelers • 📚 Dataset

For full project documentation, deployment instructions, SDK usage, and data details, visit the GitHub repository.

Contents

• Introduction
• Model Information
• Evaluation Results
• Quick Start
• Companion Dataset
• License and Citation

Introduction

Xing4.0-29B-Intent is a schema-guided model for intent recognition and structured information extraction, developed from Xing4.0-29B-A4B, the Xing semantic large language model from China Telecom Artificial Intelligence Technology Co., Ltd. It converts natural language into structured results for business systems, supporting intelligent assistants, request routing, and document information extraction.

For example, a request such as “Find flights from Beijing to Shanghai tomorrow” needs to be parsed into a flight-search intent and parameters such as origin, destination, and date. Xing4.0-29B-Intent performs recognition and extraction according to the supplied category and field definitions, returning results in a unified JSON format.

Key Features

• Four task types, one representation: intent recognition and slot filling, named entity recognition, relation extraction, and event extraction all use category + args to represent results.
• Schema-guided task configuration: specify tasks through candidate categories, category descriptions, and field definitions, using the same input/output protocol across business scenarios.
• Joint recognition and extraction: identify user intent and its parameters together, with JSON arrays supporting multiple categories and multiple argument instances.
• Chinese and English use cases: evaluation covers Chinese and English corpora, including intent understanding in conversations and structured information extraction from text.
• Companion SDK: provides schema definitions, prompt assembly, multi-turn conversation input, synchronous and asynchronous calls, and convenient access to structured results.

Model Information

The base model uses a mixture-of-experts (MoE) architecture. See the base model overview for technical details. This project focuses on intent recognition and information extraction; performance for specific use cases should be validated using the target data and schema.

Tasks and Outputs

The input schema defines candidate categories and their extractable fields: category specifies the category name, while args lists field names and descriptions. Model outputs use the corresponding category and field names. Each object in the output args array represents one extracted argument instance; when no arguments can be filled, args is [].

Evaluation Results

We evaluate the model on four task types: intent understanding, named entity recognition, relation extraction, and event extraction. The table below presents the currently compiled results on a 0–100 scale, rounded to whole numbers from the original scores.

Note: Bold marks the highest original score in each column, determined before rounding. Identical displayed integers do not necessarily indicate a tie in the original scores.

Evaluation Notes

• Evaluation data: the test set contains 6,000 samples drawn from 48 public datasets, covering named entity recognition, relation extraction, event extraction, and intent understanding. It evaluates the model's ability to identify categories and extract relevant information from input text according to the supplied task instructions and schema.
• Metric definitions: each task score is the macro-average of per-dataset field-value Micro-F1 scores. The overall score is the equally weighted average of the four task scores. The category score evaluates only category labels and is also aggregated with equal weights across the four task types. Scores are shown on a 0–100 scale and do not represent business