The ahalt/event-attribute-extractor is a 0.8 billion parameter Qwen3-based model developed by ahalt, specifically fine-tuned for extracting political events and their attributes from text. With a context length of 40960 tokens, this model excels at structured information extraction, outputting event details in a JSON format. It is optimized for identifying event types, anchor quotes, actors, recipients, dates, and locations from textual data.
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Overview
The ahalt/event-attribute-extractor is a specialized 0.8 billion parameter model built on the Qwen3 architecture, designed for precise extraction of political event attributes from text. It leverages a substantial context length of 40960 tokens, enabling it to process lengthy documents for detailed information retrieval. The model's primary function is to identify and structure event-related data into a standardized JSON format, making it highly suitable for automated data processing and analysis in political science or news monitoring applications.
Key Capabilities
- Structured Event Extraction: Identifies and extracts specific political events and their associated attributes (event type, anchor quote, actor, recipient, date, location).
- JSON Output: Formats extracted information into a clean, valid JSON structure, including an empty array if no events are found.
- High Context Length: Utilizes a 40960-token context window, allowing for comprehensive analysis of longer texts.
- Qwen3 Base: Benefits from the underlying Qwen3 architecture for robust language understanding.
Good For
- Automated News Analysis: Extracting political events from news articles or reports.
- Conflict Monitoring: Identifying actors, recipients, and locations of political incidents.
- Data Annotation: Generating structured datasets from unstructured text for event-based research.
- Information Retrieval: Quickly pinpointing key details about political events within large text corpora.