tegana/qwen2.5-arabic-finance-news-parser
The tegana/qwen2.5-arabic-finance-news-parser is a 1.5 billion parameter causal language model, fine-tuned from Qwen/Qwen2.5-1.5B-Instruct, with a 32768 token context length. It is specifically optimized for structured information extraction from Arabic financial news articles. This model excels at parsing data such as company names, event types, sentiment, and financial figures from text based on a provided JSON output schema. Its primary use case is automating the analysis of Egyptian stock-market news.
Loading preview...
Overview
This model, tegana/qwen2.5-arabic-finance-news-parser, is a specialized fine-tuned version of the Qwen/Qwen2.5-1.5B-Instruct base model. It leverages LoRA fine-tuning (rank 64, all targets) using LLaMA-Factory to achieve its specific capabilities. The model was trained on a dataset of approximately 2,792 Egyptian stock-market news articles, with a maximum sequence length of 3,500 tokens, over 3 epochs.
Key Capabilities
- Structured Information Extraction: Given an Arabic financial news article and a JSON output schema, the model extracts predefined structured data.
- Financial Data Parsing: Capable of identifying and extracting entities like company names, event types (e.g.,
earnings,acquisition,dividends), sentiment (positive, negative, neutral), impact levels, and short summaries. - Arabic Language Specialization: Specifically trained on Egyptian stock-market news, making it highly proficient in this domain and language dialect.
Use Cases
- Automated Financial News Analysis: Ideal for systems requiring automated parsing of Arabic financial news to extract key data points.
- Market Intelligence: Can be used to quickly process large volumes of news for insights into company events, market sentiment, and financial figures.
Limitations
- Primarily optimized for Egyptian stock-market news; performance may vary with other Arabic financial dialects.
- Numerical extraction quality is dependent on the clarity of figures in the source text.