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Model: tegana/qwen2.5-arabic-finance-news-parser
Source: Original Platform

README.md

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+---
+language:
+- ar
+license: apache-2.0
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+library_name: transformers
+tags:
+- fine-tuned
+- arabic
+- financial-nlp
+- information-extraction
+- lora
+- llama-factory
+datasets:
+- custom
+pipeline_tag: text-generation
+---
+
+# qwen2.5-arabic-finance-news-parser
+
+A fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) for **structured information extraction from Arabic financial news articles**.
+
+## Model Description
+
+This model was fine-tuned using **LoRA** via [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) on a dataset of ~2,792 Egyptian stock-market news articles. Given a news article and a JSON output schema, the model extracts structured data such as company name, event type, sentiment, financial figures, and a short summary.
+
+## Training Details
+
+| Setting | Value |
+|---|---|
+| Base model | Qwen/Qwen2.5-1.5B-Instruct |
+| Fine-tuning method | LoRA (rank 64, all targets) |
+| Dataset size | 2,792 samples (2,700 train / 92 val) |
+| Epochs | 3 |
+| Learning rate | 1e-4 (cosine scheduler) |
+| Max sequence length | 3,500 tokens |
+| Hardware | Kaggle T4 GPU |
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch, json
+
+model_id = "tegana/qwen2.5-arabic-finance-news-parser"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, device_map="auto", torch_dtype=torch.bfloat16
+)
+
+article = "القاهرة - واصل جهاز مستقبل مصر للتنمية المستدامة..."
+
+output_scheme = json.dumps({
+    "company_name": "اسم الشركة",
+    "event_type": "acquisition|earnings|dividends|...",
+    "sentiment": "positive|negative|neutral",
+    "impact_level": "high|medium|low",
+    "short_summary": "ملخص من 3 إلى 5 جمل"
+}, ensure_ascii=False)
+
+messages = [
+    {"role": "system", "content": (
+        "You are a professional Arabic financial news parser.\n"
+        "Extract structured information and return ONLY a valid JSON object."
+    )},
+    {"role": "user", "content": f"## Article:\n{article}\n\n## Output Scheme:\n{output_scheme}\n\n## Output JSON:"}
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    out = model.generate(inputs.input_ids, max_new_tokens=512, do_sample=False)
+
+out_ids = [o[len(i):] for i, o in zip(inputs.input_ids, out)]
+print(tokenizer.batch_decode(out_ids, skip_special_tokens=True)[0])
+```
+
+## Supported Event Types
+
+`earnings` · `capital_increase` · `capital_decrease` · `dividends` · `acquisition` · `sale_of_stake` · `financing` · `project` · `board_decision` · `regulatory_approval` · `analysis_financial` · `stock_exchange_decision` · `other`
+
+## Limitations
+
+- Trained primarily on Egyptian stock-market news; may underperform on other Arabic financial dialects.
+- Numerical extraction quality depends on how clearly figures appear in the source text.
+
+## License
+
+Apache 2.0 — same as the base model.