91 lines
3.0 KiB
Markdown
91 lines
3.0 KiB
Markdown
---
|
|
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.
|