qwen2-Alarm-json

qwen2-Alarm-json

Authors

• Lucía Herraiz Cano
• Álvaro Pérez Ortega
• Beatriz Otero Casanovas
• Alberto Prieto González

Universidad Pontificia Comillas — ICAI

License

MIT

Fine-tuned version of Qwen/Qwen2.5-7B-Instruct for news alert classification.

Given a news article with its image caption, named entities (NER) and sentiment analysis, the model determines whether an alert should be triggered and classifies it by level and category. Output is always a structured JSON object.

Model details

Output format

The model always responds with a single JSON object:

{
  "is_alert": true,
  "alert_level": "info",
  "category": "geopolitical",
  "reason": "One-sentence explanation of why the alert was triggered."
}

alert_level — "critical" · "info" · null

category — "brand_safety" · "legal_compliance" · "operational_risk" · "financial_market" · "geopolitical" · "esg_controversy" · "executive_risk" · "reputation_risk" · null

Benchmark results

Evaluated on 146 held-out test examples (same split used for training).

Binary classification (alert vs no_alert)

Field classification (on alert examples only, n=72)

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "beaotero05/qwen2-Alarm-json"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
)

messages = [
    {
        "role": "system",
        "content": (
            "You are a news alert classifier. "
            "Given a news article with its image caption, named entities, and sentiment, "
            "determine if an alert should be triggered. "
            "Always respond with a single valid JSON object and nothing else."
        )
    },
    {
        "role": "user",
        "content": """Article: <article text>

Image caption: <caption>

Named entities (NER): <entities>

Sentiment: <sentiment>

Return ONLY a JSON object with this exact structure:
{
  "is_alert": <true | false>,
  "alert_level": <"critical" | "info" | null>,
  "category": <"brand_safety" | "legal_compliance" | "operational_risk" | "financial_market" | "geopolitical" | "esg_controversy" | "executive_risk" | "reputation_risk" | null>,
  "reason": <"one-sentence explanation" | null>
}"""
    }
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=350, do_sample=False)

response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(response)