Kenpache/flame2

FLAME2 — Financial Language Analysis for Multilingual Economics v2

One model. Ten languages. 150,000 headlines. Perspective-aware financial sentiment.

FLAME2 is a multilingual financial sentiment classifier that labels news headlines as Negative, Neutral, or Positive — but unlike other models, it does this from the local investor's perspective of each economy.

The same news can mean opposite things for different markets:

• —"Oil prices fall to $65/barrel" → Negative for Arab markets (oil exporter) / Positive for India (oil importer)
• —"Yen weakens to 155 per dollar" → Positive for Japan (helps exporters) / Neutral elsewhere

No other public model does this.

Key Numbers

Quick Start

from transformers import pipeline

classifier = pipeline("text-classification", model="Kenpache/flame2")

# English — US investor perspective
classifier("[EN] Apple reported record quarterly revenue of $124 billion")
# [{'label': 'positive', 'score': 0.96}]

# Arabic — Gulf investor perspective
classifier("[AR] أسعار النفط تنخفض إلى 65 دولارا للبرميل")
# [{'label': 'negative', 'score': 0.93}]  (oil down = bad for exporters)

# Hindi — Indian investor perspective
classifier("[HI] तेल की कीमतें गिरकर 65 डॉलर प्रति बैरल हुईं")
# [{'label': 'positive', 'score': 0.91}]  (oil down = good for importers)

# Japanese
classifier("[JA] 日経平均株価が大幅下落、米中貿易摩擦の懸念で")
# [{'label': 'negative', 'score': 0.94}]

# Korean
classifier("[KO] 삼성전자 실적 호조에 코스피 상승")
# [{'label': 'positive', 'score': 0.92}]

# Chinese
classifier("[ZH] 中国央行降息50个基点，股市应声上涨")
# [{'label': 'positive', 'score': 0.95}]

# German
classifier("[DE] DAX erreicht neues Allzeithoch dank starker Bankenergebnisse")
# [{'label': 'positive', 'score': 0.93}]

# French
classifier("[FR] La Bourse de Paris chute de 3% après les tensions commerciales")
# [{'label': 'negative', 'score': 0.91}]

# Spanish
classifier("[ES] El beneficio neto de la compañía creció un 25% interanual")
# [{'label': 'positive', 'score': 0.94}]

# Portuguese
classifier("[PT] Ibovespa fecha em alta com otimismo sobre reforma tributária")
# [{'label': 'positive', 'score': 0.90}]

Important: Always use the [LANG] prefix ([EN], [AR], [HI], [JA], etc.) — this tells the model which market perspective to apply.

Supported Languages & Training Data

Total: 149,481 labeled headlines across 10 languages.

Overall Class Distribution

Data sources include financial news sites, stock market reports, and economic news agencies — labeled with perspective-aware rules specific to each economy.

What Makes FLAME2 Different

The Problem

Existing financial sentiment models treat sentiment as universal. But financial sentiment is not universal — it depends on where you are:

• —Oil prices drop? Bad for Saudi Arabia, great for India.
• —Yen weakens? Good for Japanese exporters, bad for Korean competitors.
• —Fed raises rates? Bad for US stocks, often neutral for European markets.

Our Solution: Perspective-Aware Labels

Every headline in our dataset was labeled from the perspective of a local investor in that language's primary economy. The model learns that [AR] means "Gulf investor" and [HI] means "Indian investor."

Oil Price Rules

Currency Rules

Central Bank Rules

• —Home central bank: rate cut = Positive, rate hike = Negative, hold = Neutral
• —Foreign central bank: Neutral (unless headline explicitly links to local market impact)

Labels

Results

Overall

Per-Language Performance

Per-Class Performance

Training Pipeline

FLAME2 was built in two stages:

Stage 1: Supervised Fine-Tuning

XLM-RoBERTa-large was fine-tuned on ~150,000 perspective-labeled headlines with:

• —Focal Loss (gamma=2.0) — focuses training on hard, misclassified examples instead of easy ones
• —Class weights to handle label imbalance across languages
• —Label smoothing (0.1) to handle ~3-5% annotation noise
• —Language prefix [LANG] injected before each headline for perspective routing
• —GroupShuffleSplit by news source domain — no article from the same source appears in both train and test (prevents data leakage)
• —Gradient clipping (max_norm=1.0) for training stability

Stage 2: Live Stochastic Weight Averaging (SWA)

After epoch 12, the learning rate switches to a constant low rate (1e-5) and an AveragedModel maintains a running average of weights updated every epoch. This produces smoother, more generalizable predictions than any single checkpoint.

Training Details

Batch Processing

from transformers import pipeline

classifier = pipeline("text-classification", model="Kenpache/flame2", device=0)

texts = [
    "[EN] Stocks rallied after the Fed signaled a pause in rate hikes.",
    "[EN] The company filed for Chapter 11 bankruptcy protection.",
    "[DE] DAX erreicht neues Allzeithoch dank starker Bankenergebnisse",
    "[FR] La Bourse de Paris chute de 3% après les tensions commerciales",
    "[ES] El beneficio neto de la compañía creció un 25% interanual",
    "[ZH] 中国央行降息50个基点，股市应声上涨",
    "[PT] Ibovespa fecha em alta com otimismo sobre reforma tributária",
    "[AR] ارتفاع مؤشر السوق السعودي بنسبة 2% بعد إعلان أرباح أرامكو",
    "[HI] भारतीय रिजर्व बैंक ने रेपो रेट में 25 बीपीएस की कटौती की",
    "[JA] トヨタ自動車の純利益が前年比30%増加",
    "[KO] 삼성전자 실적 호조에 코스피 상승",
]

results = classifier(texts, batch_size=32)
for text, result in zip(texts, results):
    print(f"{result['label']:>8} ({result['score']:.2f})  {text[:70]}")

Use Cases

• —Global News Monitoring — real-time sentiment classification across 10 markets
• —Algorithmic Trading — perspective-aware signals: same event, different trades per market
• —Portfolio Risk Management — track sentiment shifts across international holdings
• —Cross-Market Arbitrage — detect when markets react differently to the same news
• —Financial NLP Research — first multilingual perspective-aware sentiment benchmark

Limitations

• —Optimized for news headlines (short text, 1-2 sentences). May underperform on long articles or social media.
• —Perspective rules cover major economic patterns (oil, currency, central banks). Niche sector-specific effects may not be captured.
• —Labels reflect the perspective of the primary economy for each language (e.g., AR = Gulf States, not all Arabic-speaking countries).

Citation

@misc{flame2_2026,
  title={FLAME2: Financial Language Analysis for Multilingual Economics v2},
  author={Kenpache},
  year={2026},
  url={https://huggingface.co/Kenpache/flame2}
}

License

Apache 2.0