FLAME — Financial Language Analysis for Multilingual Economics

One model. Six languages. Real financial sentiment.

FLAME classifies financial text as Negative, Neutral, or Positive across English, Chinese, Japanese, German, French, and Spanish — in a single model, no language detection needed.

Built on XLM-RoBERTa with domain-adaptive pretraining on 35K+ financial texts, then fine-tuned on ~39K real financial news samples from 80+ sources worldwide.

Quick Start

from transformers import pipeline

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

# English
classifier("Apple reported record quarterly revenue of $124 billion, up 11% year over year.")
# [{'label': 'Positive', 'score': 0.96}]

# Chinese
classifier("该公司季度亏损扩大至5亿美元，远超市场预期。")
# [{'label': 'Negative', 'score': 0.94}]

# Japanese
classifier("トヨタ自動車の営業利益は前年同期比30%増の1兆円を突破した。")
# [{'label': 'Positive', 'score': 0.95}]

# German
classifier("Die Aktie verlor nach der Gewinnwarnung deutlich an Wert.")
# [{'label': 'Negative', 'score': 0.92}]

# French
classifier("Le chiffre d'affaires du groupe a progressé de 8% au premier semestre.")
# [{'label': 'Positive', 'score': 0.93}]

# Spanish
classifier("Las acciones de la empresa se mantuvieron estables tras la publicación de resultados.")
# [{'label': 'Neutral', 'score': 0.89}]

Batch Processing

from transformers import pipeline

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

texts = [
    "Stocks rallied after the Fed signaled a pause in rate hikes.",
    "The company filed for Chapter 11 bankruptcy protection.",
    "Q3 earnings were in line with analyst expectations.",
    "日経平均株価が3万円台を回復した。",
    "Les marchés européens ont clôturé en forte baisse.",
    "El beneficio neto de la compañía creció un 25% interanual.",
]

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

Results

Metric	Score
Accuracy	0.8103
F1 (weighted)	0.8102
Precision (weighted)	0.8111
Recall (weighted)	0.8103

Per-Class Performance

Class	Precision	Recall	F1	Support
Negative	0.78	0.83	0.81	917
Neutral	0.83	0.79	0.81	1,779
Positive	0.80	0.82	0.81	1,225

All three classes achieve balanced F1=0.81, even with imbalanced training data (Neutral 45%, Positive 31%, Negative 24%).

Labels

Label	ID	What it captures
Negative	0	Losses, decline, bearish signals, layoffs, bankruptcy
Neutral	1	Factual statements, announcements, no clear sentiment
Positive	2	Growth, gains, bullish signals, record earnings, upgrades

Supported Languages

Language	Code	Training Samples	Key Sources
Japanese	JA	8,287	Nikkei, Nikkan Kogyo, Reuters JP
Chinese	ZH	7,930	Sina Finance, EastMoney, 10jqka
Spanish	ES	7,125	Expansión, Cinco Días, Bloomberg Línea
English	EN	6,887	CNBC, Yahoo Finance, Fortune, Reuters
German	DE	5,023	Börse.de, FAZ, NTV Börse
French	FR	3,935	Boursorama, Tradingsat, BFM Business

Use Cases

• News Monitoring — classify sentiment of financial headlines across global markets in real time
• Trading Signals — feed sentiment scores into quantitative trading strategies
• Portfolio Risk — monitor sentiment shifts across international holdings
• Earnings Analysis — analyze tone of corporate press releases and earnings calls
• Social Media — track financial discussions on multilingual platforms
• Research — cross-language sentiment studies in financial NLP

How It Was Built

1. Domain Adaptation (TAPT): Masked Language Modeling on 35K+ financial texts across 6 languages — the model learns financial vocabulary and patterns before seeing any labels.

2. Fine-Tuning: Supervised classification with label smoothing (0.1), cosine LR schedule (2e-5), and Stochastic Weight Averaging of top-3 checkpoints for robust generalization.

Parameter	Value
Base model	xlm-roberta-base (278M params)
Learning rate	2e-5
Scheduler	Cosine
Label smoothing	0.1
Effective batch size	64
Precision	FP16
Post-processing	SWA (top-3 checkpoints)

Dataset

Trained on Kenpache/multilingual-financial-sentiment — ~39K curated financial news samples from 80+ real sources worldwide.

Citation

@misc{flame2025,
  title={FLAME: Financial Language Analysis for Multilingual Economics},
  author={Kenpache},
  year={2025},
  url={https://huggingface.co/Kenpache/flame}
}

License

Apache 2.0