Kenpache/perspective-aware-financial-sentiment

FLAME2 — Multilingual Perspective-Aware Financial Sentiment

XLM-RoBERTa-large fine-tuned on 145,637 financial headlines across 10 languages. Key innovation: Same news event → different sentiment per market perspective.

Key numbers

Base model	XLM-RoBERTa-large (560M params)
Languages	10 (AR, DE, EN, ES, FR, HI, JA, KO, PT, ZH)
Training data	145,637 perspective-labeled headlines
Overall Accuracy	84.11%
F1 Macro	84.20%
Labels	negative (0) / neutral (1) / positive (2)

Quick start

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="Kenpache/perspective-aware-financial-sentiment"
)

# IMPORTANT: Always prefix with [LANG] for correct perspective
classifier("[EN] Federal Reserve cuts interest rates by 25 basis points")
# → {'label': 'positive', 'score': 0.94}

classifier("[AR] Oil prices fall sharply to $60 per barrel")
# → {'label': 'negative', 'score': 0.89}  (Arab: oil exporter → bad)

classifier("[HI] Oil prices fall sharply to $60 per barrel")
# → {'label': 'positive', 'score': 0.87}  (India: oil importer → good)

Perspective-aware: same news, different labels

from transformers import pipeline
classifier = pipeline("text-classification",
                      model="Kenpache/perspective-aware-financial-sentiment")

oil_drop = "Oil prices fall to $60 per barrel"

results = {}
for lang in ["AR", "HI", "EN", "DE", "JA", "KO"]:
    r = classifier(f"[{lang}] {oil_drop}")[0]
    results[lang] = f"{r['label']} ({r['score']:.2f})"

# AR → negative  (Gulf exporter: lost revenue)
# HI → positive  (India importer: lower costs)
# EN → positive  (US: lower inflation)
# DE → positive  (Germany: lower energy costs)
# JA → positive  (Japan: lower import costs)
# KO → positive  (Korea: lower import costs)

Batch processing

headlines = [
    "[EN] Amazon reports record quarterly revenue of $187 billion",
    "[AR] OPEC+ agrees to increase oil output by 400,000 bpd",
    "[HI] OPEC+ agrees to increase oil output by 400,000 bpd",
    "[JA] Bank of Japan raises interest rates to 0.5%",
    "[DE] ECB cuts rates by 25 basis points amid slowdown",
    "[ZH] CSI 300 index rises 2.3% on stimulus optimism",
]

results = classifier(headlines)
for h, r in zip(headlines, results):
    print(f"{h[:50]:<50} → {r['label']} ({r['score']:.2f})")

Per-language performance

Language	Accuracy	F1 Macro
Hindi (hi)	89.33%	89.21%
French (fr)	87.45%	87.12%
English (en)	86.78%	86.54%
Chinese (zh)	85.92%	85.67%
German (de)	85.34%	85.01%
Spanish (es)	84.67%	84.43%
Japanese (ja)	84.12%	83.98%
Korean (ko)	83.76%	83.45%
Portuguese (pt)	83.54%	83.21%
Arabic (ar)	83.18%	82.87%
Overall	84.11%	84.20%

Training details

Parameter	Value
Base model	xlm-roberta-large
Batch size	32
Learning rate	2e-5
Epochs	5 (early stopping, patience=2)
Max sequence length	128 tokens
Precision	FP16
Label smoothing	0.1
Class weights	Balanced
Split	70/15/15 group-by-source

Language prefix injection: Each input is prefixed with [LANG] (e.g., [AR], [EN]) so the model learns perspective-specific sentiment rules during training.

Dataset

Trained on FLAME Perspective-Aware Financial Sentiment Dataset — 145,637 headlines across 10 languages with local investor perspective labeling.

When to use language prefixes

Use case	Prefix
Classifying Arabic financial news	[AR] headline
Classifying English financial news	[EN] headline
Unknown language	Detect language first, then prefix

Citation

@model{perspective_aware_financial_sentiment_2026,
  title     = {FLAME2: Multilingual Perspective-Aware Financial Sentiment Model},
  author    = {Zaraki},
  year      = {2026},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/Kenpache/perspective-aware-financial-sentiment}
}