fin-impact-xlmr

fin-impact-xlmr is a multilingual regression model that estimates the financial relevance / impact of news headlines.

The model outputs a continuous score between 0 and 10, where higher values indicate stronger financial relevance.

---

What does this model do?

Given a short text (typically a news headline), the model predicts how strongly the content is related to:

• financial markets
• macroeconomics
• corporate finance

This is not a sentiment model.

---

Supported Languages

• Turkish
• English
• French
• German
• Spanish
• Italian
• Arabic
• Japanese
• Russian
• Chinese

Language tags are not required in the input.

---

Evaluation (approx.)

• R2: ~0.62
• MAE: ~1.0 (on 0–10 scale)

---

Example

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("stocky-ai/fin-impact-xlmr")
model = AutoModelForSequenceClassification.from_pretrained("stocky-ai/fin-impact-xlmr")

def predict(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        return model(**inputs).logits.item() * 10

print(predict("Central bank raised interest rates amid inflation concerns"))