Finance Sentiment
These models are specialized for evaluating sentiment in financial news and market-related content. Unlike general-purpose sentiment models that often misclassify objective financial reporting as neutral, these models are fine-tuned to distinguish subtle positive, negative, and truly neutral signals in financial language.
Each language has its own dedicated model, trained and evaluated on high-quality financial datasets to capture language-specific nuances. This makes them particularly effective for applications such as market sentiment analysis, portfolio risk monitoring, trading signal generation, and financial news aggregation.
How to use
from transformers import pipeline
nlp = pipeline("sentiment-analysis", model="bardsai/finance-sentiment-fr-base")
nlp("Le chiffre d'affaires net a augmente de 30 % pour atteindre 36 millions d'euros."){'label': 'positive', 'score': 0.9999314546585083}Performance Metrics
| Metric | Value |
|---|---|
| F1 Macro | 0.953 |
| Precision | 0.959 |
| Recall | 0.949 |
| Accuracy | 0.961 |
FAQs
You might want to check those too!
Polish Whisper
Whisper ASR models fine-tuned for Polish Language - winner of Whisper Fine Tuning Event Challenge
Jaskier 7b LLM
Top-ranked <=7B LLM (Feb 2024): Fast, smart, open-source. Fine-tuned for exceptional reasoning and instruction-following.
Fast Twitter Emotion Detection
Emotion classification model for Polish tweets. Detects joy, optimism, sadness, and anger with high accuracy and speed.
Looking to integrate AI into your
product or project?
Get a Free consultation with our AI experts.