beethogedeon/Modern-FinBERT

Modern-FinBERT: Financial Sentiment Analysis

Modern-FinBERT is a pre-trained NLP model designed for financial sentiment analysis. It extends the `ModernBERT-large` language model by further training it on a large financial corpus, making it highly specialized for financial text classification.

For fine-tuning, the model leverages the [Financial PhraseBank](https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientations_in_Economic_Texts) by Malo et al. (2014), a widely recognized benchmark dataset for financial sentiment analysis.

Sentiment Labels

The model generates a softmax probability distribution across three sentiment categories:

• —✅ Positive
• —❌ Negative
• —⚖ Neutral

For more technical insights on ModernBERT, check out the research paper: 🔍 [ModernBERT Technical Details](https://arxiv.org/abs/2412.13663)

How to use

You can use this model with Transformers pipeline for sentiment analysis.

pip install -U transformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

# Load the pre-trained model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained('beethogedeon/Modern-FinBERT', num_labels=3)
tokenizer = AutoTokenizer.from_pretrained('answerdotai/ModernBERT')

# Initialize the NLP pipeline
nlp = pipeline("text-classification", model=model, tokenizer=tokenizer)

sentence = "Stocks rallied and the British pound gained."

print(nlp(sentence))