Modern-FinBERT-large: Financial Sentiment Analysis
Modern-FinBERT-large 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 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
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-large', num_labels=3)
tokenizer = AutoTokenizer.from_pretrained('beethogedeon/Modern-FinBERT-large')
# Initialize the NLP pipeline
nlp = pipeline("text-classification", model=model, tokenizer=tokenizer)
sentence = "Stocks rallied and the British pound gained."
print(nlp(sentence))