metadata

license: apache-2.0
datasets:
  - takala/financial_phrasebank
language:
  - en
metrics:
  - f1
base_model:
  - answerdotai/ModernBERT-large
new_version: ProsusAI/finbert
pipeline_tag: text-classification
library_name: transformers
tags:
  - finance
  - sentiment
  - financial-sentiment-analysis
  - sentiment-analysis
widget:
  - text: Stocks rallied and the British pound gained.

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('answerdotai/ModernBERT-large')

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

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

print(nlp(sentence))