FinBERT-finetuned GitHub Open-source Model - Free Deployment for Precise Financial Text Sentiment Classification

Finbert Finetuned Github

Developed by Driisa

This model is a financial sentiment classification model fine-tuned on FinBERT, capable of categorizing financial texts into negative, neutral, and positive classes.

Text Classification

Transformers

EnglishOpen Source License:MIT #Financial Sentiment Analysis #Earnings Report Sentiment Recognition #High-Precision FinBERT

Downloads 4,622

Release Time : 2/14/2025

Model Overview

A financial sentiment analysis model fine-tuned on FinBERT, suitable for sentiment classification tasks in financial texts.

Model Features

Financial Domain Optimization

Fine-tuned for financial texts to improve the accuracy of sentiment analysis in the financial domain.

Multi-Dataset Training

Trained on a combination of financial phrase banks and GitHub-generated datasets to enhance model generalization.

Efficient Classification

Capable of quickly and accurately classifying financial texts as negative, neutral, or positive.

Model Capabilities

Financial Text Sentiment Analysis

Three-Class Sentiment Prediction

Financial Domain NLP Processing

Use Cases

Financial Analysis

Earnings Report Sentiment Analysis

Analyze the sentiment tendencies in company earnings reports to aid investment decisions.

Accuracy 95.21% (Financial Phrase Bank training set)

Stock Comment Sentiment Analysis

Evaluate market sentiment in stock comments.

Quantitative Trading

Market Sentiment Indicators

Provide market sentiment data for quantitative trading strategies.

🚀 FinBERT Fine-Tuned on Financial Sentiment (Financial PhraseBank + GitHub Dataset)

This is a fine - tuned version of FinBERT for financial sentiment classification, capable of categorizing financial text into negative, neutral, and positive classes.

🚀 Quick Start

You can use the model via Hugging Face Transformers:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_name = "Driisa/finbert-finetuned-github"

# Load model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example input
text = "The company's stock has seen significant growth this quarter."

# Tokenize and predict
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
outputs = model(**inputs)

# Get predicted class
predicted_class = outputs.logits.argmax().item()
print(f"Predicted Sentiment: {['Negative', 'Neutral', 'Positive'][predicted_class]}")

✨ Features

This model is a fine - tuned version of FinBERT (ProsusAI/finbert) trained for financial sentiment classification.
It can classify financial text into three categories: Negative (0), Neutral (1), and Positive (2).

📦 Installation

The README does not provide installation steps, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_name = "Driisa/finbert-finetuned-github"

# Load model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example input
text = "The company's stock has seen significant growth this quarter."

# Tokenize and predict
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
outputs = model(**inputs)

# Get predicted class
predicted_class = outputs.logits.argmax().item()
print(f"Predicted Sentiment: {['Negative', 'Neutral', 'Positive'][predicted_class]}")

📚 Documentation

📂 Dataset Used

This model was trained on:
✅ Financial PhraseBank - A widely used financial sentiment dataset.
✅ GitHub Generated Sentiment Dataset - An additional dataset to test the model.

⚙️ Training Parameters

Parameter	Value
Model Architecture	FinBERT (based on BERT)
Batch Size	8
Learning Rate	2e-5
Epochs	3
Optimizer	AdamW
Evaluation Metric	F1-Score, Accuracy

📊 Model Performance

Dataset	Accuracy	F1 (Weighted)	Precision	Recall
Financial PhraseBank (Train)	95.21%	95.23%	95.32%	95.21%
GitHub Test Set	64.42%	64.34%	70.52%	64.42%

🚀 Intended Use

This model is designed for:
✅ Financial Analysts & Investors to assess sentiment of financial sentences in ex. reports, news, and stock discussions.
✅ Financial Institutions for NLP-based sentiment analysis in automated trading.
✅ AI Researchers exploring financial NLP models.

⚠️ Limitations

⚠️ Important Note

May not generalize well to datasets with very different financial language.

Might require fine-tuning for specific financial domains (crypto, banking, startups).

📄 License

This model is licensed under the MIT license.

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