SSAF FinBert

🚀 Model Card: SSAF-FinBert

The SSAF-FinBert model is a specialized tool for sentiment analysis of stock market news articles, offering accurate sentiment classification.

🚀 Quick Start

The SSAF-FinBert model is a fine - tuned version of the FinBert model. It's designed to classify the sentiment in financial text data into positive, negative, and neutral categories.

✨ Features

• Sentiment Classification: Classifies financial text sentiment into positive, negative, or neutral.
• High Accuracy: Achieved an accuracy of 81% - 82% on different training platforms.

📦 Installation

To access the SSAF - FinBert model for sentiment analysis, you can use the following Python code:

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

# Load the tokenizer used during fine - tuning
tokenizer = AutoTokenizer.from_pretrained('yiyanghkust/finbert-pretrain')

# Load the fine - tuned model
model_path = "likith123/SSAF-FinBert"
model = AutoModelForSequenceClassification.from_pretrained(model_path)

# Define a function for sentiment prediction
def predict_sentiment(input_text):
    # Tokenize the input text
    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True, max_length=512)

    # Perform inference
    with torch.no_grad():
        outputs = model(**inputs)

    # Get predicted probabilities for each class
    predicted_probs = torch.softmax(outputs.logits, dim=1).squeeze().tolist()

    return predicted_probs

💻 Usage Examples

Basic Usage

# Example usage
text_data = "This is a positive news article about the stock market."
predicted_sentiment = predict_sentiment(text_data)
print(predicted_sentiment)

The predicted_sentiment variable will contain a list of probabilities corresponding to each sentiment class, allowing you to analyze the sentiment expressed in the input text.

📚 Documentation

Labels Mapping

In Inference API Labels Mapping:

• Label_0 = Negative
• Label_1 = Neutral
• Label_2 = Positive

Finetuning Process

Access the Code for finetuning Kaggle Notebook. Access the WebAPP StreamLit APP.

Performance and Limitations

The SSAF - FinBert model has demonstrated strong performance in sentiment analysis tasks related to financial text data. However, it is essential to note that the model's accuracy may vary depending on the nature and complexity of the input text. While the model excels in classifying sentiment in financial news articles, it may not perform optimally in other domains or with highly specialized financial terminology.

📄 License

This project is licensed under the MIT license.

Acknowledgments

The development of the SSAF - FinBert model was made possible through the contributions of the open - source community and the resources provided by Hugging Face. We extend our gratitude to the developers and researchers who have contributed to the development and improvement of transformer - based models for natural language processing tasks.

For more information and updates on the SSAF - FinBert model, please refer to the model card.