Distilbert Base Uncased Financial News Sentiment Analysis

🚀 DistilBERT Fine-Tuned for Financial Sentiment Analysis

This model is fine - tuned for sentiment analysis in the financial domain, classifying financial texts into negative, neutral, or positive sentiment.

🚀 Quick Start

You can load and use the model with the Hugging Face transformers library as follows:

Basic Usage

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("AnkitAI/distilbert-base-uncased-financial-news-sentiment-analysis")
model = AutoModelForSequenceClassification.from_pretrained("AnkitAI/distilbert-base-uncased-financial-news-sentiment-analysis")

text = "The company's revenue declined significantly due to market competition."
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)

logits = outputs.logits
predicted_class_id = logits.argmax().item()

label_mapping = {0: "Negative", 1: "Neutral", 2: "Positive"}
predicted_label = label_mapping[predicted_class_id]

print(f"Text: {text}")
print(f"Predicted Sentiment: {predicted_label}")

✨ Features

• Domain - Specific: This model is a fine - tuned version of [distilbert - base - uncased](https://huggingface.co/distilbert - base - uncased) specifically tailored for sentiment analysis in the financial domain.
• Multi - Class Classification: It classifies financial texts into three sentiment categories: negative (label 0), neutral (label 1), and positive (label 2).

📚 Documentation

Model Description

This model is a fine - tuned version of [distilbert - base - uncased](https://huggingface.co/distilbert - base - uncased) specifically tailored for sentiment analysis in the financial domain. It has been trained on the Financial PhraseBank dataset to classify financial texts into three sentiment categories:

• Negative (label 0)
• Neutral (label 1)
• Positive (label 2)

Model Performance

The model was trained for 5 epochs and evaluated on a held - out test set constituting 20% of the dataset.

Evaluation Metrics

Training Metrics

• Final Training Loss: 0.0797
• Total Training Time: Approximately 3869 seconds (~1.07 hours)
• Training Samples per Second: 2.34
• Training Steps per Second: 0.147

Training Procedure

Data

• Dataset: Financial PhraseBank
• Configuration: sentences_allagree (sentences where all annotators agreed on the sentiment)
• Dataset Size: 2264 sentences
• Data Split: 80% training (1811 samples), 20% testing (453 samples)

Model Configuration

• Base Model: [distilbert - base - uncased](https://huggingface.co/distilbert - base - uncased)
• Number of Labels: 3 (negative, neutral, positive)
• Tokenizer: Same as the base model's tokenizer

Hyperparameters

• Number of Epochs: 5
• Batch Size: 16 (training), 64 (evaluation)
• Learning Rate: 5e - 5
• Optimizer: AdamW
• Evaluation Metric: Accuracy
• Seed: 42 (for reproducibility)

📄 License

This model is licensed under the Apache 2.0 License. You are free to use, modify, and distribute this model in your applications.

📚 Citation

If you use this model in your research or applications, please cite it as:

@misc{AnkitAI_2024_financial_sentiment_model,
  title={DistilBERT Fine-Tuned for Financial Sentiment Analysis},
  author={Ankit Aglawe},
  year={2024},
  howpublished={\url{https://huggingface.co/AnkitAI/distilbert-base-uncased-financial-news-sentiment-analysis}},
}

👏 Acknowledgments

• Hugging Face: For providing the Transformers library and model hosting.
• Data Providers: Thanks to the creators of the Financial PhraseBank dataset.
• Community: Appreciation to the open - source community for continual support and contributions.

📞 Contact Information

For questions, feedback, or collaboration opportunities, please contact:

• Name: Ankit Aglawe
• Email: [aglawe.ankit@gmail.com]
• GitHub: [GitHub Profile](https://github.com/ankit - aglawe)
• LinkedIn: [LinkedIn Profile](https://www.linkedin.com/in/ankit - aglawe)