FinancialSentimentAnalyzer: FinBERT-tuned for Market News

πŸ“‘ Overview

This model is a fine-tuned version of the bert-base-uncased pre-trained model for Sequence Classification. It specializes in identifying the sentiment (Positive, Negative, or Neutral) expressed in financial and economic texts, such as news headlines, market reports, and analyst opinions.

πŸ€– Model Architecture

The model uses the standard BERT (Bidirectional Encoder Representations from Transformers) architecture.

  • Base Model: bert-base-uncased.
  • Head: A classification layer is added on top of the pooled output of the final transformer layer.
  • Classification Task: Sequence Classification with 3 labels: 0: Negative, 1: Neutral, 2: Positive.
  • Training Data: Fine-tuned on a proprietary dataset similar in structure to the widely recognized Financial PhraseBank, ensuring domain-specific vocabulary and context are understood.

🎯 Intended Use

This model is intended for:

  1. Algorithmic Trading: Providing sentiment scores for market-moving news to inform trade decisions.
  2. Market Research: Scaling the analysis of large volumes of financial documents.
  3. Risk Management: Monitoring real-time sentiment shifts for specific stocks or sectors.

⚠️ Limitations

  • Ambiguity: Financial language is often highly technical and can be contextually neutral (e.g., "The stock fell 5%"). The model performs best on explicitly opinionated text.
  • Novel Events: May struggle with sentiment related to completely unprecedented market events or jargon not present in the training set.
  • Language: Only suitable for English text.

πŸ’» Example Code

Use the pipeline feature for quick inference:

from transformers import pipeline

# Load the model and tokenizer
sentiment_pipeline = pipeline("sentiment-analysis", model="[YOUR_HF_USERNAME]/FinancialSentimentAnalyzer")

# Test cases
result1 = sentiment_pipeline("Tesla's revenue beat expectations, leading to a surge in stock price.")
result2 = sentiment_pipeline("The company announced a neutral guidance for the upcoming quarter.")
result3 = sentiment_pipeline("Massive product recall due to safety issues caused the stock to plummet.")

print(result1)
# [{'label': 'Positive', 'score': 0.998}]
print(result2)
# [{'label': 'Neutral', 'score': 0.985}]
print(result3)
# [{'label': 'Negative', 'score': 0.999}]
Downloads last month
18
Inference Providers NEW
This model isn't deployed by any Inference Provider. πŸ™‹ Ask for provider support

Dataset used to train Tasfiya025/FinancialSentimentAnalyzer