FinancialSentimentAnalyzer: FinBERT-tuned for Market News
π 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.
π€ 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.
π― Intended Use
This model is intended for:
- Algorithmic Trading: Providing sentiment scores for market-moving news to inform trade decisions.
- Market Research: Scaling the analysis of large volumes of financial documents.
- Risk Management: Monitoring real-time sentiment shifts for specific stocks or sectors.
β οΈ 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.
π» 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}]
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