finance-sentiment-classifier-bert-base

Model Overview

This model is a fine-tuned BERT-base model optimized for classifying the sentiment of financial news headlines, earnings call transcripts, and social media posts related to the finance sector. It supports three distinct sentiment labels: NEGATIVE (0), NEUTRAL (1), and POSITIVE (2). The model achieves high F1-scores on proprietary financial sentiment datasets, making it suitable for high-stakes analysis.

Model Architecture

The model is based on the BERT (Bidirectional Encoder Representations from Transformers) architecture.

Base Model: bert-base-uncased
Modification: The model is wrapped with a BertForSequenceClassification head. This means the pooled output of the BERT encoder (corresponding to the [CLS] token) is passed through a dropout layer and then a single linear layer (classifier) with 3 output units (one for each sentiment class), followed by a Softmax activation function.
Input: Tokenized text sequences (max length 512).
Output: Logits for the three sentiment classes.

Intended Use

This model is intended for:

Quantitative Finance: Automating the categorization of large volumes of unstructured financial text data.
Market Analysis: Tracking sentiment shifts in specific stocks, sectors, or the overall market.
Risk Management: Early identification of negative media sentiment that may precede market events.
Academic Research: Studying the correlation between public sentiment and market movements.

Limitations and Ethical Considerations

Domain Specificity: While strong in finance, performance may degrade significantly on general domain text (e.g., movie reviews).
Sarcasm/Context: Like all NLP models, it may struggle with highly contextual, subtle, or sarcastic financial commentary that requires external knowledge.
Bias: The training data may implicitly contain biases related to specific companies or market events, which could affect prediction accuracy. Users should monitor for drift and bias in real-world application.
Not Financial Advice: The model's predictions are purely analytical and should not be used as the sole basis for making investment decisions.

Example Code

To use the model in Python with the HuggingFace transformers library:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "YourOrg/finance-sentiment-classifier-bert-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Example financial texts
texts = [
    "Stock price soared 15% after better-than-expected earnings report.", # Positive
    "Company X faces significant regulatory hurdles, stock dropped 8%.",  # Negative
    "Analyst issues a neutral 'Hold' rating on Company Y.",              # Neutral
]

# Tokenize and predict
inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)

predictions = torch.argmax(outputs.logits, dim=-1)
labels = [model.config.id2label[p.item()] for p in predictions]

for text, label in zip(texts, labels):
    print(f"Text: '{text}' -> Sentiment: {label}")