REDME.md

# 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:

```python
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}")