DialoGPT-Financial-Market-Sentiment-Trading-Assistant

DialoGPT-Financial-Market-Sentiment-Trading-Assistant

Fine-tuned DialoGPT-small for financial news sentiment analysis and market sentiment interpretation for trading and investment decisions.

Overview

• Base Model: microsoft/DialoGPT-small (117M parameters)
• Fine-tuning Method: LoRA (4-bit quantization)
• Dataset: Financial news sentiment dataset (1.5K samples)
• Training: 3 epochs with optimized hyperparameters

Key Features

• Real-time financial news sentiment analysis
• Market sentiment interpretation for trading decisions
• Bullish/Bearish/Neutral sentiment classification
• Conversational interface for market analysis
• Optimized for trading desks and investment research

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("sweatSmile/DialoGPT-Financial-Market-Sentiment-Trading-Assistant")
tokenizer = AutoTokenizer.from_pretrained("sweatSmile/DialoGPT-Financial-Market-Sentiment-Trading-Assistant")

# Market sentiment analysis example
prompt = "<|user|> What's the market sentiment for this news: Tesla stock surges after record delivery numbers <|bot|>"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150, pad_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Applications

• Trading desk sentiment analysis
• Algorithmic trading signal generation
• Investment research and market analysis
• Hedge fund market sentiment monitoring
• Financial news interpretation for portfolio decisions

Training Details

• LoRA rank: 8, alpha: 16
• 4-bit NF4 quantization with fp16 precision
• Learning rate: 3e-4 with linear scheduling
• Batch size: 8, Max length: 256 tokens
• 3 epochs on curated financial sentiment data

Specialized for high-frequency market sentiment analysis in trading environments.