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sweatSmile/
DialoGPT-Financial-Market-Sentiment-Trading-Assistant

Name: DialoGPT-Financial-Market-Sentiment-Trading-Assistant
Availability: InStock

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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

python

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.