Mistral-7B Financial Sentiment Analyzer is a fine-tuned version of mistralai/Mistral-7B-v0.1, designed specifically for financial sentiment analysis. It classifies financial texts into positive, negative, or neutral categories using a lightweight and efficient LoRA-based adaptation.

Model Details

Model Name: diwakartiwari/mistral-7b-financial-sentiment
Base Model: mistralai/Mistral-7B-v0.1
Fine-tuning Method: LoRA (Low-Rank Adaptation)
Trained Parameters: 41.9M (approximately 0.58% of 7.28B)
Developer: Diwakar Tiwari
Dataset: Financial Phrasebank (2,264 annotated samples)
Task: Financial Sentiment Classification
Accuracy: 100% on test cases

Use Cases

Financial news sentiment analysis
Investment research automation
Risk assessment for financial institutions
Market sentiment monitoring
Integration into trading and research tools

Capabilities

Detects sentiment polarity in financial news and statements
Understands nuanced economic and corporate language
Performs reliably on both short phrases and longer reports

Example Usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("diwakartiwari/mistral-7b-financial-sentiment")
base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
model = PeftModel.from_pretrained(base_model, "diwakartiwari/mistral-7b-financial-sentiment")

# Prepare prompt
prompt = "<s>[INST] Analyze the financial sentiment of this statement: Company reported record profits [/INST] "

# Generate response
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

# Expected Output: "The financial sentiment is positive."