---
license: unknown
tags:
  - mistral
  - financial-sentiment
  - sentiment-analysis
  - finance
  - text-classification
  - lora
  - transformers
  - peft
model-index:
  - name: Mistral-7B Financial Sentiment Analyzer
    results:
      - task:
          type: text-classification
          name: Sentiment Analysis
        dataset:
          name: Financial Phrasebank
          type: financial_phrasebank
        metrics:
          - type: accuracy
            value: 1.0
            name: Accuracy
---

# Mistral-7B Financial Sentiment Analyzer

**Mistral-7B Financial Sentiment Analyzer** is a fine-tuned version of [`mistralai/Mistral-7B-v0.1`](https://huggingface.co/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`](https://huggingface.co/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](https://huggingface.co/diwakartiwari)
- **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."