---
language:
- en
license: apache-2.0
tags:
- finance
- sentiment-analysis
- text-classification
- lora
- mistral
- indian-market
- autoscientist
- adaption
datasets:
- financial_phrasebank
- zeroshot/twitter-financial-news-sentiment
- flwrlabs/fingpt-sentiment-train
- TimKoornstra/financial-tweets-sentiment
- nickmuchi/financial-classification
base_model: mistralai/Mistral-7B-Instruct-v0.3
metrics:
- accuracy
---

# 💹 Finance Sentiment Classifier — Indian Market Focus

> Fine-tuned on Mistral-7B-Instruct using LoRA via Adaption AutoScientist.  
> Classifies financial news, headlines, and social media text into **positive**, **negative**, or **neutral** sentiment.  
> Built for the [HackIndia Adaption AutoScientist Challenge](https://hackindia.xyz) — Finance track.

---

## 📊 Performance

| Metric | Base Model | Our Model | Improvement |
|---|---|---|---|
| Win Rate (our dataset) | 44 | 56 | **+27% relative** |
| Win Rate (market analysis category) | 40 | 60 | **+50% relative** |
| Dataset quality grade | E (2.0) | B (8.1) | **+305% relative** |

Training curves showed clean, consistent loss reduction with no overfitting across 4 epochs.

---

## 🧠 Model Details

| Property | Value |
|---|---|
| Base model | mistralai/Mistral-7B-Instruct-v0.3 |
| Training method | Supervised Fine-Tuning (SFT) + LoRA |
| LoRA rank | 64 |
| LoRA alpha | 128 |
| Target layers | q_proj, k_proj, v_proj, o_proj |
| Epochs | 4 |
| Optimizer | Cosine LR scheduler |
| Warmup ratio | 0.05 |
| Gradient clipping | 1.0 |
| Weight decay | 0.01 |
| Training platform | Adaption AutoScientist |
| Dataset size | 20,000 rows (adapted) |

---

## 📁 Dataset

The training dataset is a curated merge of **6 sources** totalling ~120,000 raw rows,
cleaned and deduplicated down to 20,000 high-quality rows via Adaption's Adaptive Data pipeline.

### Sources

| Source | Type | Rows (approx) |
|---|---|---|
| financial_phrasebank (sentences_allagree) | Human-labeled news sentences | ~2,200 |
| zeroshot/twitter-financial-news-sentiment | Human-labeled financial tweets | ~9,900 |
| flwrlabs/fingpt-sentiment-train | Financial NLP training data | ~76,800 |
| TimKoornstra/financial-tweets-sentiment | Human-labeled financial tweets | ~38,000 |
| nickmuchi/financial-classification | Financial text classification | ~2,000 |
| Hand-labeled originals (Indian market) | Original, manually written examples | ~60+ |
| NewsAPI live headlines | Rule-labelled recent business news | ~500 |

### What makes this dataset original

- **Indian market focus** — original hand-labeled examples covering NSE, BSE, Sensex, Nifty, RBI decisions, Indian fintech (Paytm, Zomato, PhonePe), and Indian conglomerates (Reliance, Tata, Adani, HDFC)
- **Multi-source deduplication** — priority-aware deduplication ensures highest-quality copy is retained when the same text appears across sources
- **Multilingual context** — includes financial terminology specific to the Indian subcontinent not present in standard Western finance NLP datasets
- **Live news augmentation** — recent business headlines via NewsAPI add temporal diversity beyond static datasets

### Label distribution (after cleaning)
```
positive :  ~35%
negative :  ~33%
neutral  :  ~32%
```
Balanced across all three classes to prevent label bias.

### Data quality improvement via Adaptive Data

Adaption's Adaptive Data pipeline was applied before training:
- **Before:** Grade E, quality score 2.0, percentile 0.1
- **After:**  Grade B, quality score 8.1, percentile 17.8
- **Relative improvement: 305%**

---

## 🚀 How to Use

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base_model = "mistralai/Mistral-7B-Instruct-v0.3"
lora_model  = "Sashank1006/finance-sentiment-mistral-lora"

tokenizer = AutoTokenizer.from_pretrained(base_model)
model     = AutoModelForCausalLM.from_pretrained(base_model, device_map="auto")
model     = PeftModel.from_pretrained(model, lora_model)
model.eval()

def predict_sentiment(text: str) -> str:
    prompt = f"Classify the sentiment of this financial text as positive, negative, or neutral:\n\n{text}\n\nSentiment:"
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(**inputs, max_new_tokens=5, do_sample=False)
    result = tokenizer.decode(output[0], skip_special_tokens=True)
    return result.split("Sentiment:")[-1].strip().lower()

# Example
text = "Reliance Industries reported a 23% jump in quarterly profit."
print(predict_sentiment(text))  # → "positive"
```

---

## 🌍 Real-World Applications

- **Retail investor tools** — classify financial news before displaying to users
- **Trading signal generation** — convert news sentiment into bullish/bearish signals
- **Portfolio risk monitoring** — flag negative sentiment around held stocks
- **Indian fintech apps** — specifically tuned for Indian market terminology and companies
- **News aggregators** — auto-tag financial articles by sentiment

---

## ⚠️ Limitations

- Trained primarily on English-language financial text; performance on Hindi/Tamil/regional language finance text will be lower
- Rule-labelled NewsAPI headlines (~500 rows) may contain some label noise
- Model may underperform on highly technical financial derivative or options-specific language
- Sentiment is classified at the sentence/headline level — document-level sentiment aggregation requires additional logic
- The model reflects sentiment patterns in training data up to mid-2026; sentiment around newer entities may be less accurate

---

## 📋 Training Pipeline

```
Raw data (6 sources, ~120K rows)
        ↓
Merge + deduplicate (prepare_dataset.py)
        ↓
Upload to Adaption Adaptive Data
        ↓
Adaptive Data optimization (Grade E → B, 305% quality improvement)
        ↓
AutoScientist fine-tuning (Mistral-7B-Instruct, LoRA, 4 epochs)
        ↓
Evaluation (Win rate: 44 → 56 on dataset, 40 → 60 on market analysis)
        ↓
Released on HuggingFace + Kaggle
```

---

## 👥 Team

**Team Caribou** — HackIndia Adaption AutoScientist Challenge, Finance Track

Built using:
- [Adaption AutoScientist](https://adaptionlabs.ai) — automated model training
- [Adaption Adaptive Data](https://adaptionlabs.ai) — dataset quality optimization
- HuggingFace Datasets — source data
- NewsAPI — live headline augmentation

---

## 📄 Citation

```bibtex
@misc{caribou2026financeSentiment,
  title  = {Finance Sentiment Classifier — Indian Market Focus},
  author = {Team Caribou},
  year   = {2026},
  url    = {https://huggingface.co/YOUR_HF_USERNAME/finance-sentiment-mistral-lora},
  note   = {Built for HackIndia Adaption AutoScientist Challenge}
}
```

---

## 🔗 Links

- 📦 Dataset: [Kaggle — finance-sentiment-india](https://www.kaggle.com/Sashank610/finance-sentiment-india)
- 🤗 Model: [HuggingFace — finance-sentiment-mistral-lora](https://huggingface.co/Sashank1006/finance-sentiment-mistral-lora)
- 🎮 Demo: [HuggingFace Spaces — Gradio App](https://huggingface.co/spaces/Sashank1006/finance-sentiment-demo)