Sashank1006
/

finance-sentiment-mistral-lora

@@ -1,202 +1,204 @@
 ---
-base_model: togethercomputer/Mistral-7B-Instruct-v0.2
-library_name: peft
 ---
-# Model Card for Model ID
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-### Framework versions
-- PEFT 0.15.1

 ---
+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)