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README.md

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+---
+language: en
+license: apache-2.0
+tags:
+- finance
+- sentiment-analysis
+- text-classification
+- llama
+- qlora
+pipeline_tag: text-classification
+library_name: transformers
+---
+
+# Llama Sentiment Classifier (QLoRA Fine-Tuned)
+
+This repository provides a **LLaMA-based financial sentiment classifier** fine-tuned using **QLoRA** for **3-class sentiment classification** on finance-domain text. The model is designed for downstream applications including **sentiment-driven alpha signal generation** and market-neutral trading strategies.
+
+---
+
+## Model Summary
+
+- **Backbone**: Meta-LLaMA3-8B  
+- **Task**: Financial sentiment classification (3 classes)  
+- **Fine-tuning method**: **QLoRA** (4-bit quantization + LoRA adapters)  
+- **Training framework**: **SWIFT** (Scalable lightWeight Infrastructure for Fine-Tuning)  
+- **Architecture**: LlamaForSequenceClassification  
+
+---
+
+## Labels
+
+This model performs **single-label classification** into 3 discrete classes.
+
+In the paper, the unified sentiment label space is:
+
+- **-1** = Negative  
+- **0** = Neutral  
+- **1** = Positive  
+
+> Note: your Hugging Face config may show `LABEL_0`, `LABEL_1`, `LABEL_2`.
+
+---
+
+## Datasets
+
+### Part 1 — Fine-tuning datasets (4 sources)
+
+Training uses 4 finance-domain sentiment datasets:
+
+1. **Financial PhraseBank v1.0**  
+   - 4,840 manually annotated sentences  
+   - 3-class sentiment (positive/neutral/negative)
+
+2. **NASDAQ News Sentiment**  
+   - synthetic sentiment dataset generated using GPT-4o  
+   - labeled into positive/neutral/negative
+
+3. **Twitter Financial News Sentiment**  
+   - 11,932 finance-related tweets  
+   - original label encoding: 0 bearish, 1 bullish, 2 neutral
+
+4. **FIQA2018 (FiQA)**  
+   - financial opinion mining dataset  
+   - sentiment score in [-1, 1] (continuous), then discretized
+
+---
+
+### Part 2 — Real-world news evaluation dataset
+
+For downstream evaluation and trading strategy experiments, the paper uses a news + price dataset covering:
+
+- **502 S&P 500 companies**  
+- **~77,000 news headlines**  
+- time range: **2024-01-01 to 2025-05-30**
+
+---
+
+## Preprocessing
+
+### Label normalization (unified mapping)
+
+All datasets are standardized into the unified label space: {-1, 0, 1}.
+
+Mapping rules:
+
+- **Financial PhraseBank**:  
+  `"negative" → -1`, `"neutral" → 0`, `"positive" → 1`
+
+- **FIQA2018** (score in [-1, 1]):  
+  `< -0.2 → -1`, `> 0.2 → 1`, otherwise `0`
+
+- **Twitter Financial Sentiment**:  
+  `0 → -1`, `2 → 0`, `1 → 1`
+
+- **NASDAQ News** (0~5 score):  
+  `<= 1 → -1`, `>= 4 → 1`, otherwise `0`
+
+### Tokenization
+
+Uses the official **LLaMA 3 tokenizer**.
+
+---
+
+## Training Method
+
+### QLoRA fine-tuning
+
+The model is fine-tuned with **QLoRA**, which keeps the backbone weights in 4-bit quantized form and trains LoRA adapters in higher precision.
+
+The paper’s QLoRA setup includes NF4 quantization + double quantization for memory efficiency.
+
+### SWIFT training framework
+
+Fine-tuning is orchestrated using **SWIFT**, a modular training framework that simplifies adapter integration and efficient training for quantized models.
+
+---
+
+## Hyperparameters (paper)
+
+- LoRA rank **r = 16**
+- LoRA alpha **= 32**
+- LoRA dropout **= 0.1**
+- learning rate **1e-4**, optimizer **AdamW**
+- batch size **8**, epochs **1**
+- gradient accumulation **4**
+- gradient checkpointing enabled
+- cosine learning rate schedule
+
+---
+
+## Evaluation Results (paper)
+
+On a test set of **9,064** finance news samples, the fine-tuned model reports:
+
+- **Accuracy**: **92.18%**
+- **Micro-F1**: **0.9218**
+- **Macro-F1**: **0.5787**
+
+Class-wise performance highlights strong **Positive** and **Neutral** performance, but weaker **Negative** performance due to class imbalance.
+
+---
+
+## Usage
+
+```python
+from transformers import pipeline
+
+clf = pipeline(
+    "text-classification",
+    model="Romeo777777/Llama_Sentiment_Classifier",
+    tokenizer="Romeo777777/Llama_Sentiment_Classifier",
+    return_all_scores=True,
+)
+
+print(clf("Bitcoin is pumping hard today!"))