Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- sujet-ai/Sujet-Finance-Instruct-177k
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- finance
+- topic classification
+- sentiment analysis
+- yes no questions
+- llama3
+- llama3 8b
+- llama 3
+---
+<Gallery />
+
+# 🏦 Sujet Finance LLAMA 3 8B v0.1 🚀
+
+## 📊 A Specialized Financial Language Model Fine-Tuned on Sujet Finance Instruct-177k Dataset
+
+Welcome to the exciting world of **Sujet Finance LLAMA 3 8B v0.1** – your go-to language model for all things finance! 💰 This state-of-the-art model is a fine-tuned version of the powerful LLAMA 3 model, meticulously trained on the comprehensive Sujet Finance Instruct-177k dataset. 📈
+
+### 🎯 Fine-Tuning Focus
+
+In this initial fine-tuning iteration, we've focused on three key financial tasks:
+
+1. ✅❌ Yes/No Questions
+   - Description: This task involves answering financial questions that require a simple "yes" or "no" response.
+   - Class Distribution:
+     - Train Set: 5,265 "yes" examples, 5,302 "no" examples
+     - Eval Set: 1,340 "yes" examples, 1,303 "no" examples
+
+2. 📂 Topic Classification
+   - Description: The model classifies financial texts into specific finance-related categories such as company news, markets, earnings, and more.
+   - Class Distribution:
+     - Train Set: Balanced across 20 classes, with 29-40 examples per class
+     - Eval Set: Varies across classes, ranging from 4 to 15 examples per class
+
+3. 😊😐😡 Sentiment Analysis
+   - Description: This task involves analyzing financial texts to categorize sentiments as positive, negative, neutral, bearish, or bullish.
+   - Class Distribution:
+     - Train Set: 1,160 positive, 1,155 negative, 1,150 neutral, 1,133 bearish, and 1,185 bullish examples
+     - Eval Set: 281 positive, 286 negative, 291 neutral, 308 bearish, and 256 bullish examples
+
+Our model has been carefully trained to excel in these areas, providing accurate and insightful responses to your financial queries. 💡
+
+### 🎓 Training Methodology
+
+To ensure optimal performance, we've employed a balanced training approach. Our dataset preparation process strategically selects an equal number of examples from each subclass within the three focus tasks. This results in a well-rounded model that can handle a diverse range of financial questions and topics. 🧠
+
+The final balanced training dataset consists of 17,036 examples, while the evaluation dataset contains 4,259 examples.
+
+### 🔧 Model Specifications
+
+- Base Model: LLAMA 3 8B 🦙
+- Fine-Tuning Technique: LoRA (Low-Rank Adaptation)
+  - r = 16
+  - alpha = 32
+- Learning Rate: 2e-4 📈
+- Weight Decay: 0.01 🏋️‍♂️
+- Epochs: 1 🔄
+- Quantization: float16 for VLLM 🗜️
+
+### 📊 Evaluation Results
+
+We've put our model to the test, comparing its performance against the base LLAMA 3 model on our evaluation dataset. The results are impressive! 🏆
+
+We consider a response correct if the true answer appears within the first 10 words generated by the model. This strict criterion ensures that our model not only provides accurate answers but also prioritizes the most relevant information. 🎯