Expand README with comprehensive project documentation

Add detailed project documentation including tech stack, ABSA task definition, project structure, setup instructions, coding conventions, and current phase. Establishes project scaffolding for Week 1.

Note: .DS_Store should be added to .gitignore to prevent future commits of macOS system files.

README.md

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-# multilingual-absa
-Aspect-level sentiment analysis on multilingual product reviews — English, Hindi & Hinglish. Fine-tuned XLM-RoBERTa + IndicBERT, ONNX inference, FastAPI backend, React dashboard.
+# Multilingual-Absa
+
+Aspect-Based Sentiment Analysis (ABSA) on multilingual product reviews. Supports English, Hindi, and Hinglish (code-mixed).
+
+## Overview
+Aspect-level sentiment analysis on multilingual product reviews. This project fine-tunes XLM-RoBERTa and IndicBERT models, exports them to ONNX for fast inference, and serves them via a FastAPI backend and a React dashboard.
+
+## Tech Stack
+- **Model:** XLM-RoBERTa (primary), IndicBERT (Hindi), exported to ONNX
+- **Fine-tuning:** HuggingFace Transformers + PEFT/QLoRA
+- **Backend:** FastAPI + Celery + Redis + PostgreSQL
+- **Frontend:** React + Vite + Recharts + TailwindCSS
+- **MLOps:** MLflow, DVC, Evidently AI, Prometheus + Grafana
+- **Deploy:** Docker + Railway (API), Vercel (frontend), HuggingFace Hub (models)
+
+## ABSA Task Definition
+- **Stage 1:** Aspect term extraction (token classification, BIO tagging)
+- **Stage 2:** Per-aspect sentiment classification (positive / negative / neutral / conflict)
+- Both stages compiled into a single ONNX graph for efficient serving.
+
+## Project Structure
+```text
+multilingual-absa/
+├── data/               # Raw + processed datasets (DVC tracked)
+├── notebooks/          # EDA, training experiments
+├── src/
+│   ├── data/           # Preprocessing, language detection, tokenization
+│   ├── models/         # Fine-tuning scripts, ONNX export
+│   ├── evaluation/     # Metrics, confusion matrix, cross-lingual eval
+│   └── utils/          # Shared utilities
+├── api/                # FastAPI app, Celery tasks, DB models
+├── dashboard/          # React frontend
+├── docker/             # Dockerfiles, docker-compose
+└── mlflow/             # MLflow tracking config
+```
+
+## Setup & Installation
+```bash
+# Clone the repository
+git clone https://github.com/your-org/multilingual-absa.git
+cd multilingual-absa
+
+# Install Python dependencies
+pip install -r requirements.txt
+
+# Pull DVC tracked data
+dvc pull
+```
+
+## Coding Conventions
+- Python 3.11+, type hints everywhere, Pydantic v2 for API schemas
+- All training runs logged to MLflow with params, metrics, and artifacts
+- Dataset versions tracked with DVC
+- Macro-F1 is the primary evaluation metric (not accuracy)
+- ONNX export required before any model goes to the API
+
+## Current Phase
+**Week 1** — Project scaffold, data collection, EDA