Update README.md

README.md

@@ -1,40 +1,94 @@
 ---
-license: mit
-task_categories:
-- time-series-forecasting
-- text-classification
 language:
-- ko
-size_categories:
-- 1K<n<10K
+  - ko
+pretty_name: "KRX Investment Warning Prediction Dataset (OHLCV + Korean News)"
+tags:
+  - finance
+  - krx
+  - korea
+  - time-series
+  - ohlcv
+  - news
+  - multimodal
+  - anomaly-detection
+  - binary-classification
+task_categories:
+  - text-classification
+  - time-series-forecasting
+task_ids:
+  - binary-classification
+license: mit
 ---
 
-# TIPS Raw Test Dataset
+# KRX Investment Warning Prediction Dataset (OHLCV + Korean News)
+
+## Dataset Summary
+
+This dataset is a test dataset for predicting **Investment Warning (투자주의 / 주의 종목)** designations in the Korean stock market (KRX).  
+It contains **raw daily OHLCV** price data and **Korean news text** (title + body), designed for **multimodal anomaly detection / binary classification**.
+
+**Important:** No technical indicators are included, and no normalization/scaling is applied.
+
+- **Date range:** 2025-07-01 ~ 2025-09-30
+- **Prediction horizon:** whether a stock will be designated as an investment warning **within the next 1 trading day**
+
+## Task
+
+Binary classification:
+
+- **Label 0:** Normal trading (no investment warning designation within the next 1 trading day)
+- **Label 1:** Investment warning designation (within the next 1 trading day)
+
+### Label Alignment
+
+For each `(ticker, date=t)`, set `label=1` if the stock is designated as an investment warning on `t+1` (the next trading day).
+
+## Data Sources
+
+| Source | Description |
+|------|-------------|
+| **Stock Prices** | Daily OHLCV data for KRX listed stocks |
+| **Investment Warning** | KRX investment warning designation history (labels) |
+| **News** | Korean news articles per stock (title + body) |
+
+## Dataset Format
+
+This dataset is structured to be used directly with Hugging Face `datasets`, and consists of **three columns**:
+
+- **`labels`**: Binary label (`0` or `1`)
+- **`time_series`**: Price time-series information (OHLCV)
+- **`texts`**: Korean news text mapped to the corresponding stock (title + body)
+
+> The exact internal structure of `time_series` and `texts` (e.g., list/dict formats, sequence length, date ordering) follows the dataset schema.  
+> In general, `time_series` is provided as a fixed-length historical window, and `texts` contains news from the same date (or window period), either concatenated or stored as a list.
+
+### Example (Conceptual)
 
-## Dataset Description
+- `labels`: `0` or `1`
+- `time_series`: `[{date, open, high, low, close, volume}, ...]` or `[[open, high, low, close, volume], ...]`
+- `texts`: `"title ... body ..."` or `["article1 ...", "article2 ..."]`
 
-Raw OHLCV test data for multimodal stock anomaly prediction.
-No technical indicators, no normalization/scaling applied.
+## Feature Details
 
-### Key Configuration
+### Price (OHLCV) — `time_series`
 
-- **Sequence Length**: 10
-- **Features**: ['open', 'high', 'low', 'close', 'volume'] (raw values)
-- **Test Ratio**: 30% (most recent data)
-- **Undersampling**: Majority class reduced to 10%
-- **Label Distribution**: {0: 13116, 1: 44}
-- **Total Samples**: 13160
+- OHLCV is provided as **raw daily bars**.
+- **No technical indicators** (e.g., RSI, MACD) are included.
+- **No normalization/scaling** is applied.
+- **Currency unit:** KRW
+- **Volume:** number of shares (not value)
 
-### Data Fields
+### News — `texts`
 
-- `labels`: int - Binary label (0: normal, 1: anomaly)
-- `time_series`: array [10, 5] - Raw OHLCV values
-- `texts`: string - Korean news text
+- News is mapped to tickers via an **exact ticker-code mapping**.
+- **Deduplication** has been applied.
+- Each news item includes **title + body** (stored as a single string or list depending on schema).
 
-### Usage
+## Recommended Metrics
 
-```python
-from datasets import load_dataset
+Because investment warning events are likely to be rare (class imbalance), the following metrics are recommended:
 
-dataset = load_dataset("k-datasoft/Multimodal-test-dataset")
-```
+- ROC-AUC, PR-AUC
+- F1 (positive class), precision/recall
+- Precision/recall at Top-k (useful for practical detection scenarios)
+- (Optional) probability calibration