Add dataset card README

README.md

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-license: cc
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+# EventX: A Multimodal Benchmark Linking Social Media Posts to Prediction Market Dynamics
+
+**Dataset: [https://huggingface.co/datasets/mlsys-io/EventXBench](https://huggingface.co/datasets/mlsys-io/EventXBench)**
+
+[![Dataset on HF](https://img.shields.io/badge/%F0%9F%A4%97%20Dataset-EventXBench-blue)](https://huggingface.co/datasets/mlsys-io/EventXBench)
+[![Paper](https://img.shields.io/badge/Paper-ACM%20MM%20'26-red)](https://doi.org/PLACEHOLDER)
+[![License: CC BY-NC 4.0](https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc/4.0/)
+
+**EventX** is a multimodal benchmark connecting 9M Twitter/X posts from 1,152 KOL accounts to 11,952 Polymarket prediction markets (2021--2026). It defines seven tasks across two tiers: **resolution** (human-annotated ground truth) and **forecast** (deterministic labels from post-publication tick data).
+
+## Quick Start
+
+```bash
+pip install -r requirements.txt
+
+from eventxbench import load_task
+train, test = load_task("t1")  # Returns pandas DataFrames
+
+# Run a baseline
+python baselines/t1/llm_baseline.py --provider openai --model gpt-4o --shots 0
+
+# Evaluate predictions
+python evaluation/evaluate.py --task t1 --predictions results/t1_predictions.jsonl
+```
+
+## Benchmark Overview
+
+| Task | Name | Tier | Output | Instances | Primary Metrics |
+|------|------|------|--------|-----------|-----------------|
+| T1 | Market Volume Prediction | Forecast | 3-class (`high`/`moderate`/`low`) | 305 | Macro-F1, `high`-class P@K |
+| T2 | Post-to-Market Linking | Resolution | Market ID or `none` | 815 | Accuracy@1, MRR |
+| T3 | Evidence Grading | Resolution | Ordinal 0--5 | 342,552 | QWK (kappa), macro-F1 |
+| T4 | Market Movement Prediction | Forecast | Direction x Magnitude | 4,803 | Dir-Acc, Mag-F1, Spearman rho |
+| T5 | Volume & Price Impact | Forecast | Continuous | 407 (268 clean) | Spearman rho (price_impact, volume_multiplier) |
+| T6 | Cross-Market Propagation | Forecast | 3-class | 4,006 | Macro-F1, MAE (onset lag) |
+| T7 | Impact Persistence (Decay) | Forecast | 3-class (`transient`/`sustained`/`reversal`) | 407 (268 clean) | Macro-F1 |
+
+## Tasks
+
+### T1: Conditional Market Volume Prediction
+Predict the final trading-volume percentile of a subsequently created market, given pre-market social signals.
+
+- **Labels**: `high` (>80th pctl), `moderate` (40th--80th), `low` (<40th)
+- **Input**: Tweet cluster features, event metadata, temporal features
+- **Metrics**: Macro-F1, `high`-class precision@K
+
+### T2: Post-to-Market Linking
+Given a tweet and a candidate set recalled by BGE-large-en-v1.5 / FAISS dense retrieval, identify which market the post addresses (or `none`).
+
+- **Input**: Tweet text + candidate market questions
+- **Metrics**: Accuracy@1, MRR, `none`-class F1
+
+### T3: Evidence Grading and Resolution Potential
+Assign an ordinal evidence grade (0--5) to each post-market pair.
+
+- **Grade scale**: 0 (`noise`), 1 (`commentary_reaction`), 2 (`speculation_rumor`), 3 (`indirect_report`), 4 (`strong_direct`), 5 (`resolving`)
+- **Metrics**: Quadratic-weighted kappa, `resolving`-class precision, macro-F1
+
+### T4: Market Movement Prediction
+Predict direction and magnitude of the YES-price change at 2-hour horizon after a tweet.
+
+- **Direction**: `up` (delta > 0.02), `down` (delta < -0.02), `flat` (otherwise)
+- **Magnitude**: `large` (>8%), `medium` (2--8%), `small` (<=2%)
+- **Metrics**: Direction accuracy, Magnitude macro-F1, Spearman rho on continuous delta curve
+- **Secondary horizons**: 30 min, 6 h
+
+### T5: Volume and Price Impact
+Predict two continuous targets per post: (i) `price_impact` (max absolute deviation from p0), (ii) `volume_multiplier` (total volume / 24h baseline).
+
+- **Targets**: `price_impact` (continuous), `volume_multiplier` (continuous)
+- **Metrics**: Spearman rho for each target
+- **Note**: T5 and T7 share the same underlying data (`task5+7/` in the codebase). T5 evaluates the continuous predictions; T7 evaluates the decay classification.
+
+### T7: Impact Persistence (Decay)
+Classify whether a tweet's initial market impact is transient, sustained, or reverses over time.
+
+- **Labels**: `transient` (|delta_2h| < 30% of |delta_15m|), `sustained` (same sign, larger), `reversal` (sign flips at 2h)
+- **Metrics**: Macro-F1
+- **Note**: Uses the same data as T5 but evaluates the `decay_class` field. In the codebase, uses `task5+7/` directories and `t7_` prefixes.
+
+### T6: Cross-Market Propagation
+Predict whether a tweet's market impact propagates to sibling markets within 2 hours. A sibling is deemed "moved" if |delta_p| > 1.5 sigma (rolling 24h stdev).
+
+- **Labels**: `no_effect`, `primary_mover` (primary market moves first, sibling follows), `propagated_signal` (sibling moves before or instead of primary)
+- **Metrics**: Macro-F1, MAE on onset lag (minutes)
+
+## Data
+
+### Hugging Face Dataset
+
+All data is hosted on Hugging Face:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("mlsys-io/EventXBench", "t1")
+
+# Or use our convenience loader
+from eventxbench import load_task
+train, test = load_task("t1")
+```
+
+See [`data/README.md`](data/README.md) for the full dataset card.
+
+### Data Files
+
+| File | Description | Size |
+|------|-------------|------|
+| `posts_no_text.jsonl` | Tweet IDs and metadata (text stripped for privacy) | ~9M rows |
+| `market_fundamental.json` | Market metadata (question, category, resolution) | 11,952 markets |
+| `market_ohlcv.json` | Price/volume time series (OHLCV) | 1.8 GB |
+| `t1_labels.jsonl` | T1 ground truth with train/test splits | 305 |
+| `t2_groundtruth.jsonl` | T2 post-market linking pairs | 815 |
+| `t3_graded.json` | T3 evidence grades (0--5) | 342,552 |
+| `t4_labels.jsonl` | T4 direction x magnitude labels | 4,803 |
+| `t5_labels.jsonl` | T5 price impact + volume multiplier | 407 |
+| `t6_labels.jsonl` | T6 cross-market propagation labels | 4,006 |
+| `t7_labels.jsonl` | T7 decay class labels (same data as T5) | 407 |
+
+### Privacy
+
+Tweet text is **not** included in the public release to comply with Twitter/X Terms of Service. The `posts_no_text.jsonl` file contains tweet IDs for rehydration via the Twitter API. Market data from Polymarket is fully included.
+
+## Baselines
+
+We provide three baseline families:
+
+### LLM Baselines
+Zero-shot and few-shot prompting:
+- GPT-4o (OpenAI)
+- Sonnet 4.5 (Anthropic)
+- Grok 4.1 (xAI)
+- Qwen 3.5 (local via vLLM, 4B and 27B)
+
+```bash
+# T1 zero-shot with GPT-4o
+python baselines/t1/llm_baseline.py --provider openai --model gpt-4o --shots 0
+
+# T4 three-shot with Claude
+python baselines/t4/llm_baseline.py --provider anthropic --model claude-sonnet-4-5-20250514 --shots 3
+
+# T2 contextual validation with an OpenAI-compatible Lumid model
+python baselines/t2/llm_baseline.py \
+  --provider openai \
+  --base-url https://lum.id/llm \
+  --api-key-env LUMID_API_KEY \
+  --model nvidia/Gemma-4-26B-A4B-NVFP4 \
+  --disable-thinking \
+  --split val \
+  --shots 0 \
+  --resume \
+  --output results/t2_gemma.val.0shot.jsonl
+```
+
+**Required env vars**: `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, `XAI_API_KEY`, `HF_TOKEN` (for Qwen)
+
+The T2 runner defaults to the frozen 2,500-row contextual-gold package, treats
+`NONE` as an explicit option, and writes Accuracy@1, MRR, NONE-class F1,
+bootstrap intervals, token usage, latency, input hashes, and prompt provenance.
+Run validation first. A frozen test run requires `--split test --allow-test`.
+Models recorded as Task 2 train-silver judges are automatically marked
+`diagnostic_excluded_silver_judge` and must not be reported as independent
+paper baselines.
+
+Lumid also accepts the Anthropic message format. Use `--provider anthropic`
+with the same gateway root and PAT environment variable. The provider flag
+describes the request format; the actual baseline model remains the model ID
+passed with `--model` and returned by Lumid's `/llm/v1/models` endpoint.
+
+### ML Baselines
+LightGBM classifiers with Bayesian hyperparameter tuning (Optuna):
+
+```bash
+python baselines/t1/lightgbm_baseline.py
+python baselines/t4/lightgbm_baseline.py
+```
+
+### Heuristic & Basic Baselines
+Majority class, random walk, graph heuristics, BM25 retrieval:
+
+```bash
+python baselines/t1/basic_baseline.py
+python baselines/t6/basic_baseline.py
+```
+
+## Evaluation
+
+```bash
+python evaluation/evaluate.py --task t1 --predictions results/t1_preds.jsonl
+python evaluation/evaluate.py --task t4 --predictions results/t4_preds.jsonl
+python evaluation/evaluate.py --task all --predictions-dir results/
+```
+
+See [`evaluation/README.md`](evaluation/README.md) for prediction format specs.
+
+## Repository Structure
+
+```
+EventXBench/
+├── README.md
+├── LEADERBOARD.md
+├── requirements.txt
+├── eventxbench/                  # Data loading utilities
+│   ├── __init__.py
+│   └── loader.py
+├── data/
+│   └── README.md                 # Hugging Face dataset card
+├── baselines/
+│   ├── t1/ ... t6/               # Per-task baselines (LLM, ML, basic)
+│   └── t7/                       # Impact Persistence (Decay) baselines
+├── evaluation/
+│   ├── evaluate.py               # Unified evaluation CLI
+│   ├── metrics.py                # Metric implementations
+│   └── README.md
+├── scripts/
+│   └── upload_to_hf.py           # Upload data to Hugging Face
+└── examples/
+    └── quickstart.py
+```
+
+## Leaderboard
+
+See [LEADERBOARD.md](LEADERBOARD.md) for current results. To submit, open a pull request.
+
+## License
+
+- **Code**: MIT License
+- **Data**: [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)
+- Tweet text excluded; use Twitter API for rehydration
+- Polymarket data included under fair use for research