Update README.md — 8300 games (scraper pull 2026-06-18)

README.md

@@ -1,77 +1,19 @@
----
-license: mit
-pretty_name: Hexo Human Corpus (encoding-free)
-task_categories:
-  - other
-tags:
-  - hex
-  - hex-tac-toe
-  - board-games
-  - game-records
-  - reinforcement-learning
-  - alphazero
-size_categories:
-  - 1K<n<10K
----
-
 # Hexo Human Corpus
 
-Encoding-free corpus of **6,902** decisive human *Hex Tac Toe* games — hexagonal
-grid, six-in-a-row to win (player 1 opens with 1 move, then both players play 2
-moves per turn; the board is theoretically infinite).
-
-Each line is one game as a **raw axial move list + outcome**. Nothing about any
-neural-network encoding is baked in — no planes, no fixed board size, no action
-space. Read it with the stdlib `json` module and build whatever representation
-you want.
-
-## Files
-
-| file | description |
-|------|-------------|
-| `hexo_human_corpus.jsonl` | the corpus — one game per line |
-| `SCHEMA.md` | full per-line schema + conventions |
-| `dataset_metadata.json` | provenance: counts, sha256, source filter |
-
-## Schema
-
-One JSON object per line:
-
-```json
-{"game_hash":"0f8c6bdfc55e7f6f","moves":[[0,0],[2,-2],[-3,3]],"winner":1,"source":"human","elo":[898,955]}
-```
-
-| field | type | meaning |
-|-------|------|---------|
-| `game_hash` | string (16 hex) | SHA-256 of the move sequence — stable content/dedup key |
-| `moves` | array of `[x, y]` | axial hex coords `(x,y)=(q,r)`, in play order |
-| `winner` | `1` or `-1` | `1` = first player (X) wins, `-1` = second player (O) |
-| `source` | string | `"human"` |
-| `elo` | `[int\|null, int\|null]` | `[elo_p1, elo_p2]` |
-
-**Conventions**
-
-- Axial hex coordinates `(x, y)`; the board is infinite so values can be
-  negative. The first player's forced opener is always `(0, 0)`.
-- Replay `moves` in order to reconstruct any board state.
-- Only decisive (six-in-a-row) games are included — there are **no draws**.
+Encoding-free corpus of **8300** decisive human *Hex Tac Toe*
+games (hexagonal grid, six-in-a-row to win). Each line is one game as a raw
+axial move list plus outcome — build any encoding you like on top.
 
-## Usage
+See `SCHEMA.md` for the per-line schema and `dataset_metadata.json` for full
+provenance (sha256, counts, source filter).
 
 ```python
 import json
-
-games = [json.loads(line) for line in open("hexo_human_corpus.jsonl")]
-print(len(games), "games")          # 6902
-g = games[0]
-print(g["moves"], g["winner"])      # [[0,0], [2,-2], ...]  1
+with open("hexo_human_corpus.jsonl") as f:
+    for line in f:
+        game = json.loads(line)
+        moves, winner = game["moves"], game["winner"]
 ```
 
-## Provenance
-
-Rated human games filtered to: rated, ≥20 moves, decisive by six-in-a-row.
-Per-game `elo` is each player's rating at game time. Games are anonymised
-(player ids dropped; only relative Elo retained). See `dataset_metadata.json`
-for the exact sha256 and counts.
-
-License: MIT.
+- Coordinates: axial hex `(x,y)`, infinite board, opener at `(0,0)`.
+- Winner: `1` = first player (X), `-1` = second player (O). No draws.