Chess Challenge submission by Valbad

README.md

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+---
+library_name: transformers
+tags:
+- chess
+- llm-course
+- chess-challenge
+license: mit
+---
+
+# chess-better-valbad
+
+Chess model submitted to the LLM Course Chess Challenge.
+
+## Submission Info
+
+- **Submitted by**: [Valbad](https://huggingface.co/Valbad)
+- **Parameters**: 440,300
+- **Organization**: LLM-course
+
+## Model Details
+
+- **Architecture**: Chess Transformer (GPT-style)
+- **Vocab size**: 97
+- **Embedding dim**: 100
+- **Layers**: 4
+- **Heads**: 4

config.json

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+{
+  "architectures": [
+    "ChessForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "dropout": 0.1,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "chess_transformer",
+  "n_ctx": 256,
+  "n_embd": 100,
+  "n_head": 4,
+  "n_inner": 300,
+  "n_layer": 4,
+  "pad_token_id": 0,
+  "tie_weights": true,
+  "transformers_version": "4.57.3",
+  "vocab_size": 97
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0cd361c3d20274e1cd1458bebd874f6e7a6898e7592857e17d2015bbe40baa05
+size 1765576

special_tokens_map.json

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+{
+  "bos_token": "[BOS]",
+  "eos_token": "[EOS]",
+  "pad_token": "[PAD]",
+  "unk_token": "[UNK]"
+}

tokenizer.py

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+# """
+# Custom Chess Tokenizer for the Chess Challenge.
+
+# This tokenizer treats each move as a single token using the extended UCI notation
+# from the Lichess dataset (e.g., WPe2e4, BNg8f6).
+
+# The dataset format uses:
+# - W/B prefix for White/Black
+# - Piece letter: P=Pawn, N=Knight, B=Bishop, R=Rook, Q=Queen, K=King
+# - Source and destination squares (e.g., e2e4)
+# - Special suffixes: (x)=capture, (+)=check, (+*)=checkmate, (o)/(O)=castling
+# """
+# 
+# from __future__ import annotations
+
+# import json
+# import os
+# from pathlib import Path
+# from typing import Dict, List, Optional
+
+# from transformers import PreTrainedTokenizer
+
+
+# class ChessTokenizer(PreTrainedTokenizer):
+#     """
+#     A custom tokenizer for chess moves using extended UCI notation.
+    
+#     This tokenizer maps each possible chess move to a unique token ID.
+#     The vocabulary is built from the training dataset to ensure all moves
+#     encountered during training have a corresponding token.
+    
+#     Example:
+#         >>> tokenizer = ChessTokenizer()
+#         >>> tokenizer.encode("WPe2e4 BPe7e5")
+#         [1, 42, 87, 2]  # [BOS, e2e4, e7e5, EOS]
+#     """
+    
+#     model_input_names = ["input_ids", "attention_mask"]
+#     vocab_files_names = {"vocab_file": "vocab.json"}
+    
+#     # Special tokens
+#     PAD_TOKEN = "[PAD]"
+#     BOS_TOKEN = "[BOS]"
+#     EOS_TOKEN = "[EOS]"
+#     UNK_TOKEN = "[UNK]"
+    
+#     def __init__(
+#         self,
+#         vocab_file: Optional[str] = None,
+#         vocab: Optional[Dict[str, int]] = None,
+#         **kwargs,
+#     ):
+#         """
+#         Initialize the chess tokenizer.
+        
+#         Args:
+#             vocab_file: Path to a JSON file containing the vocabulary mapping.
+#             vocab: Dictionary mapping tokens to IDs (alternative to vocab_file).
+#             **kwargs: Additional arguments passed to PreTrainedTokenizer.
+#         """
+#         # Initialize special tokens
+#         self._pad_token = self.PAD_TOKEN
+#         self._bos_token = self.BOS_TOKEN
+#         self._eos_token = self.EOS_TOKEN
+#         self._unk_token = self.UNK_TOKEN
+
+#         # Remove any duplicate special-token entries passed through kwargs
+#         # to avoid "multiple values for keyword" errors when loading from disk.
+#         kwargs.pop("pad_token", None)
+#         kwargs.pop("bos_token", None)
+#         kwargs.pop("eos_token", None)
+#         kwargs.pop("unk_token", None)
+        
+#         # Load or create vocabulary
+#         if vocab is not None:
+#             self._vocab = vocab
+#         elif vocab_file is not None and os.path.exists(vocab_file):
+#             with open(vocab_file, "r", encoding="utf-8") as f:
+#                 self._vocab = json.load(f)
+#         else:
+#             # Create a minimal vocabulary with just special tokens
+#             # The full vocabulary should be built from the dataset
+#             self._vocab = self._create_default_vocab()
+        
+#         # Create reverse mapping
+#         self._ids_to_tokens = {v: k for k, v in self._vocab.items()}
+        
+#         # Call parent init AFTER setting up vocab
+#         super().__init__(
+#             pad_token=self._pad_token,
+#             bos_token=self._bos_token,
+#             eos_token=self._eos_token,
+#             unk_token=self._unk_token,
+#             **kwargs,
+#         )
+    
+#     def _create_default_vocab(self) -> Dict[str, int]:
+#         """
+#         Create a minimal default vocabulary with just special tokens.
+        
+#         For the full vocabulary, use `build_vocab_from_dataset()`.
+#         This minimal vocab is just a placeholder - you should build from data.
+#         """
+#         special_tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
+#         vocab = {token: idx for idx, token in enumerate(special_tokens)}
+#         return vocab
+    
+#     # @classmethod
+#     # def build_vocab_from_iterator(
+#     #     cls,
+#     #     iterator,
+#     #     min_frequency: int = 1,
+#     # ) -> "ChessTokenizer":
+#     #     """
+#     #     Build a tokenizer vocabulary from an iterator of game strings.
+        
+#     #     Args:
+#     #         iterator: An iterator yielding game strings (space-separated moves).
+#     #         min_frequency: Minimum frequency for a token to be included.
+        
+#     #     Returns:
+#     #         A ChessTokenizer with the built vocabulary.
+#     #     """
+#     #     from collections import Counter
+        
+#     #     token_counts = Counter()
+        
+#     #     for game in iterator:
+#     #         moves = game.strip().split()
+#     #         token_counts.update(moves)
+        
+#     #     # Filter by frequency
+#     #     tokens = [
+#     #         token for token, count in token_counts.items()
+#     #         if count >= min_frequency
+#     #     ]
+        
+#     #     # Sort for reproducibility
+#     #     tokens = sorted(tokens)
+        
+#     #     # Build vocabulary
+#     #     special_tokens = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
+#     #     vocab = {token: idx for idx, token in enumerate(special_tokens + tokens)}
+        
+#     #     return cls(vocab=vocab)
+
+#     @classmethod
+#     def build_vocab_from_iterator(
+#         cls,
+#         iterator,
+#         vocab_size: int = 1200,
+#         min_frequency: int = 1,
+#         ) -> "ChessTokenizer":
+#         """
+#         Build a tokenizer vocabulary from an iterator of game strings.
+
+#         - Controls final vocab size explicitly via vocab_size.
+#         - Keeps the most frequent move tokens (best coverage).
+#         - Uses min_frequency as a floor, but vocab_size is the main control.
+#         """
+#         from collections import Counter
+
+#         token_counts = Counter()
+#         for game in iterator:
+#             moves = game.strip().split()
+#             token_counts.update(moves)
+
+#         # Filter by min_frequency first
+#         items = [(tok, cnt) for tok, cnt in token_counts.items() if cnt >= min_frequency]
+
+#         # Sort by frequency desc, then token for determinism
+#         items.sort(key=lambda x: (-x[1], x[0]))
+
+#         special_tokens = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
+#         max_move_tokens = max(0, vocab_size - len(special_tokens))
+
+#         move_tokens = [tok for tok, _ in items[:max_move_tokens]]
+#         vocab = {token: idx for idx, token in enumerate(special_tokens + move_tokens)}
+
+#         return cls(vocab=vocab)
+
+    
+#     # @classmethod
+#     # def build_vocab_from_dataset(
+#     #     cls,
+#     #     dataset_name: str = "dlouapre/lichess_2025-01_1M",
+#     #     split: str = "train",
+#     #     column: str = "text",
+#     #     min_frequency: int = 500,
+#     #     max_samples: Optional[int] = 100000,
+#     # ) -> "ChessTokenizer":
+#     #     """
+#     #     Build a tokenizer vocabulary from a Hugging Face dataset.
+        
+#     #     Args:
+#     #         dataset_name: Name of the dataset on Hugging Face Hub.
+#     #         split: Dataset split to use.
+#     #         column: Column containing the game strings.
+#     #         min_frequency: Minimum frequency for a token to be included (default: 500).
+#     #         max_samples: Maximum number of samples to process (default: 100k).
+        
+#     #     Returns:
+#     #         A ChessTokenizer with the built vocabulary.
+#     #     """
+#     #     from datasets import load_dataset
+        
+#     #     dataset = load_dataset(dataset_name, split=split)
+        
+#     #     if max_samples is not None:
+#     #         dataset = dataset.select(range(min(max_samples, len(dataset))))
+        
+#     #     def game_iterator():
+#     #         for example in dataset:
+#     #             yield example[column]
+        
+#     #     return cls.build_vocab_from_iterator(game_iterator(), min_frequency=min_frequency)
+#     @classmethod
+#     def build_vocab_from_dataset(
+#         cls,
+#         dataset_name: str = "dlouapre/lichess_2025-01_1M",
+#         split: str = "train",
+#         column: str = "text",
+#         vocab_size: int = 1200,
+#         min_frequency: int = 1,
+#         max_samples: Optional[int] = 200000,
+#     ) -> "ChessTokenizer":
+#         """
+#         Build a tokenizer vocabulary from a Hugging Face dataset.
+
+#         Args:
+#             vocab_size: Final vocab size INCLUDING special tokens.
+#             min_frequency: Minimum count to consider a move (usually 1 is fine).
+#             max_samples: How many games to scan to build vocab.
+#         """
+#         from datasets import load_dataset
+
+#         dataset = load_dataset(dataset_name, split=split)
+
+#         # if max_samples is not None: # v0&1
+#         #     dataset = dataset.select(range(min(max_samples, len(dataset))))
+         
+#         if max_samples is not None: # v2
+#             n = min(max_samples, len(dataset))
+#             dataset = dataset.shuffle(seed=42).select(range(n))
+
+#         def game_iterator():
+#             for example in dataset:
+#                 yield example[column]
+
+#         return cls.build_vocab_from_iterator(
+#             game_iterator(),
+#             vocab_size=vocab_size,
+#             min_frequency=min_frequency,
+#         )
+    
+#     @property
+#     def vocab_size(self) -> int:
+#         """Return the size of the vocabulary."""
+#         return len(self._vocab)
+    
+#     def get_vocab(self) -> Dict[str, int]:
+#         """Return the vocabulary as a dictionary."""
+#         return dict(self._vocab)
+    
+#     def _tokenize(self, text: str) -> List[str]:
+#         """
+#         Tokenize a string of moves into a list of tokens.
+        
+#         Args:
+#             text: A string of space-separated moves.
+        
+#         Returns:
+#             List of move tokens.
+#         """
+#         return text.strip().split()
+    
+#     def _convert_token_to_id(self, token: str) -> int:
+#         """Convert a token to its ID."""
+#         return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN, 0))
+    
+#     def _convert_id_to_token(self, index: int) -> str:
+#         """Convert an ID to its token."""
+#         return self._ids_to_tokens.get(index, self.UNK_TOKEN)
+    
+#     def convert_tokens_to_string(self, tokens: List[str]) -> str:
+#         """Convert a list of tokens back to a string."""
+#         # Filter out special tokens for cleaner output
+#         special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
+#         return " ".join(t for t in tokens if t not in special)
+    
+#     def save_vocabulary(
+#         self,
+#         save_directory: str,
+#         filename_prefix: Optional[str] = None,
+#     ) -> tuple:
+#         """
+#         Save the vocabulary to a JSON file.
+        
+#         Args:
+#             save_directory: Directory to save the vocabulary.
+#             filename_prefix: Optional prefix for the filename.
+        
+#         Returns:
+#             Tuple containing the path to the saved vocabulary file.
+#         """
+#         if not os.path.isdir(save_directory):
+#             os.makedirs(save_directory, exist_ok=True)
+        
+#         vocab_file = os.path.join(
+#             save_directory,
+#             (filename_prefix + "-" if filename_prefix else "") + "vocab.json",
+#         )
+        
+#         with open(vocab_file, "w", encoding="utf-8") as f:
+#             json.dump(self._vocab, f, ensure_ascii=False, indent=2)
+        
+#         return (vocab_file,)
+        
+#     # def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+#     #     if token_ids_1 is not None:
+#     #         # Not expected here, but handle gracefully
+#     #         token_ids = token_ids_0 + token_ids_1
+#     #     else:
+#     #         token_ids = token_ids_0
+#     #     return [self.bos_token_id] + token_ids + [self.eos_token_id]
+
+#     # def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
+#     #     if already_has_special_tokens:
+#     #         return [1 if t in (self.pad_token_id, self.bos_token_id, self.eos_token_id, self.unk_token_id) else 0 for t in token_ids_0]
+#     #     if token_ids_1 is not None:
+#     #         token_ids = token_ids_0 + token_ids_1
+#     #     else:
+#     #         token_ids = token_ids_0
+#     #     return [1] + [0] * len(token_ids) + [1]
+
+# def count_vocab_from_dataset(
+#     dataset_name: str = "dlouapre/lichess_2025-01_1M",
+#     split: str = "train",
+#     column: str = "text",
+#     max_samples: Optional[int] = 10000,
+# ) -> Dict[str, int]:
+#     """
+#     Count token frequencies in a dataset (useful for vocabulary analysis).
+    
+#     Args:
+#         dataset_name: Name of the dataset on Hugging Face Hub.
+#         split: Dataset split to use.
+#         column: Column containing the game strings.
+#         max_samples: Maximum number of samples to process.
+    
+#     Returns:
+#         Dictionary mapping tokens to their frequencies.
+#     """
+#     from collections import Counter
+#     from datasets import load_dataset
+    
+#     dataset = load_dataset(dataset_name, split=split)
+    
+#     if max_samples is not None:
+#         dataset = dataset.select(range(min(max_samples, len(dataset))))
+    
+#     token_counts = Counter()
+    
+#     for example in dataset:
+#         moves = example[column].strip().split()
+#         token_counts.update(moves)
+    
+#     return dict(token_counts)
+
+"""
+Grammar-aware Chess Tokenizer for the Chess Challenge.
+
+Goal: maximize legal move extraction in evaluate.py which searches for
+two square patterns ([a-h][1-8]) in the generated text and takes the first two.
+
+Strategy:
+- Decompose each move into structured tokens:
+  - CP_<color><piece>   (e.g., CP_WP, CP_BN)
+  - SQ_<square>         (e.g., SQ_e2, SQ_e4)
+  - EV_<event>          (e.g., EV_NONE, EV_X, EV_PLUS, EV_MATE, EV_PROMO_Q, ...)
+  - SEP                 (end-of-move marker, decoded as a space)
+- Deterministic vocab: no dataset-dependent OOV -> UNK for rare full moves disappears.
+"""
+
+from __future__ import annotations
+
+import json
+import os
+import re
+from typing import Dict, List, Optional
+
+from transformers import PreTrainedTokenizer
+
+
+class ChessTokenizer(PreTrainedTokenizer):
+    model_input_names = ["input_ids", "attention_mask"]
+    vocab_files_names = {"vocab_file": "vocab.json"}
+
+    PAD_TOKEN = "[PAD]"
+    BOS_TOKEN = "[BOS]"
+    EOS_TOKEN = "[EOS]"
+    UNK_TOKEN = "[UNK]"
+    SEP_TOKEN = "[SEP]"  # end-of-move marker (decoded as a space)
+
+    _SQUARE_RE = re.compile(r"^[a-h][1-8]$") # positions are in the format xY where x is in [a-h], y in [1-8]
+
+    def __init__(
+        self,
+        vocab_file: Optional[str] = None,
+        vocab: Optional[Dict[str, int]] = None,
+        **kwargs,
+    ):
+        self._pad_token = self.PAD_TOKEN
+        self._bos_token = self.BOS_TOKEN
+        self._eos_token = self.EOS_TOKEN
+        self._unk_token = self.UNK_TOKEN
+        self._sep_token = self.SEP_TOKEN
+
+        kwargs.pop("pad_token", None)
+        kwargs.pop("bos_token", None)
+        kwargs.pop("eos_token", None)
+        kwargs.pop("unk_token", None)
+
+        if vocab is not None:
+            self._vocab = vocab
+        elif vocab_file is not None and os.path.exists(vocab_file):
+            with open(vocab_file, "r", encoding="utf-8") as f:
+                self._vocab = json.load(f)
+        else:
+            self._vocab = self._create_default_vocab()
+
+        self._ids_to_tokens = {v: k for k, v in self._vocab.items()}
+
+        super().__init__(
+            pad_token=self._pad_token,
+            bos_token=self._bos_token,
+            eos_token=self._eos_token,
+            unk_token=self._unk_token,
+            **kwargs,
+        )
+
+    #### Vocab
+    def _create_default_vocab(self) -> Dict[str, int]:
+        special = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN, self.SEP_TOKEN]
+
+        # Color+piece (12 tokens)
+        cp = [f"CP_{c}{p}" for c in "WB" for p in "PNBRQK"]
+
+        # Squares (64 tokens)
+        squares = [f"SQ_{f}{r}" for f in "abcdefgh" for r in "12345678"]
+
+        # Events: keep small & canonical (you can extend later)
+        events = [
+            "EV_NONE",
+            "EV_X",
+            "EV_PLUS",
+            "EV_MATE",
+            "EV_XPLUS",
+            "EV_XMATE",
+            "EV_O",    # kingside castle
+            "EV_OO",   # queenside castle
+            "EV_PROMO_N",
+            "EV_PROMO_B",
+            "EV_PROMO_R",
+            "EV_PROMO_Q",
+            "EV_XPROMO_N",
+            "EV_XPROMO_B",
+            "EV_XPROMO_R",
+            "EV_XPROMO_Q",
+        ]
+
+        vocab_list = special + cp + squares + events # this vocabulary has size 12 + 64 + 16 + 5 = 97 tokens
+        return {tok: i for i, tok in enumerate(vocab_list)}
+
+    @property
+    def vocab_size(self) -> int:
+        return len(self._vocab)
+
+    def get_vocab(self) -> Dict[str, int]:
+        return dict(self._vocab)
+
+    #### Core tokenization
+    def _tokenize(self, text: str) -> List[str]:
+        """
+        Input is a space-separated list of moves in extended UCI, e.g.
+        "WPe2e4 BPe7e5 ..."
+
+        Output is a sequence of structured tokens:
+        CP_WP SQ_e2 SQ_e4 EV_NONE [SEP] ...
+        """
+        moves = text.strip().split()
+        tokens: List[str] = []
+
+        for mv in moves:
+            toks = self._tokenize_one_move(mv)
+            tokens.extend(toks)
+            tokens.append(self.SEP_TOKEN)
+
+        return tokens
+
+    def _tokenize_one_move(self, mv: str) -> List[str]:
+        # Minimal sanity: needs at least "WPe2e4" length 6
+        if len(mv) < 6:
+            return [self.UNK_TOKEN]
+
+        color = mv[0]  # W/B
+        piece = mv[1]  # P/N/B/R/Q/K
+        from_sq = mv[2:4]
+        to_sq = mv[4:6]
+        suffix = mv[6:]  # can include capture/check/mate/castle/promo etc. => cf events tokens
+
+        cp_tok = f"CP_{color}{piece}"
+        from_tok = f"SQ_{from_sq}"
+        to_tok = f"SQ_{to_sq}"
+
+        if cp_tok not in self._vocab or from_tok not in self._vocab or to_tok not in self._vocab:
+            return [self.UNK_TOKEN]
+
+        ev_tok = self._event_token(piece, from_sq, to_sq, suffix)
+
+        return [cp_tok, from_tok, to_tok, ev_tok]
+
+    def _event_token(self, piece: str, from_sq: str, to_sq: str, suffix: str) -> str:
+        """
+        Canonicalize suffix into one of EV_* tokens.
+        Keep it simple: evaluator does not need these, but they help learning.
+        """
+        # Castling (dataset uses (o)/(O))
+        if "(o)" in suffix: # kingside
+            return "EV_O"
+        if "(O)" in suffix: # queenside
+            return "EV_OO"
+
+        capture = "(x" in suffix  # covers (x), (x+), (x+*), (x+) etc.
+        mate = "+*" in suffix
+        check = "(+)" in suffix or "(x+)" in suffix or "(+)" in suffix  # tolerant
+
+        promo = None
+        m = re.search(r"=([NBRQ])", suffix)
+        if m:
+            promo = m.group(1)
+
+        if promo is not None:
+            base = f"EV_PROMO_{promo}"
+            if capture:
+                base = f"EV_XPROMO_{promo}"
+            return base if base in self._vocab else "EV_NONE"
+
+        if capture and mate:
+            return "EV_XMATE"
+        if capture and check:
+            return "EV_XPLUS"
+        if capture:
+            return "EV_X"
+        if mate:
+            return "EV_MATE"
+        if check:
+            return "EV_PLUS"
+        return "EV_NONE"
+
+    #### Conversions
+    def _convert_token_to_id(self, token: str) -> int:
+        return self._vocab.get(token, self._vocab[self.UNK_TOKEN])
+
+    def _convert_id_to_token(self, index: int) -> str:
+        return self._ids_to_tokens.get(index, self.UNK_TOKEN)
+
+    def convert_tokens_to_string(self, tokens: List[str]) -> str:
+        """
+        Decode to a string that contains squares early and clearly.
+        We intentionally emit raw squares like "e2" "e4" separated by spaces,
+        so evaluate.py will reliably extract them.
+        """
+        out: List[str] = []
+        special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
+
+        for tok in tokens:
+            if tok in special:
+                continue
+            if tok == self.SEP_TOKEN:
+                out.append(" ")
+                continue
+            if tok.startswith("SQ_"):
+                out.append(tok[3:])      # "SQ_e2" -> "e2"
+                out.append(" ")
+                continue
+            if tok.startswith("CP_"):
+                # Optional: keep CP to help model conditioning; does not hurt extraction
+                out.append(tok[3:])      # "CP_WP" -> "WP"
+                out.append(" ")
+                continue
+            if tok.startswith("EV_"):
+                # Optional: keep events; ensure no squares are embedded here
+                out.append(tok[3:])      # "EV_X" -> "X"
+                out.append(" ")
+                continue
+            # fallback
+            out.append(tok)
+            out.append(" ")
+
+        return "".join(out).strip()
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple:
+        if not os.path.isdir(save_directory):
+            os.makedirs(save_directory, exist_ok=True)
+
+        vocab_file = os.path.join(
+            save_directory,
+            (filename_prefix + "-" if filename_prefix else "") + "vocab.json",
+        )
+        with open(vocab_file, "w", encoding="utf-8") as f:
+            json.dump(self._vocab, f, ensure_ascii=False, indent=2)
+        return (vocab_file,)
+
+    

tokenizer_config.json

@@ -0,0 +1,50 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[BOS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[EOS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenizer.ChessTokenizer",
+      null
+    ]
+  },
+  "bos_token": "[BOS]",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "[EOS]",
+  "extra_special_tokens": {},
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "tokenizer_class": "ChessTokenizer",
+  "unk_token": "[UNK]"
+}

vocab.json

@@ -0,0 +1,99 @@
+{
+  "[PAD]": 0,
+  "[BOS]": 1,
+  "[EOS]": 2,
+  "[UNK]": 3,
+  "[SEP]": 4,
+  "CP_WP": 5,
+  "CP_WN": 6,
+  "CP_WB": 7,
+  "CP_WR": 8,
+  "CP_WQ": 9,
+  "CP_WK": 10,
+  "CP_BP": 11,
+  "CP_BN": 12,
+  "CP_BB": 13,
+  "CP_BR": 14,
+  "CP_BQ": 15,
+  "CP_BK": 16,
+  "SQ_a1": 17,
+  "SQ_a2": 18,
+  "SQ_a3": 19,
+  "SQ_a4": 20,
+  "SQ_a5": 21,
+  "SQ_a6": 22,
+  "SQ_a7": 23,
+  "SQ_a8": 24,
+  "SQ_b1": 25,
+  "SQ_b2": 26,
+  "SQ_b3": 27,
+  "SQ_b4": 28,
+  "SQ_b5": 29,
+  "SQ_b6": 30,
+  "SQ_b7": 31,
+  "SQ_b8": 32,
+  "SQ_c1": 33,
+  "SQ_c2": 34,
+  "SQ_c3": 35,
+  "SQ_c4": 36,
+  "SQ_c5": 37,
+  "SQ_c6": 38,
+  "SQ_c7": 39,
+  "SQ_c8": 40,
+  "SQ_d1": 41,
+  "SQ_d2": 42,
+  "SQ_d3": 43,
+  "SQ_d4": 44,
+  "SQ_d5": 45,
+  "SQ_d6": 46,
+  "SQ_d7": 47,
+  "SQ_d8": 48,
+  "SQ_e1": 49,
+  "SQ_e2": 50,
+  "SQ_e3": 51,
+  "SQ_e4": 52,
+  "SQ_e5": 53,
+  "SQ_e6": 54,
+  "SQ_e7": 55,
+  "SQ_e8": 56,
+  "SQ_f1": 57,
+  "SQ_f2": 58,
+  "SQ_f3": 59,
+  "SQ_f4": 60,
+  "SQ_f5": 61,
+  "SQ_f6": 62,
+  "SQ_f7": 63,
+  "SQ_f8": 64,
+  "SQ_g1": 65,
+  "SQ_g2": 66,
+  "SQ_g3": 67,
+  "SQ_g4": 68,
+  "SQ_g5": 69,
+  "SQ_g6": 70,
+  "SQ_g7": 71,
+  "SQ_g8": 72,
+  "SQ_h1": 73,
+  "SQ_h2": 74,
+  "SQ_h3": 75,
+  "SQ_h4": 76,
+  "SQ_h5": 77,
+  "SQ_h6": 78,
+  "SQ_h7": 79,
+  "SQ_h8": 80,
+  "EV_NONE": 81,
+  "EV_X": 82,
+  "EV_PLUS": 83,
+  "EV_MATE": 84,
+  "EV_XPLUS": 85,
+  "EV_XMATE": 86,
+  "EV_O": 87,
+  "EV_OO": 88,
+  "EV_PROMO_N": 89,
+  "EV_PROMO_B": 90,
+  "EV_PROMO_R": 91,
+  "EV_PROMO_Q": 92,
+  "EV_XPROMO_N": 93,
+  "EV_XPROMO_B": 94,
+  "EV_XPROMO_R": 95,
+  "EV_XPROMO_Q": 96
+}