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""" |
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Custom Chess Tokenizer V3 for the Chess Challenge. |
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Enhanced version with additional chess-specific tokens for: |
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- Castling moves (O-O, O-O-O) |
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- Check/checkmate indicators (+, #) |
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- Capture indicator (x) |
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- Turn indicators ([WHITE], [BLACK]) |
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This provides richer context while keeping vocabulary minimal (81 tokens total). |
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""" |
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from __future__ import annotations |
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import json |
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import os |
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from pathlib import Path |
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from typing import Dict, List, Optional |
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import re |
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from transformers import PreTrainedTokenizer |
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class ChessTokenizer(PreTrainedTokenizer): |
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""" |
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Enhanced chess tokenizer with special chess notation tokens. |
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Vocabulary (79 tokens): |
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- 4 special tokens: [PAD], [BOS], [EOS], [UNK] |
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- 64 square tokens: a1-h8 |
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- 4 promotion tokens: q, r, b, n |
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- 2 castling tokens: O-O, O-O-O |
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- 3 modifier tokens: +, #, x (check, checkmate, capture) |
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- 2 turn tokens: [WHITE], [BLACK] |
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""" |
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model_input_names = ["input_ids", "attention_mask"] |
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vocab_files_names = {"vocab_file": "vocab.json"} |
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PAD_TOKEN = "[PAD]" |
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BOS_TOKEN = "[BOS]" |
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EOS_TOKEN = "[EOS]" |
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UNK_TOKEN = "[UNK]" |
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WHITE_TOKEN = "[WHITE]" |
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BLACK_TOKEN = "[BLACK]" |
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def __init__( |
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self, |
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vocab_file: Optional[str] = None, |
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vocab: Optional[Dict[str, int]] = None, |
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**kwargs, |
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): |
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self._pad_token = self.PAD_TOKEN |
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self._bos_token = self.BOS_TOKEN |
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self._eos_token = self.EOS_TOKEN |
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self._unk_token = self.UNK_TOKEN |
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kwargs.pop("pad_token", None) |
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kwargs.pop("bos_token", None) |
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kwargs.pop("eos_token", None) |
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kwargs.pop("unk_token", None) |
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self.token_pattern = re.compile( |
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r'O-O-O|O-O|' |
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r'\[WHITE\]|\[BLACK\]|' |
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r'[a-h][1-8]|' |
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r'[qrbn]|' |
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r'[+#x]' |
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) |
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if vocab is not None: |
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self._vocab = vocab |
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elif vocab_file is not None and os.path.exists(vocab_file): |
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with open(vocab_file, "r", encoding="utf-8") as f: |
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self._vocab = json.load(f) |
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else: |
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self._vocab = self._create_default_vocab() |
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self._ids_to_tokens = {v: k for k, v in self._vocab.items()} |
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super().__init__( |
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pad_token=self._pad_token, |
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bos_token=self._bos_token, |
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eos_token=self._eos_token, |
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unk_token=self._unk_token, |
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**kwargs, |
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) |
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def _create_default_vocab(self) -> Dict[str, int]: |
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""" |
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Create the complete vocabulary with all chess-specific tokens. |
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Total: 79 tokens |
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""" |
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vocab = {} |
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idx = 0 |
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for token in [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]: |
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vocab[token] = idx |
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idx += 1 |
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for f in 'abcdefgh': |
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for r in '12345678': |
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vocab[f"{f}{r}"] = idx |
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idx += 1 |
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for p in ['q', 'r', 'b', 'n']: |
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vocab[p] = idx |
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idx += 1 |
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vocab["O-O"] = idx |
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idx += 1 |
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vocab["O-O-O"] = idx |
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idx += 1 |
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vocab["+"] = idx |
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idx += 1 |
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vocab["#"] = idx |
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idx += 1 |
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vocab["x"] = idx |
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idx += 1 |
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vocab[self.WHITE_TOKEN] = idx |
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idx += 1 |
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vocab[self.BLACK_TOKEN] = idx |
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idx += 1 |
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return vocab |
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def _tokenize(self, text: str) -> List[str]: |
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""" |
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Enhanced tokenization with preprocessing for common chess notation variants. |
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Handles: |
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- Lichess format: (Q) → q, (x) → x, (+) → +, (#) → # |
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- Standard notation: keeps O-O, O-O-O, +, #, x as-is |
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- Extracts squares, promotions, castling, and modifiers |
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""" |
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text = (text.replace("(Q)", "q") |
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.replace("(R)", "r") |
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.replace("(B)", "b") |
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.replace("(N)", "n") |
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.replace("(x)", "x") |
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.replace("(+)", "+") |
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.replace("(#)", "#") |
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.replace("(+*)", "#") |
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.replace("(o)", "O-O") |
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.replace("(O)", "O-O-O")) |
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return self.token_pattern.findall(text) |
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def _convert_token_to_id(self, token: str) -> int: |
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"""Convert a token to its ID.""" |
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return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN, 0)) |
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def _convert_id_to_token(self, index: int) -> str: |
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"""Convert an ID to its token.""" |
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return self._ids_to_tokens.get(index, self.UNK_TOKEN) |
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def convert_tokens_to_string(self, tokens: List[str]) -> str: |
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""" |
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Reconstructs chess moves in standard UCI format with modifiers. |
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Intelligently groups tokens: |
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- Combines squares into moves: e2, e4 → e2e4 |
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- Attaches promotions: a7, a8, q → a7a8q |
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- Keeps modifiers separate: e2e4, x, + → e2e4x+ |
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- Preserves castling and turn indicators |
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""" |
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special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN} |
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clean_tokens = [t for t in tokens if t not in special] |
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output = [] |
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modifiers = {'+', '#', 'x'} |
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promotions = {'q', 'r', 'b', 'n'} |
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for token in clean_tokens: |
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if token in ["O-O", "O-O-O", self.WHITE_TOKEN, self.BLACK_TOKEN]: |
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output.append(token) |
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elif token in promotions and output and len(output[-1]) == 4: |
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output[-1] += token |
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elif token in modifiers and output: |
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output[-1] += token |
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elif len(token) == 2 and token[0] in 'abcdefgh': |
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if output and len(output[-1]) == 2 and output[-1][0] in 'abcdefgh': |
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output[-1] += token |
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else: |
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output.append(token) |
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else: |
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output.append(token) |
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return " ".join(output) |
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def add_turn_indicators(self, text: str, add_white_indicator: bool = True) -> str: |
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""" |
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Add turn indicators to help the model understand whose turn it is. |
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Args: |
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text: Game string (space-separated moves) |
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add_white_indicator: If True, add [WHITE] at start (white moves first) |
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Returns: |
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Game string with turn indicators |
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""" |
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moves = text.strip().split() |
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result = [] |
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is_white = add_white_indicator |
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for move in moves: |
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turn_token = self.WHITE_TOKEN if is_white else self.BLACK_TOKEN |
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result.append(turn_token) |
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result.append(move) |
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is_white = not is_white |
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return " ".join(result) |
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def save_vocabulary( |
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self, |
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save_directory: str, |
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filename_prefix: Optional[str] = None, |
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) -> tuple: |
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"""Save the vocabulary to a JSON file.""" |
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if not os.path.isdir(save_directory): |
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os.makedirs(save_directory, exist_ok=True) |
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vocab_file = os.path.join( |
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save_directory, |
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(filename_prefix + "-" if filename_prefix else "") + "vocab.json", |
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) |
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with open(vocab_file, "w", encoding="utf-8") as f: |
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json.dump(self._vocab, f, ensure_ascii=False, indent=2) |
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return (vocab_file,) |
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@classmethod |
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def build_vocab_from_iterator(cls, iterator, min_frequency=1): |
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"""Returns tokenizer with fixed vocabulary (doesn't depend on data).""" |
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return cls() |
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@classmethod |
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def build_vocab_from_dataset(cls, **kwargs): |
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"""Returns tokenizer with fixed vocabulary (doesn't depend on data).""" |
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return cls() |
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@property |
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def vocab_size(self) -> int: |
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"""Return the size of the vocabulary (79 tokens).""" |
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return len(self._vocab) |
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def get_vocab(self) -> Dict[str, int]: |
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"""Return the vocabulary as a dictionary.""" |
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return dict(self._vocab) |
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