Upload tokenizer_module.py with huggingface_hub

tokenizer_module.py

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+"""BPE tokenizer training and loading."""
+
+import os
+from tokenizers import Tokenizer, models, pre_tokenizers, trainers
+
+import config
+
+
+def train_tokenizer(text_path: str, vocab_size: int = None) -> Tokenizer:
+    """Train a BPE tokenizer from a text file using whitespace tokenization."""
+    if vocab_size is None:
+        vocab_size = config.VOCAB_SIZE
+
+    tokenizer = Tokenizer(models.BPE())
+    tokenizer.pre_tokenizer = pre_tokenizers.Whitespace()
+
+    trainer = trainers.BpeTrainer(
+        vocab_size=vocab_size,
+        special_tokens=["[PAD]", "[BOS]", "[EOS]", "[UNK]", "[MASK]"],
+        min_frequency=2,
+    )
+
+    # Stream lines from file to save memory
+    def line_iterator():
+        with open(text_path, "r", encoding="utf-8") as f:
+            for line in f:
+                yield line
+
+    tokenizer.train_from_iterator(line_iterator(), trainer=trainer)
+
+    # NO post-processor — we add BOS/EOS manually in dataset/generate
+    tokenizer.enable_padding(length=config.MAX_SEQ_LEN, pad_id=tokenizer.token_to_id("[PAD]"))
+    tokenizer.enable_truncation(max_length=config.MAX_SEQ_LEN)
+
+    os.makedirs(config.DATA_DIR, exist_ok=True)
+    tokenizer.save(config.TOKENIZER_PATH)
+    print(f"Tokenizer saved: {config.TOKENIZER_PATH} | vocab={tokenizer.get_vocab_size()}")
+    return tokenizer
+
+
+def load_tokenizer() -> Tokenizer:
+    if not os.path.exists(config.TOKENIZER_PATH):
+        raise FileNotFoundError(f"Tokenizer not found at {config.TOKENIZER_PATH}")
+    return Tokenizer.from_file(config.TOKENIZER_PATH)
+
+
+if __name__ == "__main__":
+    tok = train_tokenizer(config.DATA_TEXT_PATH)
+    tok.no_padding()
+    tok.no_truncation()
+    enc = tok.encode("Привет! Как дела?")
+    print(f"Tokens: {enc.tokens}")
+    print(f"IDs:    {enc.ids}")
+    print(f"Decoded: {tok.decode(enc.ids)}")