Upload BPE Tokenizer Bahasa Indonesia

README.md

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+---
+language: id
+tags:
+  - tokenizer
+  - bpe
+  - bahasa-indonesia
+  - indonesian
+  - nlp
+license: mit
+library_name: transformers
+---
+
+# BPE Tokenizer - Bahasa Indonesia 🇮🇩
+
+A Byte Pair Encoding (BPE) tokenizer built **from scratch** for Bahasa Indonesia.
+
+## Overview
+
+This tokenizer was built entirely from scratch (no libraries like SentencePiece or HuggingFace Tokenizers) to demonstrate the BPE algorithm and provide a tokenizer optimized for Indonesian text.
+
+### Key Features
+- **Built from scratch** — Pure Python BPE implementation
+- **Optimized for Indonesian** — Trained on diverse Indonesian text data
+- **Lightweight** — Small vocabulary, fast encoding/decoding
+- **HuggingFace compatible** — Standard file format for easy integration
+
+## Usage
+
+```python
+from bpe_tokenizer import BPETokenizer
+
+# Load tokenizer
+tokenizer = BPETokenizer.from_pretrained("./")
+
+# Encode text
+text = "Saya suka makan nasi goreng di Jakarta"
+token_ids = tokenizer.encode(text)
+print(f"Token IDs: {token_ids}")
+
+# Decode back
+decoded = tokenizer.decode(token_ids)
+print(f"Decoded: {decoded}")
+
+# Get tokens (string form)
+tokens = tokenizer.tokenize(text)
+print(f"Tokens: {tokens}")
+```
+
+## Training Details
+
+- **Algorithm**: Byte Pair Encoding (BPE)
+- **Training data**: Curated Indonesian text corpus covering technology, culture, economy, daily life, politics, education, health, and sports
+- **Vocabulary size**: ~4,000 tokens
+- **Special tokens**: `<PAD>`, `<UNK>`, `<BOS>`, `<EOS>`
+
+## Files
+
+| File | Description |
+|------|-------------|
+| `vocab.json` | Token-to-ID mapping |
+| `merges.txt` | BPE merge rules |
+| `tokenizer.json` | HuggingFace format tokenizer |
+| `tokenizer_config.json` | Tokenizer configuration |
+| `special_tokens_map.json` | Special token definitions |
+| `bpe_tokenizer.py` | Source code (standalone, no dependencies) |
+
+## How BPE Works
+
+1. Start with individual characters as tokens
+2. Count frequency of adjacent token pairs in the corpus
+3. Merge the most frequent pair into a new token
+4. Repeat until reaching target vocabulary size
+
+This produces subword tokens that efficiently represent the language — common words become single tokens, while rare words are split into meaningful subparts.
+
+## Example
+
+```
+Input:  "saya makan nasi goreng"
+Tokens: ["saya", " makan", " nasi", " goreng"]  (or subword splits)
+IDs:    [234, 567, 891, 123]
+```
+
+## Limitations
+
+- Trained on a curated but limited corpus (~150 texts)
+- Best suited as a learning resource and proof of concept
+- For production use, consider training on a larger corpus (Wikipedia, Common Crawl, etc.)
+
+## Author
+
+Built by **Jekardah AI Lab** 🇮🇩
+
+## License
+
+MIT License

bpe_tokenizer.py

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+"""
+BPE (Byte Pair Encoding) Tokenizer - Built from Scratch
+Bahasa Indonesia Tokenizer untuk Hugging Face
+
+Author: Jekardah AI Lab
+"""
+
+import json
+import re
+import os
+from collections import Counter, defaultdict
+from typing import List, Dict, Tuple, Optional
+
+
+class BPETokenizer:
+    """
+    Byte Pair Encoding Tokenizer built from scratch.
+    Learns subword units from raw text data without requiring any dictionary.
+    """
+
+    def __init__(self, vocab_size: int = 32000, do_lower_case: bool = True):
+        self.vocab_size = vocab_size
+        self.do_lower_case = do_lower_case
+        self.vocab = {}           # token -> id
+        self.inverse_vocab = {}   # id -> token
+        self.merges = []          # list of (pair_a, pair_b) merge rules
+        self._merge_priority = {} # (pair) -> priority index for fast lookup
+        self.pattern = re.compile(
+            r"""'nya|'kan|'lah|'kah|'pun| ?\w+| ?\d+| ?[^\s\w\d]+|\s+(?!\S)|\s+"""
+        )
+
+        # Special tokens
+        self.special_tokens = {
+            "<PAD>": 0,
+            "<UNK>": 1,
+            "<BOS>": 2,
+            "<EOS>": 3,
+        }
+
+    def _get_pairs(self, word: List[str]) -> Counter:
+        """Get frequency of adjacent pairs in a word."""
+        pairs = Counter()
+        for i in range(len(word) - 1):
+            pairs[(word[i], word[i + 1])] += 1
+        return pairs
+
+    def _get_corpus_pairs(self, corpus: Dict[tuple, int]) -> Counter:
+        """Get frequency of all adjacent pairs across the entire corpus."""
+        pairs = Counter()
+        for word, freq in corpus.items():
+            for i in range(len(word) - 1):
+                pairs[(word[i], word[i + 1])] += freq
+        return pairs
+
+    def _merge_pair(self, pair: Tuple[str, str], corpus: Dict[tuple, int]) -> Dict[tuple, int]:
+        """Merge all occurrences of a pair in the corpus."""
+        new_corpus = {}
+        bigram = pair
+        for word, freq in corpus.items():
+            new_word = []
+            i = 0
+            while i < len(word):
+                if i < len(word) - 1 and word[i] == bigram[0] and word[i + 1] == bigram[1]:
+                    new_word.append(bigram[0] + bigram[1])
+                    i += 2
+                else:
+                    new_word.append(word[i])
+                    i += 1
+            new_corpus[tuple(new_word)] = freq
+        return new_corpus
+
+    def _pre_tokenize(self, text: str) -> List[str]:
+        """Split text into initial words/chunks."""
+        return self.pattern.findall(text)
+
+    def train(self, texts: List[str], min_frequency: int = 2, verbose: bool = True):
+        """
+        Train BPE tokenizer on a list of texts.
+
+        Args:
+            texts: List of training text strings
+            min_frequency: Minimum pair frequency to consider for merging
+            verbose: Print progress during training
+        """
+        if verbose:
+            print("=" * 60)
+            print("🚀 Training BPE Tokenizer")
+            print(f"   Target vocab size: {self.vocab_size}")
+            print(f"   Training texts: {len(texts)}")
+            print("=" * 60)
+
+        # Step 1: Pre-tokenize and build initial corpus
+        if verbose:
+            print("\n📝 Step 1: Pre-tokenizing text...")
+
+        word_freqs = Counter()
+        for text in texts:
+            text_input = text.lower() if self.do_lower_case else text
+            words = self._pre_tokenize(text_input)
+            for word in words:
+                word_freqs[word] += 1
+
+        if verbose:
+            print(f"   Found {len(word_freqs)} unique words")
+
+        # Step 2: Initialize corpus as character-level splits
+        if verbose:
+            print("\n🔤 Step 2: Initializing character-level tokens...")
+
+        corpus = {}
+        for word, freq in word_freqs.items():
+            chars = tuple(list(word))
+            corpus[chars] = freq
+
+        # Build initial character vocabulary
+        char_vocab = set()
+        for word in corpus.keys():
+            for char in word:
+                char_vocab.add(char)
+
+        if verbose:
+            print(f"   Initial character vocab: {len(char_vocab)} characters")
+
+        # Step 3: Iteratively merge most frequent pairs
+        if verbose:
+            print(f"\n🔗 Step 3: Learning merges (target: {self.vocab_size} tokens)...")
+
+        num_merges = self.vocab_size - len(char_vocab) - len(self.special_tokens)
+        self.merges = []
+
+        for i in range(num_merges):
+            pairs = self._get_corpus_pairs(corpus)
+            if not pairs:
+                if verbose:
+                    print(f"   No more pairs to merge at step {i}")
+                break
+
+            best_pair = pairs.most_common(1)[0]
+            if best_pair[1] < min_frequency:
+                if verbose:
+                    print(f"   Stopping at step {i}: min frequency {min_frequency} reached")
+                break
+
+            pair = best_pair[0]
+            self.merges.append(pair)
+            corpus = self._merge_pair(pair, corpus)
+
+            if verbose and (i + 1) % 500 == 0:
+                merged_token = pair[0] + pair[1]
+                print(f"   Merge {i + 1}/{num_merges}: '{pair[0]}' + '{pair[1]}' → '{merged_token}' (freq: {best_pair[1]})")
+
+        if verbose:
+            print(f"   Total merges learned: {len(self.merges)}")
+
+        # Step 4: Build final vocabulary
+        if verbose:
+            print("\n📚 Step 4: Building final vocabulary...")
+
+        self.vocab = dict(self.special_tokens)
+        idx = len(self.special_tokens)
+
+        # Add individual characters
+        for char in sorted(char_vocab):
+            if char not in self.vocab:
+                self.vocab[char] = idx
+                idx += 1
+
+        # Add merged tokens
+        for pair in self.merges:
+            merged = pair[0] + pair[1]
+            if merged not in self.vocab:
+                self.vocab[merged] = idx
+                idx += 1
+
+        self.inverse_vocab = {v: k for k, v in self.vocab.items()}
+        self._merge_priority = {pair: i for i, pair in enumerate(self.merges)}
+
+        if verbose:
+            print(f"   Final vocab size: {len(self.vocab)}")
+            print("\n✅ Training complete!")
+            print("=" * 60)
+
+    def _apply_merges(self, tokens: List[str]) -> List[str]:
+        """Apply learned merge rules to a list of tokens using greedy-by-priority."""
+        while len(tokens) >= 2:
+            # Find the adjacent pair with the highest priority (lowest index)
+            best_pair = None
+            best_rank = float('inf')
+            for i in range(len(tokens) - 1):
+                pair = (tokens[i], tokens[i + 1])
+                rank = self._merge_priority.get(pair, float('inf'))
+                if rank < best_rank:
+                    best_rank = rank
+                    best_pair = pair
+            if best_pair is None or best_rank == float('inf'):
+                break
+            # Merge all occurrences of best_pair
+            new_tokens = []
+            i = 0
+            while i < len(tokens):
+                if i < len(tokens) - 1 and tokens[i] == best_pair[0] and tokens[i + 1] == best_pair[1]:
+                    new_tokens.append(best_pair[0] + best_pair[1])
+                    i += 2
+                else:
+                    new_tokens.append(tokens[i])
+                    i += 1
+            tokens = new_tokens
+        return tokens
+
+    def encode(self, text: str) -> List[int]:
+        """
+        Encode text to token IDs.
+
+        Args:
+            text: Input text string
+
+        Returns:
+            List of token IDs
+        """
+        text_input = text.lower() if self.do_lower_case else text
+        words = self._pre_tokenize(text_input)
+        ids = []
+
+        for word in words:
+            chars = list(word)
+            tokens = self._apply_merges(chars)
+            for token in tokens:
+                if token in self.vocab:
+                    ids.append(self.vocab[token])
+                else:
+                    ids.append(self.special_tokens["<UNK>"])
+
+        return ids
+
+    def decode(self, ids: List[int]) -> str:
+        """
+        Decode token IDs back to text.
+
+        Args:
+            ids: List of token IDs
+
+        Returns:
+            Decoded text string
+        """
+        tokens = []
+        for token_id in ids:
+            if token_id in self.inverse_vocab:
+                tokens.append(self.inverse_vocab[token_id])
+            else:
+                tokens.append("<UNK>")
+        return "".join(tokens)
+
+    def tokenize(self, text: str) -> List[str]:
+        """
+        Tokenize text into subword tokens (string form).
+
+        Args:
+            text: Input text string
+
+        Returns:
+            List of token strings
+        """
+        text_input = text.lower() if self.do_lower_case else text
+        words = self._pre_tokenize(text_input)
+        all_tokens = []
+
+        for word in words:
+            chars = list(word)
+            tokens = self._apply_merges(chars)
+            all_tokens.extend(tokens)
+
+        return all_tokens
+
+    def save(self, directory: str):
+        """Save tokenizer to directory (HuggingFace compatible format)."""
+        os.makedirs(directory, exist_ok=True)
+
+        # 1. vocab.json
+        with open(os.path.join(directory, "vocab.json"), "w", encoding="utf-8") as f:
+            json.dump(self.vocab, f, ensure_ascii=False, indent=2)
+
+        # 2. merges.txt (space-separated with U+2581 for literal spaces)
+        with open(os.path.join(directory, "merges.txt"), "w", encoding="utf-8") as f:
+            f.write("#version: 0.3\n")
+            for pair in self.merges:
+                a = pair[0].replace(' ', '\u2581')
+                b = pair[1].replace(' ', '\u2581')
+                f.write(f"{a} {b}\n")
+
+        # 3. tokenizer_config.json
+        config = {
+            "tokenizer_class": "BPETokenizer",
+            "vocab_size": len(self.vocab),
+            "model_type": "bpe",
+            "special_tokens": self.special_tokens,
+            "do_lower_case": self.do_lower_case,
+            "language": "id",
+        }
+        with open(os.path.join(directory, "tokenizer_config.json"), "w", encoding="utf-8") as f:
+            json.dump(config, f, ensure_ascii=False, indent=2)
+
+        # 4. special_tokens_map.json
+        special_map = {
+            "pad_token": "<PAD>",
+            "unk_token": "<UNK>",
+            "bos_token": "<BOS>",
+            "eos_token": "<EOS>",
+        }
+        with open(os.path.join(directory, "special_tokens_map.json"), "w", encoding="utf-8") as f:
+            json.dump(special_map, f, ensure_ascii=False, indent=2)
+
+        # 5. tokenizer.json (HuggingFace format)
+        hf_tokenizer = {
+            "version": "1.0",
+            "model": {
+                "type": "BPE",
+                "vocab": self.vocab,
+                "merges": [
+                    f"{p[0].replace(' ', chr(0x2581))} {p[1].replace(' ', chr(0x2581))}"
+                    for p in self.merges
+                ],
+            },
+            "pre_tokenizer": {
+                "type": "Split",
+                "pattern": {"Regex": self.pattern.pattern},
+                "behavior": "Isolated",
+            },
+            "decoder": {
+                "type": "Fuse",
+            },
+            "added_tokens": [
+                {"id": v, "content": k, "special": True}
+                for k, v in self.special_tokens.items()
+            ],
+        }
+        if self.do_lower_case:
+            hf_tokenizer["normalizer"] = {"type": "Lowercase"}
+        with open(os.path.join(directory, "tokenizer.json"), "w", encoding="utf-8") as f:
+            json.dump(hf_tokenizer, f, ensure_ascii=False, indent=2)
+
+        print(f"💾 Tokenizer saved to: {directory}")
+
+    @classmethod
+    def from_pretrained(cls, directory: str) -> "BPETokenizer":
+        """Load tokenizer from directory."""
+        tokenizer = cls()
+
+        # Load vocab
+        with open(os.path.join(directory, "vocab.json"), "r", encoding="utf-8") as f:
+            tokenizer.vocab = json.load(f)
+
+        tokenizer.inverse_vocab = {v: k for k, v in tokenizer.vocab.items()}
+
+        # Load merges (supports both old JSON+tab and new space-separated formats)
+        tokenizer.merges = []
+        with open(os.path.join(directory, "merges.txt"), "r", encoding="utf-8") as f:
+            for line in f:
+                line = line.strip()
+                if line and not line.startswith("#"):
+                    if "\t" in line:
+                        # Old JSON+tab format (backward compat)
+                        parts = line.split("\t")
+                        if len(parts) == 2:
+                            a = json.loads(parts[0])
+                            b = json.loads(parts[1])
+                            tokenizer.merges.append((a, b))
+                    else:
+                        # New space-separated format with U+2581 escape
+                        parts = line.split(" ", 1)
+                        if len(parts) == 2:
+                            a = parts[0].replace('\u2581', ' ')
+                            b = parts[1].replace('\u2581', ' ')
+                            tokenizer.merges.append((a, b))
+
+        tokenizer._merge_priority = {pair: i for i, pair in enumerate(tokenizer.merges)}
+
+        # Load config
+        with open(os.path.join(directory, "tokenizer_config.json"), "r", encoding="utf-8") as f:
+            config = json.load(f)
+            tokenizer.special_tokens = config.get("special_tokens", tokenizer.special_tokens)
+            tokenizer.vocab_size = config.get("vocab_size", len(tokenizer.vocab))
+            tokenizer.do_lower_case = config.get("do_lower_case", True)
+
+        print(f"✅ Tokenizer loaded from: {directory}")
+        return tokenizer
+
+
+if __name__ == "__main__":
+    # Quick test
+    tokenizer = BPETokenizer(vocab_size=1000)
+
+    sample_texts = [
+        "Saya suka makan nasi goreng di Jakarta",
+        "Indonesia adalah negara kepulauan terbesar di dunia",
+    ]
+
+    tokenizer.train(sample_texts, min_frequency=1)
+
+    test = "saya makan nasi goreng"
+    tokens = tokenizer.tokenize(test)
+    ids = tokenizer.encode(test)
+    decoded = tokenizer.decode(ids)
+
+    print(f"\nInput:   {test}")
+    print(f"Tokens:  {tokens}")
+    print(f"IDs:     {ids}")
+    print(f"Decoded: {decoded}")

merges.txt

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+#version: 0.3
+a n
+e r
+▁ d
+▁ m
+e n
+s i
+a r
+▁ p
+i n
+an g
+a t
+▁ b
+▁ k
+a k
+▁ s
+u n
+▁ t
+a l
+a h
+e m
+▁d i
+a m
+o n
+▁m en
+▁d an
+a s
+k an
+e l
+a si
+a d
+▁ in
+u k
+▁ y
+▁b er
+▁y ang
+▁k e
+e k
+e si
+a g
+in g
+▁ l
+▁in d
+ak an
+a p
+i t
+en g
+u m
+▁p er
+on esi
+onesi a
+i k
+▁t er
+▁ind onesia
+y a
+▁s e
+u r
+▁ h
+u p
+ar i
+u s
+▁m em
+un g
+e s
+a i
+o r
+▁ un
+o l
+u t
+i l
+ar a
+e t
+al am
+o m
+t uk
+▁men g
+▁un tuk
+p er
+ad i
+▁ r
+▁m er
+▁p en
+d i
+t i
+an t
+▁ n
+a ya
+b ang
+▁mer up
+▁merup akan
+▁ j
+u l
+r o
+▁ g
+▁men j
+eng an
+▁d engan
+t er
+g i
+▁d alam
+a u
+▁ c
+i s
+a b
+u h
+b ag
+a w
+▁menj adi
+b er
+i m
+at an
+bag ai
+a an
+e h
+t r
+▁p em
+▁m as
+e g
+u d
+▁ 1
+▁d ari
+ing k
+i d
+ar ak
+▁m el
+g a
+al ah
+e b
+i an
+on al
+am a
+a j
+t ing
+o t
+y arak
+yarak at
+▁ f
+u si
+e p
+u b
+▁mas yarakat
+▁ ad
+▁p eng
+▁ w
+i h
+ad a
+▁ at
+at a
+n ya
+em bang
+a y
+ah an
+ah un
+▁ber bagai
+▁ber k
+ang an
+am p
+in d
+▁b ang
+▁h ar
+t em
+u kan
+i a
+ung an
+o k
+▁t ahun
+ingk at
+▁mem b
+i b
+ag a
+es ar
+s a
+es t
+▁p ada
+i kan
+▁t el
+o d
+▁men y
+▁pen ting
+▁ ol
+g un
+▁ke s
+▁n eg
+asi onal
+▁har us
+am an
+▁t r
+▁s em
+f or
+u an
+▁ad alah
+▁s ek
+ik asi
+i r
+▁d un
+▁dun ia
+as ar
+▁ter b
+in t
+▁l a
+▁k om
+▁ si
+for m
+▁ol eh
+▁p ro
+▁k er
+a c
+it as
+eh atan
+▁b ag
+on om
+▁tel ah
+per ti
+ter n
+em p
+▁s el
+an d
+▁ 2
+▁at au
+ek n
+ar an
+an an
+form asi
+▁men c
+▁l eb
+▁k u
+▁kes ehatan
+▁neg ara
+d ang
+en er
+j a
+ek s
+i si
+ap at
+ang k
+h ad
+had ap
+o b
+at u
+d uk
+o si
+▁leb ih
+▁ter us
+▁se bagai
+▁pem er
+ud aya
+id up
+ek onom
+▁s um
+p en
+▁ ant
+ol o
+olo gi
+p ut
+p ro
+▁se perti
+s tem
+g it
+▁pen y
+▁men ingkat
+at i
+p ul
+▁bang sa
+▁b udaya
+▁per t
+▁sel ur
+▁selur uh
+▁pemer int
+▁la ut
+et er
+ekn ologi
+▁se c
+▁sec ara
+▁in tern
+al an
+gun akan
+am b
+e d
+er a
+▁ alam
+▁ke c
+di di
+didi kan
+r i
+u j
+▁ber s
+▁m akan
+▁mem il
+▁memil ik
+▁memilik i
+▁k ar
+um b
+▁k el
+bang un
+ar ga
+as an
+ti f
+▁m an
+▁b el
+as a
+▁d apat
+git al
+an c
+en si
+ur ang
+il an
+t a
+k en
+▁bag i
+▁l ingk
+an j
+▁sek it
+▁sekit ar
+p em
+b el
+in kan
+l u
+g r
+▁b esar
+an s
+▁di gital
+▁ter hadap
+▁b i
+u at
+▁k on
+ot a
+▁j aw
+adi si
+ken al
+al i
+uh an
+▁pemerint ah
+▁n asional
+▁lingk ungan
+ap i
+ang g
+an ak
+▁per s
+▁k em
+uk um
+▁t an
+asi l
+m as
+▁sem ak
+▁semak in
+▁s er
+us tr
+ustr i
+▁meningkat kan
+e f
+urang i
+▁ker ja
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