Add benchmark results: 13 tokenizer comparison

README.md

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+---
+license: cc-by-nc-4.0
+language:
+  - ar
+  - en
+tags:
+  - tokenizer
+  - arabic
+  - morphology
+  - benchmark
+---
+
+# SARF Tokenizer
+
+**SARF** (Segmentation-Aware Rewriting Framework) is a morphologically-aware tokenizer for Arabic that combines unsupervised morphological segmentation (Morfessor) with Byte-Pair Encoding. It uses Unicode Private Use Area (PUA) characters to map Arabic morphemes to single tokens before BPE training, achieving strong Arabic tokenization with a compact 72K vocabulary.
+
+## Benchmark Results
+
+Evaluation on ~5,000 Arabic + 5,000 English samples from the eval-test-data dataset.
+
+| Rank | Tokenizer | Vocab | AR Fertility | AR Chars/Tok | EN Fertility | EN Chars/Tok | Parity | Score |
+|------|-----------|------:|-------------:|-------------:|-------------:|-------------:|-------:|------:|
+| 1 | GPT-4o | 200,019 | 2.249 | 3.111 | 1.213 | 3.492 | 0.8909 | 0.8280 |
+| 2 | Gemma-3-4B | 262,145 | 2.311 | 2.864 | 1.137 | 2.911 | 0.9840 | 0.7740 |
+| 3 | Fanar-1-9B | 128,256 | 2.264 | 2.812 | 1.141 | 2.880 | 0.9764 | 0.7603 |
+| 4 | Hala-9B | 128,256 | 2.264 | 2.812 | 1.141 | 2.880 | 0.9764 | 0.7603 |
+| 5 | Command-R-Arabic | 255,033 | 2.320 | 2.799 | 1.142 | 2.906 | 0.9631 | 0.7545 |
+| 6 | Qwen3-4B | 151,669 | 2.314 | 2.599 | 1.225 | 2.964 | 0.8767 | 0.6720 |
+| 7 | Qwen3-VL-4B | 151,669 | 2.314 | 2.599 | 1.225 | 2.964 | 0.8767 | 0.6720 |
+| 8 | Falcon-H1-7B | 130,049 | 2.083 | 3.272 | 1.266 | 2.835 | 1.1543 | 0.6608 |
+| 9 | **SARF (Ours)** | **72,195** | **1.978** | **2.832** | 1.561 | 3.163 | 0.8952 | 0.6203 |
+| 10 | ALLaM-7B | 64,000 | 1.286 | 3.898 | 1.197 | 2.699 | 1.4442 | 0.5615 |
+| 11 | GPT-4 | 100,277 | 4.111 | 1.430 | 1.225 | 3.452 | 0.4144 | 0.3583 |
+| 12 | Mistral-7B-v0.3 | 32,768 | 5.133 | 1.131 | 1.218 | 2.702 | 0.4185 | 0.1459 |
+| 13 | AceGPT-13B | 32,000 | 5.236 | 1.110 | 1.237 | 2.691 | 0.4124 | 0.1274 |
+
+### Metric Definitions
+
+- **AR Fertility**: Arabic tokens per word (lower = better)
+- **AR Chars/Tok**: Arabic characters per token (higher = better compression)
+- **EN Fertility**: English tokens per word (lower = better)
+- **EN Chars/Tok**: English characters per token (higher = better compression)
+- **Parity**: AR Chars/Tok / EN Chars/Tok (closer to 1.0 = more balanced)
+- **Score**: Composite metric (33% Arabic efficiency + 33% English efficiency + 33% parity), min-max normalized across all tokenizers
+
+### Key Findings
+
+- SARF achieves the **lowest Arabic fertility** (1.978 tokens/word) among all tokenizers with vocabulary under 130K, demonstrating that morphological preprocessing enables efficient Arabic tokenization without massive vocabularies.
+- With only **72K vocabulary**, SARF achieves Arabic compression (2.832 chars/token) competitive with tokenizers 2-3x its size.
+- SARF has **near-perfect parity** (0.895), meaning Arabic and English text are tokenized with similar efficiency — unlike GPT-4 (0.414) or ALLaM (1.444) which show strong language bias.
+
+## Tokenizers Compared
+
+| Tokenizer | Model | Source |
+|-----------|-------|--------|
+| SARF | DeepLatent | [almaghrabima/deeplatent-tokenizer-parity](https://huggingface.co/almaghrabima/deeplatent-tokenizer-parity) |
+| GPT-4o | o200k_base | tiktoken |
+| GPT-4 | cl100k_base | tiktoken |
+| ALLaM-7B | humain-ai/ALLaM-7B-Instruct-preview | HuggingFace |
+| AceGPT-13B | FreedomIntelligence/AceGPT-13B-chat | HuggingFace |
+| Gemma-3-4B | google/gemma-3-4b-it | HuggingFace |
+| Command-R Arabic | CohereLabs/c4ai-command-r7b-arabic-02-2025 | HuggingFace |
+| Fanar-1-9B | QCRI/Fanar-1-9B-Instruct | HuggingFace |
+| Hala-9B | hammh0a/Hala-9B | HuggingFace |
+| Qwen3-4B | Qwen/Qwen3-4B-Instruct-2507 | HuggingFace |
+| Qwen3-VL-4B | Qwen/Qwen3-VL-4B-Instruct | HuggingFace |
+| Mistral-7B-v0.3 | mistralai/Mistral-7B-Instruct-v0.3 | HuggingFace |
+| Falcon-H1-7B | tiiuae/Falcon-H1-7B-Instruct | HuggingFace |
+
+## How SARF Works
+
+SARF uses a morphologically-aware preprocessing pipeline before BPE:
+
+1. **Morfessor** segments Arabic words into morphemes unsupervised
+2. **Morpheme-to-PUA mapping** assigns each morpheme a Unicode Private Use Area character
+3. **ByteRewriter** rewrites Arabic text so morphemes become single characters
+4. **BPE** trains on the rewritten text, naturally learning morpheme-level tokens
+
+This approach achieves strong Arabic compression without inflating the vocabulary for English or other languages.
+
+## Files
+
+- `results.json` — Raw benchmark data
+- `tokenizer_benchmark.py` — Benchmark script (reproduces results)
+
+## Citation
+
+```bibtex
+@misc{sarf2025,
+  title={SARF: Segmentation-Aware Rewriting Framework for Arabic Tokenization},
+  author={Al-Maghrabima},
+  year={2025},
+  url={https://huggingface.co/almaghrabima/SARF-Tokenizer}
+}
+```
+
+## License
+
+CC-BY-NC-4.0

results.json

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+{
+  "num_ar_samples": 4998,
+  "num_en_samples": 5000,
+  "results": [
+    {
+      "name": "GPT-4o",
+      "vocab_size": 200019,
+      "ar_fertility": 2.2489,
+      "ar_chars_per_token": 3.1108,
+      "en_fertility": 1.2132,
+      "en_chars_per_token": 3.4918,
+      "parity": 0.8909,
+      "score": 0.828
+    },
+    {
+      "name": "Gemma-3-4B",
+      "vocab_size": 262145,
+      "ar_fertility": 2.3109,
+      "ar_chars_per_token": 2.8642,
+      "en_fertility": 1.1368,
+      "en_chars_per_token": 2.9107,
+      "parity": 0.984,
+      "score": 0.774
+    },
+    {
+      "name": "Fanar-1-9B",
+      "vocab_size": 128256,
+      "ar_fertility": 2.2643,
+      "ar_chars_per_token": 2.8119,
+      "en_fertility": 1.1412,
+      "en_chars_per_token": 2.88,
+      "parity": 0.9764,
+      "score": 0.7603
+    },
+    {
+      "name": "Hala-9B",
+      "vocab_size": 128256,
+      "ar_fertility": 2.2643,
+      "ar_chars_per_token": 2.8119,
+      "en_fertility": 1.1412,
+      "en_chars_per_token": 2.88,
+      "parity": 0.9764,
+      "score": 0.7603
+    },
+    {
+      "name": "Command-R-Arabic",
+      "vocab_size": 255033,
+      "ar_fertility": 2.3196,
+      "ar_chars_per_token": 2.7987,
+      "en_fertility": 1.1422,
+      "en_chars_per_token": 2.906,
+      "parity": 0.9631,
+      "score": 0.7545
+    },
+    {
+      "name": "Qwen3-4B",
+      "vocab_size": 151669,
+      "ar_fertility": 2.314,
+      "ar_chars_per_token": 2.5988,
+      "en_fertility": 1.2247,
+      "en_chars_per_token": 2.9641,
+      "parity": 0.8767,
+      "score": 0.672
+    },
+    {
+      "name": "Qwen3-VL-4B",
+      "vocab_size": 151669,
+      "ar_fertility": 2.314,
+      "ar_chars_per_token": 2.5988,
+      "en_fertility": 1.2247,
+      "en_chars_per_token": 2.9641,
+      "parity": 0.8767,
+      "score": 0.672
+    },
+    {
+      "name": "Falcon-H1-7B",
+      "vocab_size": 130049,
+      "ar_fertility": 2.0829,
+      "ar_chars_per_token": 3.2722,
+      "en_fertility": 1.2661,
+      "en_chars_per_token": 2.8348,
+      "parity": 1.1543,
+      "score": 0.6608
+    },
+    {
+      "name": "SARF (Ours)",
+      "vocab_size": 72195,
+      "ar_fertility": 1.9778,
+      "ar_chars_per_token": 2.8319,
+      "en_fertility": 1.5609,
+      "en_chars_per_token": 3.1635,
+      "parity": 0.8952,
+      "score": 0.6203
+    },
+    {
+      "name": "ALLaM-7B",
+      "vocab_size": 64000,
+      "ar_fertility": 1.2856,
+      "ar_chars_per_token": 3.8978,
+      "en_fertility": 1.1973,
+      "en_chars_per_token": 2.699,
+      "parity": 1.4442,
+      "score": 0.5615
+    },
+    {
+      "name": "GPT-4",
+      "vocab_size": 100277,
+      "ar_fertility": 4.1107,
+      "ar_chars_per_token": 1.4303,
+      "en_fertility": 1.2247,
+      "en_chars_per_token": 3.4518,
+      "parity": 0.4144,
+      "score": 0.3583
+    },
+    {
+      "name": "Mistral-7B-v0.3",
+      "vocab_size": 32768,
+      "ar_fertility": 5.1329,
+      "ar_chars_per_token": 1.1307,
+      "en_fertility": 1.2185,
+      "en_chars_per_token": 2.7016,
+      "parity": 0.4185,
+      "score": 0.1459
+    },
+    {
+      "name": "AceGPT-13B",
+      "vocab_size": 32000,
+      "ar_fertility": 5.236,
+      "ar_chars_per_token": 1.1098,
+      "en_fertility": 1.2368,
+      "en_chars_per_token": 2.6909,
+      "parity": 0.4124,
+      "score": 0.1274
+    }
+  ],
+  "markdown_table": "| Rank | Tokenizer | Vocab | AR Fertility | AR Chars/Tok | EN Fertility | EN Chars/Tok | Parity | Score |\n|------|-----------|------:|-------------:|-------------:|-------------:|-------------:|-------:|------:|\n| 1 | GPT-4o | 200,019 | 2.249 | 3.111 | 1.213 | 3.492 | 0.8909 | 0.8280 |\n| 2 | Gemma-3-4B | 262,145 | 2.311 | 2.864 | 1.137 | 2.911 | 0.9840 | 0.7740 |\n| 3 | Fanar-1-9B | 128,256 | 2.264 | 2.812 | 1.141 | 2.880 | 0.9764 | 0.7603 |\n| 4 | Hala-9B | 128,256 | 2.264 | 2.812 | 1.141 | 2.880 | 0.9764 | 0.7603 |\n| 5 | Command-R-Arabic | 255,033 | 2.320 | 2.799 | 1.142 | 2.906 | 0.9631 | 0.7545 |\n| 6 | Qwen3-4B | 151,669 | 2.314 | 2.599 | 1.225 | 2.964 | 0.8767 | 0.6720 |\n| 7 | Qwen3-VL-4B | 151,669 | 2.314 | 2.599 | 1.225 | 2.964 | 0.8767 | 0.6720 |\n| 8 | Falcon-H1-7B | 130,049 | 2.083 | 3.272 | 1.266 | 2.835 | 1.1543 | 0.6608 |\n| 9 | SARF (Ours) | 72,195 | 1.978 | 2.832 | 1.561 | 3.163 | 0.8952 | 0.6203 |\n| 10 | ALLaM-7B | 64,000 | 1.286 | 3.898 | 1.197 | 2.699 | 1.4442 | 0.5615 |\n| 11 | GPT-4 | 100,277 | 4.111 | 1.430 | 1.225 | 3.452 | 0.4144 | 0.3583 |\n| 12 | Mistral-7B-v0.3 | 32,768 | 5.133 | 1.131 | 1.218 | 2.702 | 0.4185 | 0.1459 |\n| 13 | AceGPT-13B | 32,000 | 5.236 | 1.110 | 1.237 | 2.691 | 0.4124 | 0.1274 |"
+}

tokenizer_benchmark.py

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+"""
+Multi-tokenizer comparison benchmark.
+
+Evaluates SARF against 11 other tokenizers on Arabic+English text,
+computing fertility, chars/token, parity, and a composite score.
+"""
+
+import os, sys, re, json, argparse, time
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+# Load .env file
+_env_path = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), ".env")
+if os.path.exists(_env_path):
+    with open(_env_path) as _f:
+        for _line in _f:
+            _line = _line.strip()
+            if _line and not _line.startswith("#") and "=" in _line:
+                _k, _v = _line.split("=", 1)
+                os.environ.setdefault(_k.strip(), _v.strip())
+
+# Disable hf_transfer if not installed
+try:
+    import hf_transfer  # noqa: F401
+except ImportError:
+    os.environ.pop("HF_HUB_ENABLE_HF_TRANSFER", None)
+
+import pyarrow.parquet as pq
+import glob as globmod
+
+from scripts.rewrite_bytes import ByteRewriter
+
+
+# ── Tokenizer wrappers ──────────────────────────────────────────────
+
+class SarfTokenizer:
+    def __init__(self, tokenizer_dir, morf_map_path):
+        from transformers import PreTrainedTokenizerFast
+        self._tok = PreTrainedTokenizerFast(
+            tokenizer_file=os.path.join(tokenizer_dir, "tokenizer.json")
+        )
+        self._rewriter = ByteRewriter(morf_map_path)
+
+    def encode(self, text):
+        return self._tok.encode(self._rewriter.rewrite_text(text), add_special_tokens=False)
+
+    @property
+    def vocab_size(self):
+        return len(self._tok)
+
+    @property
+    def name(self):
+        return "SARF (Ours)"
+
+
+class TiktokenTokenizer:
+    def __init__(self, encoding_name, display_name=None):
+        import tiktoken
+        self._enc = tiktoken.get_encoding(encoding_name)
+        self._name = display_name or encoding_name
+
+    def encode(self, text):
+        return self._enc.encode(text, allowed_special="all")
+
+    @property
+    def vocab_size(self):
+        return self._enc.n_vocab
+
+    @property
+    def name(self):
+        return self._name
+
+
+class HFTokenizer:
+    def __init__(self, model_id, display_name=None):
+        from transformers import AutoTokenizer
+        try:
+            self._tok = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+        except Exception:
+            self._tok = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, use_fast=False)
+        self._name = display_name or model_id.split("/")[-1]
+
+    def encode(self, text):
+        return self._tok.encode(text, add_special_tokens=False)
+
+    @property
+    def vocab_size(self):
+        return len(self._tok)
+
+    @property
+    def name(self):
+        return self._name
+
+
+# ── Tokenizer registry ──────────────────────────────────────────────
+
+TOKENIZER_DEFS = [
+    # (display_name, type, source)
+    ("SARF (Ours)", "sarf", None),
+    ("GPT-4o", "tiktoken", "o200k_base"),
+    ("GPT-4", "tiktoken", "cl100k_base"),
+    ("ALLaM-7B", "hf", "humain-ai/ALLaM-7B-Instruct-preview"),
+    ("AceGPT-13B", "hf", "FreedomIntelligence/AceGPT-13B-chat"),
+    ("Gemma-3-4B", "hf", "google/gemma-3-4b-it"),
+    ("Command-R-Arabic", "hf", "CohereLabs/c4ai-command-r7b-arabic-02-2025"),
+    ("Fanar-1-9B", "hf", "QCRI/Fanar-1-9B-Instruct"),
+    ("Hala-9B", "hf", "hammh0a/Hala-9B"),
+    ("Qwen3-4B", "hf", "Qwen/Qwen3-4B-Instruct-2507"),
+    ("Qwen3-VL-4B", "hf", "Qwen/Qwen3-VL-4B-Instruct"),
+    ("Mistral-7B-v0.3", "hf", "mistralai/Mistral-7B-Instruct-v0.3"),
+    ("Falcon-H1-7B", "hf", "tiiuae/Falcon-H1-7B-Instruct"),
+]
+
+
+def load_all_tokenizers(tokenizer_dir, morf_map_path):
+    """Load all tokenizers. Returns list of wrapper objects."""
+    tokenizers = []
+    for display_name, typ, source in TOKENIZER_DEFS:
+        print(f"Loading {display_name}...", end=" ", flush=True)
+        t0 = time.time()
+        try:
+            if typ == "sarf":
+                tok = SarfTokenizer(tokenizer_dir, morf_map_path)
+            elif typ == "tiktoken":
+                tok = TiktokenTokenizer(source, display_name)
+            elif typ == "hf":
+                tok = HFTokenizer(source, display_name)
+            else:
+                raise ValueError(f"Unknown type: {typ}")
+            print(f"OK (vocab={tok.vocab_size:,}, {time.time()-t0:.1f}s)")
+            tokenizers.append(tok)
+        except Exception as e:
+            print(f"FAILED: {e}")
+    return tokenizers
+
+
+# ── Data loading ─────────────────────────────────────────────────────
+
+AR_DETECT = re.compile(r'[\u0600-\u06FF]')
+
+
+def load_samples(data_dir, num_ar=5000, num_en=5000):
+    parquet_files = sorted(globmod.glob(os.path.join(data_dir, '*.parquet')))
+    ar_samples, en_samples = [], []
+    for filepath in parquet_files:
+        if len(ar_samples) >= num_ar and len(en_samples) >= num_en:
+            break
+        pf = pq.ParquetFile(filepath)
+        for rg_idx in range(pf.num_row_groups):
+            rg = pf.read_row_group(rg_idx)
+            for text in rg.column("text").to_pylist():
+                if len(text) < 100:
+                    continue
+                ar_chars = len(AR_DETECT.findall(text))
+                ar_ratio = ar_chars / len(text)
+                if ar_ratio > 0.3 and len(ar_samples) < num_ar:
+                    ar_samples.append(text[:2000])
+                elif ar_ratio < 0.05 and len(en_samples) < num_en:
+                    en_samples.append(text[:2000])
+                if len(ar_samples) >= num_ar and len(en_samples) >= num_en:
+                    break
+    print(f"Loaded {len(ar_samples)} Arabic, {len(en_samples)} English samples")
+    return ar_samples, en_samples
+
+
+# ── Metrics ──────────────────────────────────────────────────────────
+
+AR_WORD = re.compile(r'[\u0600-\u06FF]+')
+EN_WORD = re.compile(r'[a-zA-Z]+')
+
+
+def compute_metrics(tokenizer, ar_texts, en_texts):
+    """Compute fertility, chars/token, and parity for one tokenizer."""
+    ar_total_chars = ar_total_tokens = ar_total_words = ar_total_word_tokens = 0
+    for text in ar_texts:
+        tokens = tokenizer.encode(text)
+        ar_total_chars += len(text)
+        ar_total_tokens += len(tokens)
+        words = AR_WORD.findall(text)
+        ar_total_words += len(words)
+        for w in words:
+            ar_total_word_tokens += len(tokenizer.encode(w))
+
+    en_total_chars = en_total_tokens = en_total_words = en_total_word_tokens = 0
+    for text in en_texts:
+        tokens = tokenizer.encode(text)
+        en_total_chars += len(text)
+        en_total_tokens += len(tokens)
+        words = EN_WORD.findall(text)
+        en_total_words += len(words)
+        for w in words:
+            en_total_word_tokens += len(tokenizer.encode(w))
+
+    ar_fertility = ar_total_word_tokens / ar_total_words if ar_total_words else 0
+    ar_cpt = ar_total_chars / ar_total_tokens if ar_total_tokens else 0
+    en_fertility = en_total_word_tokens / en_total_words if en_total_words else 0
+    en_cpt = en_total_chars / en_total_tokens if en_total_tokens else 0
+    parity = ar_cpt / en_cpt if en_cpt else 0
+
+    return {
+        "name": tokenizer.name,
+        "vocab_size": tokenizer.vocab_size,
+        "ar_fertility": round(ar_fertility, 4),
+        "ar_chars_per_token": round(ar_cpt, 4),
+        "en_fertility": round(en_fertility, 4),
+        "en_chars_per_token": round(en_cpt, 4),
+        "parity": round(parity, 4),
+    }
+
+
+# ── Composite score ──────────────────────────────────────────────────
+
+def compute_scores(results):
+    """Add normalized composite score to each result dict (in-place).
+
+    Score = 33% Arabic + 33% English + 33% Parity
+    Arabic sub = 50% fertility (lower=better, inverted) + 50% chars/token (higher=better)
+    English sub = same
+    Parity sub = closeness to 1.0 (lower |1-p| = better, inverted)
+    Each sub-metric min-max normalized across tokenizers.
+    """
+    def minmax(vals, invert=False):
+        lo, hi = min(vals), max(vals)
+        if hi == lo:
+            return [0.5] * len(vals)
+        normed = [(v - lo) / (hi - lo) for v in vals]
+        if invert:
+            normed = [1.0 - n for n in normed]
+        return normed
+
+    n = len(results)
+    ar_fert_n = minmax([r["ar_fertility"] for r in results], invert=True)
+    ar_cpt_n = minmax([r["ar_chars_per_token"] for r in results])
+    en_fert_n = minmax([r["en_fertility"] for r in results], invert=True)
+    en_cpt_n = minmax([r["en_chars_per_token"] for r in results])
+    parity_dev = [abs(1.0 - r["parity"]) for r in results]
+    parity_n = minmax(parity_dev, invert=True)
+
+    for i, r in enumerate(results):
+        ar_sub = 0.5 * ar_fert_n[i] + 0.5 * ar_cpt_n[i]
+        en_sub = 0.5 * en_fert_n[i] + 0.5 * en_cpt_n[i]
+        score = (ar_sub + en_sub + parity_n[i]) / 3.0
+        r["score"] = round(score, 4)
+
+
+# ── Display ──────────────────────────────────────────────────────────
+
+def print_table(results):
+    results_sorted = sorted(results, key=lambda r: r["score"], reverse=True)
+    header = f"{'Rank':<5} {'Tokenizer':<22} {'Vocab':>9} {'AR Fert':>9} {'AR C/T':>9} {'EN Fert':>9} {'EN C/T':>9} {'Parity':>9} {'Score':>9}"
+    print("\n" + "=" * len(header))
+    print("TOKENIZER BENCHMARK RESULTS")
+    print("=" * len(header))
+    print(header)
+    print("-" * len(header))
+    for rank, r in enumerate(results_sorted, 1):
+        print(f"{rank:<5} {r['name']:<22} {r['vocab_size']:>9,} {r['ar_fertility']:>9.3f} {r['ar_chars_per_token']:>9.3f} {r['en_fertility']:>9.3f} {r['en_chars_per_token']:>9.3f} {r['parity']:>9.4f} {r['score']:>9.4f}")
+    print("=" * len(header))
+    print("AR Fert = Arabic tokens/word (lower=better)")
+    print("AR C/T  = Arabic chars/token (higher=better)")
+    print("EN Fert = English tokens/word (lower=better)")
+    print("EN C/T  = English chars/token (higher=better)")
+    print("Parity  = AR_C/T / EN_C/T (closer to 1.0=better)")
+    print("Score   = composite (higher=better)\n")
+
+
+def results_to_markdown(results):
+    """Return a markdown table string for the results."""
+    results_sorted = sorted(results, key=lambda r: r["score"], reverse=True)
+    lines = [
+        "| Rank | Tokenizer | Vocab | AR Fertility | AR Chars/Tok | EN Fertility | EN Chars/Tok | Parity | Score |",
+        "|------|-----------|------:|-------------:|-------------:|-------------:|-------------:|-------:|------:|",
+    ]
+    for rank, r in enumerate(results_sorted, 1):
+        lines.append(
+            f"| {rank} | {r['name']} | {r['vocab_size']:,} | {r['ar_fertility']:.3f} | {r['ar_chars_per_token']:.3f} | {r['en_fertility']:.3f} | {r['en_chars_per_token']:.3f} | {r['parity']:.4f} | {r['score']:.4f} |"
+        )
+    return "\n".join(lines)
+
+
+# ── Main ─────────────────────────────────────────────────────────────
+
+def main():
+    parser = argparse.ArgumentParser(description="Multi-tokenizer comparison benchmark")
+    parser.add_argument("--data_dir", default="/root/.cache/Deeplatent/eval_1b/data")
+    parser.add_argument("--tokenizer_dir", default="/root/.cache/deeplatent/tokenizer_parity")
+    parser.add_argument("--morf_map_path", default="/root/.cache/deeplatent/morfessor_models/morf_map.json")
+    parser.add_argument("--num_samples", type=int, default=5000)
+    parser.add_argument("--output", default="benchmark_results.json")
+    parser.add_argument("--dry_run", action="store_true", help="Test on 10 samples first")
+    args = parser.parse_args()
+
+    # Load tokenizers
+    print("Loading tokenizers...")
+    tokenizers = load_all_tokenizers(args.tokenizer_dir, args.morf_map_path)
+    print(f"\nLoaded {len(tokenizers)} tokenizers successfully.\n")
+
+    # Load data
+    n = 10 if args.dry_run else args.num_samples
+    print(f"Loading {n} samples per language...")
+    ar_texts, en_texts = load_samples(args.data_dir, n, n)
+
+    # Evaluate
+    results = []
+    for tok in tokenizers:
+        print(f"Evaluating {tok.name}...", end=" ", flush=True)
+        t0 = time.time()
+        m = compute_metrics(tok, ar_texts, en_texts)
+        print(f"done ({time.time()-t0:.1f}s)")
+        results.append(m)
+
+    # Compute composite scores
+    compute_scores(results)
+
+    # Display
+    print_table(results)
+
+    # Save
+    output = {
+        "num_ar_samples": len(ar_texts),
+        "num_en_samples": len(en_texts),
+        "results": sorted(results, key=lambda r: r["score"], reverse=True),
+        "markdown_table": results_to_markdown(results),
+    }
+    with open(args.output, 'w') as f:
+        json.dump(output, f, indent=2, ensure_ascii=False)
+    print(f"Results saved to {args.output}")
+
+
+if __name__ == "__main__":
+    main()