Add README.md: 600-sample SARFTokenizer benchmark eval

README.md

@@ -0,0 +1,155 @@
+---
+license: apache-2.0
+language:
+  - ar
+  - en
+tags:
+  - tokenizer
+  - benchmark
+  - eval
+  - arabic
+  - english
+  - chars-per-token
+pretty_name: SARFTokenizer Benchmark Eval (600)
+size_categories:
+  - n<1K
+task_categories:
+  - text-classification
+dataset_info:
+  features:
+    - name: idx
+      dtype: int64
+    - name: language
+      dtype: string
+    - name: text
+      dtype: string
+    - name: source
+      dtype: string
+  splits:
+    - name: test
+      num_bytes: 574100
+      num_examples: 600
+configs:
+  - config_name: default
+    data_files:
+      - split: test
+        path: eval.parquet
+---
+
+# SARFTokenizer Benchmark Eval (600 docs)
+
+The exact 300 Arabic + 300 English documents used to benchmark [`almaghrabima/SARFTokenizer`](https://huggingface.co/almaghrabima/SARFTokenizer) against GPT-5, GPT-4o, Gemma-4, Qwen3.6, Kimi-K2.6, ALLaM, and 8 other tokenizers.
+
+Publishing this dataset makes the benchmark in [SARFTokenizer/BENCHMARK.md](https://huggingface.co/almaghrabima/SARFTokenizer/blob/main/BENCHMARK.md) **fully reproducible** — anyone can compute the exact same chars-per-token numbers on the exact same samples.
+
+## Statistics
+
+| | AR | EN | Total |
+|---|--:|--:|--:|
+| Documents | 300 | 300 | 600 |
+| Characters | 479,808 | 69,308 | 549,116 |
+| Words | 82,964 | 9,805 | 92,769 |
+| Max chars/sample | 2,000 | 2,000 | — |
+
+## Schema
+
+| Field | Type | Description |
+|---|---|---|
+| `idx` | int64 | Index within the per-language subset (0–299) |
+| `language` | string | `"ar"` or `"en"` |
+| `text` | string | Document text, truncated to 2000 characters |
+| `source` | string | Always `"deeplatent-hq-bilingual"` |
+
+Sampled from the first 5 `ar_*.parquet` and first 5 `en_*.parquet` files of the `deeplatent-hq-bilingual` validation shards, with a 10% Arabic-character threshold for AR filtering (matching `scripts/bench_tokenizers.py`).
+
+## Reproduce the headline benchmark
+
+```bash
+pip install transformers tokenizers datasets
+```
+
+```python
+from datasets import load_dataset
+from transformers import AutoTokenizer
+
+# Our eval corpus
+ds = load_dataset("almaghrabima/SARFTokenizer-benchmark-eval", split="test")
+ar_texts = [r["text"] for r in ds if r["language"] == "ar"]
+en_texts = [r["text"] for r in ds if r["language"] == "en"]
+
+# Load any tokenizer
+from huggingface_hub import login
+login(token="your_hf_token")  # needed for private SARFTokenizer; skip if public
+tok = AutoTokenizer.from_pretrained("almaghrabima/SARFTokenizer")
+
+def stats(texts):
+    chars = sum(len(t) for t in texts)
+    words = sum(len(t.split()) for t in texts)
+    tokens = sum(len(tok.encode(t, add_special_tokens=False)) for t in texts)
+    return chars, words, tokens
+
+ar_c, ar_w, ar_t = stats(ar_texts)
+en_c, en_w, en_t = stats(en_texts)
+print(f"AR: {ar_c:,}c / {ar_t:,}t  →  CpT={ar_c/ar_t:.3f}  T/W={ar_t/ar_w:.2f}")
+print(f"EN: {en_c:,}c / {en_t:,}t  →  CpT={en_c/en_t:.3f}  T/W={en_t/en_w:.2f}")
+print(f"Parity = {(ar_c/ar_t)/(en_c/en_t):.3f}")
+```
+
+Expected output for SARFTokenizer v0.2:
+
+```
+AR: 479,808c / 130,253t  →  CpT=3.683  T/W=1.57
+EN:  69,308c /  19,680t  →  CpT=3.522  T/W=2.01
+Parity = 1.046
+```
+
+## Compare multiple tokenizers in one shot
+
+```python
+from datasets import load_dataset
+from transformers import AutoTokenizer
+from huggingface_hub import login
+
+login(token="your_hf_token")  # needed for private SARFTokenizer
+
+ds = load_dataset("almaghrabima/SARFTokenizer-benchmark-eval", split="test")
+ar_texts = [r["text"] for r in ds if r["language"] == "ar"]
+en_texts = [r["text"] for r in ds if r["language"] == "en"]
+ar_chars = sum(len(t) for t in ar_texts)
+en_chars = sum(len(t) for t in en_texts)
+
+peers = {
+    "SARFTokenizer-v0.2":      ("almaghrabima/SARFTokenizer", False),
+    "gemma-4-31B-it":          ("google/gemma-4-31B-it", False),
+    "Qwen3.6-35B-A3B":         ("Qwen/Qwen3.6-35B-A3B", False),
+    "Kimi-K2.6":               ("moonshotai/Kimi-K2.6", True),
+    "ALLaM-7B":                ("ALLaM-AI/ALLaM-7B-Instruct-preview", False),
+    "Qwen2.5-0.5B":            ("Qwen/Qwen2.5-0.5B", False),
+    "Falcon-7B":               ("tiiuae/falcon-7b", False),
+}
+
+print(f"{'Tokenizer':<22} {'Vocab':>10} {'AR CpT':>8} {'EN CpT':>8} {'Parity':>8}")
+print("=" * 60)
+for name, (repo, trc) in peers.items():
+    try:
+        t = AutoTokenizer.from_pretrained(repo, trust_remote_code=trc)
+        ar_t = sum(len(t.encode(x, add_special_tokens=False)) for x in ar_texts)
+        en_t = sum(len(t.encode(x, add_special_tokens=False)) for x in en_texts)
+        ar_cpt = ar_chars / ar_t
+        en_cpt = en_chars / en_t
+        print(f"{name:<22} {len(t):>10,} {ar_cpt:>8.3f} {en_cpt:>8.3f} {ar_cpt/en_cpt:>8.3f}")
+    except Exception as e:
+        print(f"{name:<22} SKIP: {e.__class__.__name__}")
+```
+
+Expected numbers are the [headline benchmark](https://huggingface.co/almaghrabima/SARFTokenizer/blob/main/BENCHMARK.md) — any divergence means a tokenizer changed upstream.
+
+## Files
+
+- `eval.parquet` �� 600 documents, schema above, ~540 KB
+- `summary.json` — aggregate stats
+- `README.md` — this file
+
+## License
+
+Apache 2.0. Underlying text is from the `deeplatent-hq-bilingual` curated corpus; individual documents retain their original web-sourced licenses.