NIRVLab
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xnli-morphel-tr-32k-ablation-random-baseline

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 ---
+language: tr
+license: apache-2.0
+tags:
+  - tokenizer
+  - morphel
+  - morphology-aware
+  - turkish
+  - xnli
+  - nlp-research
+  - low-resource
+  - ablation-random-baseline
+datasets:
+  - facebook/xnli
 ---
+# NIRVLab — MorpheL Tokenizer for Turkish XNLI — ABLATION: Random-segmentation null baseline
+**This is NOT the real MorpheL algorithm.** Every algorithmic component is
+disabled: no MI scoring (Eq.1-4), no vowel-consonant plausible-boundary
+pre-filter, no TopK candidate ranking (Eq.5), no Gumbel sampling (Eq.6-9).
+Boundary count/position come from a plain uniform RNG. Exists purely as a
+worst-case sanity control for MorpheL's own pipeline (Proposal: *MorpheL:
+Morphology-Aware Tokenizer Adaptation for Pretrained Models in Low-Resource
+Languages*) — if the real MI+Gumbel pipeline doesn't clearly beat this, the
+added complexity isn't earning its keep.
+Trained on the Turkish (`tr`) subset of
+[facebook/xnli](https://huggingface.co/datasets/facebook/xnli) — all splits.
+## Algorithm (ablated)
+Words are segmented by drawing a uniform-random number of cuts (0-2) and
+uniform-random cut positions — no MI, no vowel filter, no Gumbel, no learned
+signal of any kind. Segmentation is cached at induction time with a fixed
+seed for reproducibility, but carries no linguistic information.
+## Training Config
+| Parameter | Value |
+|---|---|
+| Algorithm | RANDOM (ablation — MI+Gumbel+vowel-filter all OFF) |
+| Vocabulary size | 32,055 |
+| random_max_cuts | 2 |
+| seed | 42 |
+| min_frequency | 2 |
+| Special tokens | `<s>, <pad>, </s>, <unk>, <mask>` |
+| Corpus | `facebook/xnli/tr` — all splits (800,404 sentences) |
+| Vowel set | N/A — no plausible-boundary filter in this ablation |
+## Evaluation Metrics (vs Baselines, vocab_size=32000, same corpus)
+| Metric | BPE | WordPiece | Unigram | MorpheL (full) | **Random (this ablation)** |
+|---|---|---|---|---|
+| Fertility ↓ | — | — | — | — | **1.4317** |
+| Tokens/char ↓ | — | — | — | — | **0.1911** |
+| Avg seq len ↓ | — | — | — | — | **16.92** |
+| Vocab coverage ↑ | — | — | — | — | **1.0000** |
+| OOV rate ↓ | — | — | — | — | **0.0000** |
+*Fill baseline columns after running baseline notebooks.*
+## Usage
+```python
+from transformers import AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("NIRVLab/xnli-morphel-tr-32k-ablation-random-baseline")
+```
+> **Note**: this checkpoint uses RANDOM segmentation (ablation control) — do
+> not use it for actual MorpheL comparisons other than as the worst-case
+> sanity baseline. No temperature / Gumbel parameter applies here.