diff --git a/README.md b/README.md index 25048daace7df6df4bfe230761e0b02cd96c7f41..26b301797f3793c0d256433fe34c1fbce01e0356 100644 --- a/README.md +++ b/README.md @@ -23,14 +23,14 @@ dataset_info: metrics: - name: best_compression_ratio type: compression - value: 4.113 + value: 4.243 - name: best_isotropy type: isotropy - value: 0.0314 + value: 0.0518 - name: vocabulary_size type: vocab - value: 2085 -generated: 2025-12-28 + value: 0 +generated: 2026-01-03 --- # CH - Wikilangs Models @@ -44,12 +44,13 @@ We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and ### Models & Assets - Tokenizers (8k, 16k, 32k, 64k) -- N-gram models (2, 3, 4-gram) -- Markov chains (context of 1, 2, 3 and 4) +- N-gram models (2, 3, 4, 5-gram) +- Markov chains (context of 1, 2, 3, 4 and 5) - Subword N-gram and Markov chains -- Embeddings in various sizes and dimensions +- Embeddings in various sizes and dimensions (aligned and unaligned) - Language Vocabulary - Language Statistics + ![Performance Dashboard](visualizations/performance_dashboard.png) ### Analysis and Evaluation @@ -59,7 +60,8 @@ We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and - [3. Markov Chain Evaluation](#3-markov-chain-evaluation) - [4. Vocabulary Analysis](#4-vocabulary-analysis) - [5. Word Embeddings Evaluation](#5-word-embeddings-evaluation) -- [6. Summary & Recommendations](#6-summary--recommendations) +- [6. Morphological Analysis (Experimental)](#6-morphological-analysis) +- [7. Summary & Recommendations](#7-summary--recommendations) - [Metrics Glossary](#appendix-metrics-glossary--interpretation-guide) - [Visualizations Index](#visualizations-index) @@ -68,44 +70,49 @@ We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and ![Tokenizer Compression](visualizations/tokenizer_compression.png) +![Tokenizer Fertility](visualizations/tokenizer_fertility.png) + +![Tokenizer OOV](visualizations/tokenizer_oov.png) + +![Total Tokens](visualizations/tokenizer_total_tokens.png) + ### Results | Vocab Size | Compression | Avg Token Len | UNK Rate | Total Tokens | |------------|-------------|---------------|----------|--------------| -| **8k** | 3.833x | 3.79 | 0.1107% | 46,087 | -| **16k** | 4.113x 🏆 | 4.07 | 0.1187% | 42,948 | +| **8k** | 3.977x | 3.99 | 0.1019% | 38,272 | +| **16k** | 4.243x 🏆 | 4.26 | 0.1087% | 35,871 | ### Tokenization Examples Below are sample sentences tokenized with each vocabulary size: -**Sample 1:** `+ 125px 125px 300px -Bulgaria, capitat Sofia.` +**Sample 1:** `Doerun, nasong-song gi Estados Unidos. GuĂ„ha 774 na tataogues na populasion i se...` | Vocab | Tokens | Count | |-------|--------|-------| -| 8k | `▁+ ▁ 1 2 5 px ▁ 1 2 5 ... (+12 more)` | 22 | -| 16k | `▁+ ▁ 1 2 5 px ▁ 1 2 5 ... (+11 more)` | 21 | +| 8k | `▁do er un , ▁nasong - song ▁gi ▁estados ▁unidos ... (+16 more)` | 26 | +| 16k | `▁doerun , ▁nasong - song ▁gi ▁estados ▁unidos . ▁guĂ„ha ... (+14 more)` | 24 | -**Sample 2:** `Giacomo Puccini (1858, Lucca, Italia - 1924, Bruselas, Belgica), un danderu Ă„tmo...` +**Sample 2:** `Newhalen, nasong-song gi Estados Unidos. GuĂ„ha 190 na tataogues na populasion i ...` | Vocab | Tokens | Count | |-------|--------|-------| -| 8k | `▁gi a com o ▁pu ccini ▁( 1 8 5 ... (+29 more)` | 39 | -| 16k | `▁giacomo ▁puccini ▁( 1 8 5 8 , ▁lucca , ... (+25 more)` | 35 | +| 8k | `▁newha len , ▁nasong - song ▁gi ▁estados ▁unidos . ... (+15 more)` | 25 | +| 16k | `▁newhalen , ▁nasong - song ▁gi ▁estados ▁unidos . ▁guĂ„ha ... (+14 more)` | 24 | -**Sample 3:** `I Sisteman Laibirihan Pupbleko GuĂ„han este na ufsiĂ„t na sistema para i islan GuĂ„...` +**Sample 3:** `Larsen Bay, nasong-song gi Estados Unidos. GuĂ„ha 87 na tataogues na populasion i...` | Vocab | Tokens | Count | |-------|--------|-------| -| 8k | `▁i ▁sisteman ▁lai birihan ▁pupble ko ▁guĂ„han ▁este ▁na ▁ufsiĂ„t ... (+7 more)` | 17 | -| 16k | `▁i ▁sisteman ▁laibirihan ▁pupbleko ▁guĂ„han ▁este ▁na ▁ufsiĂ„t ▁na ▁sistema ... (+5 more)` | 15 | +| 8k | `▁larsen ▁bay , ▁nasong - song ▁gi ▁estados ▁unidos . ... (+14 more)` | 24 | +| 16k | `▁larsen ▁bay , ▁nasong - song ▁gi ▁estados ▁unidos . ... (+14 more)` | 24 | ### Key Findings -- **Best Compression:** 16k achieves 4.113x compression -- **Lowest UNK Rate:** 8k with 0.1107% unknown tokens +- **Best Compression:** 16k achieves 4.243x compression +- **Lowest UNK Rate:** 8k with 0.1019% unknown tokens - **Trade-off:** Larger vocabularies improve compression but increase model size - **Recommendation:** 32k vocabulary provides optimal balance for production use @@ -114,57 +121,89 @@ Bulgaria, capitat Sofia.` ![N-gram Perplexity](visualizations/ngram_perplexity.png) +![N-gram Unique](visualizations/ngram_unique.png) + ![N-gram Coverage](visualizations/ngram_coverage.png) ### Results -| N-gram | Perplexity | Entropy | Unique N-grams | Top-100 Coverage | Top-1000 Coverage | -|--------|------------|---------|----------------|------------------|-------------------| -| **2-gram** | 304 🏆 | 8.25 | 836 | 61.4% | 100.0% | -| **2-gram** | 262 🏆 | 8.03 | 1,028 | 68.2% | 99.9% | -| **3-gram** | 367 | 8.52 | 1,276 | 58.0% | 93.7% | -| **3-gram** | 1,384 | 10.43 | 5,255 | 35.7% | 78.3% | -| **4-gram** | 472 | 8.88 | 1,937 | 54.6% | 83.8% | -| **4-gram** | 3,626 | 11.82 | 13,909 | 27.2% | 58.1% | +| N-gram | Variant | Perplexity | Entropy | Unique N-grams | Top-100 Coverage | Top-1000 Coverage | +|--------|---------|------------|---------|----------------|------------------|-------------------| +| **2-gram** | Word | 181 | 7.50 | 496 | 68.1% | 100.0% | +| **2-gram** | Subword | 228 | 7.83 | 869 | 71.1% | 100.0% | +| **3-gram** | Word | 134 🏆 | 7.07 | 582 | 70.7% | 100.0% | +| **3-gram** | Subword | 1,281 | 10.32 | 4,543 | 36.5% | 79.7% | +| **4-gram** | Word | 158 | 7.30 | 842 | 66.6% | 100.0% | +| **4-gram** | Subword | 3,667 | 11.84 | 12,416 | 26.2% | 57.0% | ### Top 5 N-grams by Size -**2-grams:** +**2-grams (Word):** | Rank | N-gram | Count | |------|--------|-------| -| 1 | `katigoria :` | 774 | -| 2 | `estados unidos` | 443 | -| 3 | `. katigoria` | 411 | -| 4 | `i sengsong` | 364 | -| 5 | `. guĂ„ha` | 328 | +| 1 | `i sengsong` | 364 | +| 2 | `nu i` | 329 | +| 3 | `i senso` | 310 | +| 4 | `na populasion` | 309 | +| 5 | `populasion i` | 308 | -**3-grams:** +**3-grams (Word):** | Rank | N-gram | Count | |------|--------|-------| -| 1 | `. katigoria :` | 411 | -| 2 | `nu i senso` | 308 | +| 1 | `nu i senso` | 308 | +| 2 | `na tataogues na` | 304 | | 3 | `na populasion i` | 304 | -| 4 | `na tataogues na` | 304 | -| 5 | `tataogues na populasion` | 304 | +| 4 | `tataogues na populasion` | 304 | +| 5 | `i sengsong nu` | 299 | -**4-grams:** +**4-grams (Word):** | Rank | N-gram | Count | |------|--------|-------| | 1 | `na tataogues na populasion` | 304 | | 2 | `tataogues na populasion i` | 303 | -| 3 | `i sengsong nu i` | 299 | -| 4 | `populasion i sengsong nu` | 299 | -| 5 | `na populasion i sengsong` | 299 | +| 3 | `na populasion i sengsong` | 299 | +| 4 | `sengsong nu i senso` | 299 | +| 5 | `populasion i sengsong nu` | 299 | + +**2-grams (Subword):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `a _` | 4,934 | +| 2 | `i _` | 4,206 | +| 3 | `n a` | 2,921 | +| 4 | `a n` | 2,812 | +| 5 | `_ i` | 2,769 | + +**3-grams (Subword):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `_ i _` | 2,254 | +| 2 | `_ n a` | 1,827 | +| 3 | `n a _` | 1,562 | +| 4 | `_ g i` | 1,306 | +| 5 | `_ m a` | 1,153 | + +**4-grams (Subword):** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `_ n a _` | 1,359 | +| 2 | `_ g i _` | 957 | +| 3 | `s o n g` | 950 | +| 4 | `_ i _ s` | 792 | +| 5 | `o n g _` | 757 | ### Key Findings -- **Best Perplexity:** 2-gram with 262 +- **Best Perplexity:** 3-gram (word) with 134 - **Entropy Trend:** Decreases with larger n-grams (more predictable) -- **Coverage:** Top-1000 patterns cover ~58% of corpus +- **Coverage:** Top-1000 patterns cover ~57% of corpus - **Recommendation:** 4-gram or 5-gram for best predictive performance --- @@ -172,55 +211,86 @@ Bulgaria, capitat Sofia.` ![Markov Entropy](visualizations/markov_entropy.png) +![Markov Contexts](visualizations/markov_contexts.png) + ![Markov Branching](visualizations/markov_branching.png) ### Results -| Context | Avg Entropy | Perplexity | Branching Factor | Unique Contexts | Predictability | -|---------|-------------|------------|------------------|-----------------|----------------| -| **1** | 0.4437 | 1.360 | 2.83 | 5,950 | 55.6% | -| **1** | 1.1631 | 2.239 | 8.71 | 235 | 0.0% | -| **2** | 0.1938 | 1.144 | 1.42 | 16,706 | 80.6% | -| **2** | 1.1662 | 2.244 | 5.45 | 2,042 | 0.0% | -| **3** | 0.0836 | 1.060 | 1.14 | 23,561 | 91.6% | -| **3** | 0.7209 | 1.648 | 2.73 | 11,105 | 27.9% | -| **4** | 0.0382 🏆 | 1.027 | 1.06 | 26,653 | 96.2% | -| **4** | 0.3731 🏆 | 1.295 | 1.67 | 30,233 | 62.7% | +| Context | Variant | Avg Entropy | Perplexity | Branching Factor | Unique Contexts | Predictability | +|---------|---------|-------------|------------|------------------|-----------------|----------------| +| **1** | Word | 0.4921 | 1.406 | 2.63 | 5,466 | 50.8% | +| **1** | Subword | 1.0948 | 2.136 | 7.84 | 226 | 0.0% | +| **2** | Word | 0.1702 | 1.125 | 1.32 | 14,200 | 83.0% | +| **2** | Subword | 1.1295 | 2.188 | 5.29 | 1,769 | 0.0% | +| **3** | Word | 0.0593 | 1.042 | 1.09 | 18,551 | 94.1% | +| **3** | Subword | 0.7380 | 1.668 | 2.81 | 9,336 | 26.2% | +| **4** | Word | 0.0213 🏆 | 1.015 | 1.03 | 19,968 | 97.9% | +| **4** | Subword | 0.3911 | 1.311 | 1.72 | 26,134 | 60.9% | + +### Generated Text Samples (Word-based) + +Below are text samples generated from each word-based Markov chain model: + +**Context Size 1:** + +1. `i islan guĂ„han si nanĂ„ ña ti ha nĂ„ an i senso ine giya guĂ„han i` +2. `na tataogues na petsona siha manma Ă„ ñao i kotturan ñiha gi i dos botkan ni` +3. `gi sankattan na populasion i mina tres manggimen tuba ginen i taotao ya ma ganna i` + +**Context Size 2:** + +1. `i sengsong nu i senso unidos` +2. `nu i proteksion i tano jesukristo gi fecha ni 25 disiembre` +3. `populasion i sengsong nu i senso unidos` + +**Context Size 3:** + +1. `na populasion i sengsong nu i senso unidos` +2. `na tataogues na populasion i sengsong nu i senso unidos` +3. `tataogues na populasion i sengsong nu i senso unidos` + +**Context Size 4:** + +1. `na tataogues na populasion i sengsong nu i senso unidos` +2. `tataogues na populasion i sengsong nu i senso unidos` +3. `i sengsong nu i senso unidos` -### Generated Text Samples -Below are text samples generated from each Markov chain model: +### Generated Text Samples (Subword-based) + +Below are text samples generated from each subword-based Markov chain model: **Context Size 1:** -1. `i sengsong dong hoi . guĂ„ha 1 ] yan i isla ni mineddong - spike barker` -2. `. guĂ„ha 103 , un lepblo - ña , krasnodar ) minisengsĂłng / web . katigoria` -3. `' huyong as a ' gue ' aotoridat para otro na populasion i senso 2010 .` +1. `_eyanso'_giterge` +2. `asipa_dinakoso_m` +3. `nsi_nandiki_u_pa` **Context Size 2:** -1. `katigoria : dangkulo katigoria : chile` -2. `. katigoria : dangkulo katigoria : china katigoria : yeogrĂ„fia katigoria : dangkulo katigoria : esta...` -3. `i sengsong nu i senso 2010 . katigoria : estados unidos . guĂ„ha 4 154 200 na` +1. `a_Ă„chokkas_na_kri` +2. `i_ta_magu_gi_i_ha` +3. `na'neho_"thunidos` **Context Size 3:** -1. `. katigoria : tiempo` -2. `nu i senso 2000 . katigoria : dangkulo katigoria : estados unidos` -3. `na tataogues na populasion i sengsong nu i senso 2016 ( ine ) . katigoria : dangkulo katigoria` +1. `_i_caste_pies_dang` +2. `_na_populasifiku)_` +3. `na_tan_atten-ñiha_` **Context Size 4:** -1. `na tataogues na populasion i sengsong nu i senso 2010 . katigoria : dangkulo katigoria : estados uni...` -2. `tataogues na populasion i sengsong nu i senso 2012 . katigoria : chapan katigoria : dangkulo` -3. `i sengsong nu i senso 2014 . katigoria : dangkulo katigoria : estados unidos` +1. `_na_po'lu_na_aterit` +2. `_gi_estorio_ni'_kad` +3. `song-song_nu_i_akti` ### Key Findings -- **Best Predictability:** Context-4 with 96.2% predictability +- **Best Predictability:** Context-4 (word) with 97.9% predictability - **Branching Factor:** Decreases with context size (more deterministic) -- **Memory Trade-off:** Larger contexts require more storage (30,233 contexts) +- **Memory Trade-off:** Larger contexts require more storage (26,134 contexts) - **Recommendation:** Context-3 or Context-4 for text generation --- @@ -236,64 +306,64 @@ Below are text samples generated from each Markov chain model: | Metric | Value | |--------|-------| -| Vocabulary Size | 2,085 | -| Total Tokens | 25,270 | -| Mean Frequency | 12.12 | +| Vocabulary Size | 1,918 | +| Total Tokens | 22,697 | +| Mean Frequency | 11.83 | | Median Frequency | 3 | -| Frequency Std Dev | 73.61 | +| Frequency Std Dev | 73.74 | ### Most Common Words | Rank | Word | Frequency | |------|------|-----------| -| 1 | i | 2,329 | -| 2 | na | 1,512 | -| 3 | gi | 982 | -| 4 | katigoria | 774 | -| 5 | estados | 459 | -| 6 | unidos | 448 | -| 7 | yan | 440 | -| 8 | sengsong | 370 | -| 9 | guĂ„ha | 356 | -| 10 | ni | 339 | +| 1 | i | 2,327 | +| 2 | na | 1,513 | +| 3 | gi | 972 | +| 4 | unidos | 448 | +| 5 | yan | 436 | +| 6 | sengsong | 370 | +| 7 | guĂ„ha | 356 | +| 8 | ni | 339 | +| 9 | nu | 335 | +| 10 | populasion | 333 | ### Least Common Words (from vocabulary) | Rank | Word | Frequency | |------|------|-----------| -| 1 | sĂ€ger | 2 | -| 2 | ett | 2 | -| 3 | sĂ„ | 2 | -| 4 | du | 2 | -| 5 | skate | 2 | -| 6 | med | 2 | -| 7 | smaskiga | 2 | -| 8 | löken | 2 | -| 9 | tychy | 2 | -| 10 | museon | 2 | +| 1 | av | 2 | +| 2 | berit | 2 | +| 3 | larsson | 2 | +| 4 | hemliga | 2 | +| 5 | tycker | 2 | +| 6 | att | 2 | +| 7 | var | 2 | +| 8 | rolig | 2 | +| 9 | ett | 2 | +| 10 | du | 2 | ### Zipf's Law Analysis | Metric | Value | |--------|-------| -| Zipf Coefficient | 0.9652 | -| RÂČ (Goodness of Fit) | 0.986639 | +| Zipf Coefficient | 0.9581 | +| RÂČ (Goodness of Fit) | 0.986461 | | Adherence Quality | **excellent** | ### Coverage Analysis | Top N Words | Coverage | |-------------|----------| -| Top 100 | 62.8% | -| Top 1,000 | 90.4% | +| Top 100 | 63.1% | +| Top 1,000 | 91.3% | | Top 5,000 | 0.0% | | Top 10,000 | 0.0% | ### Key Findings -- **Zipf Compliance:** RÂČ=0.9866 indicates excellent adherence to Zipf's law -- **High Frequency Dominance:** Top 100 words cover 62.8% of corpus -- **Long Tail:** -7,915 words needed for remaining 100.0% coverage +- **Zipf Compliance:** RÂČ=0.9865 indicates excellent adherence to Zipf's law +- **High Frequency Dominance:** Top 100 words cover 63.1% of corpus +- **Long Tail:** -8,082 words needed for remaining 100.0% coverage --- ## 5. Word Embeddings Evaluation @@ -306,24 +376,108 @@ Below are text samples generated from each Markov chain model: ![t-SNE Sentences](visualizations/tsne_sentences.png) -### Model Comparison -| Model | Vocab Size | Dimension | Avg Norm | Std Norm | Isotropy | -|-------|------------|-----------|----------|----------|----------| -| **mono_32d** | 546 | 32 | 1.807 | 0.436 | 0.0314 🏆 | -| **mono_64d** | 546 | 64 | 1.773 | 0.400 | 0.0096 | -| **mono_128d** | 546 | 128 | 1.779 | 0.401 | 0.0020 | -| **embeddings_enhanced** | 0 | 0 | 0.000 | 0.000 | 0.0000 | +### 5.1 Cross-Lingual Alignment + +> *Note: Multilingual alignment visualization not available for this language.* + + +### 5.2 Model Comparison + +| Model | Dimension | Isotropy | Semantic Density | Alignment R@1 | Alignment R@10 | +|-------|-----------|----------|------------------|---------------|----------------| +| **mono_32d** | 32 | 0.0518 🏆 | 0.6801 | N/A | N/A | +| **mono_64d** | 64 | 0.0071 | 0.8792 | N/A | N/A | +| **mono_128d** | 128 | 0.0017 | 0.8741 | N/A | N/A | ### Key Findings -- **Best Isotropy:** mono_32d with 0.0314 (more uniform distribution) -- **Dimension Trade-off:** Higher dimensions capture more semantics but reduce isotropy -- **Vocabulary Coverage:** All models cover 546 words -- **Recommendation:** 100d for balanced semantic capture and efficiency +- **Best Isotropy:** mono_32d with 0.0518 (more uniform distribution) +- **Semantic Density:** Average pairwise similarity of 0.8111. Lower values indicate better semantic separation. +- **Alignment Quality:** No aligned models evaluated in this run. +- **Recommendation:** 128d aligned for best cross-lingual performance + +--- +## 6. Morphological Analysis (Experimental) + +> ⚠ **Warning:** This language shows low morphological productivity. The statistical signals used for this analysis may be noisy or less reliable than for morphologically rich languages. + +This section presents an automated morphological analysis derived from the statistical divergence between word-level and subword-level models. By analyzing where subword predictability spikes and where word-level coverage fails, we can infer linguistic structures without supervised data. + +### 6.1 Productivity & Complexity + +| Metric | Value | Interpretation | Recommendation | +|--------|-------|----------------|----------------| +| Productivity Index | **0.000** | Low morphological productivity | ⚠ Likely unreliable | +| Idiomaticity Gap | **-1.000** | Low formulaic content | - | + +### 6.2 Affix Inventory (Productive Units) + +These are the most productive prefixes and suffixes identified by sampling the vocabulary for global substitutability patterns. A unit is considered an affix if stripping it leaves a valid stem that appears in other contexts. + +#### Productive Prefixes +| Prefix | Examples | +|--------|----------| +| `-ma` | matematika, matai, mansen | + +#### Productive Suffixes +| Suffix | Examples | +|--------|----------| +| `-a` | matematika, kana, bĂ„nda | +| `-n` | sanhayan, monhayan, fan | +| `-on` | organisasion, aplikasion, adelanton | +| `-an` | sanhayan, monhayan, fan | +| `-ia` | bibliografia, termania, indonesia | +| `-ion` | organisasion, aplikasion, atministrasion | + +### 6.3 Bound Stems (Lexical Roots) + +Bound stems are high-frequency subword units that are semantically cohesive but rarely appear as standalone words. These often correspond to the 'core' of a word that requires inflection or derivation to be valid. + +*No significant bound stems detected.* + + +### 6.4 Affix Compatibility (Co-occurrence) + +This table shows which prefixes and suffixes most frequently co-occur on the same stems, revealing the 'stacking' rules of the language's morphology. + +| Prefix | Suffix | Frequency | Examples | +|--------|--------|-----------|----------| +| `-ma` | `-a` | 17 words | matematika, manfa | +| `-ma` | `-n` | 13 words | mansen, mandarin | +| `-ma` | `-an` | 6 words | manguayan, man | +| `-ma` | `-on` | 4 words | matutuhon, madison | +| `-ma` | `-ia` | 1 words | malaysia, maria | + +### 6.5 Recursive Morpheme Segmentation + +Using **Recursive Hierarchical Substitutability**, we decompose complex words into their constituent morphemes. This approach handles nested affixes (e.g., `prefix-prefix-root-suffix`). + +| Word | Suggested Split | Confidence | Stem | +|------|-----------------|------------|------| +| makonsidera | **`ma-konsidera`** | 4.5 | `konsidera` | +| matutuhon | **`ma-tutuh-on`** | 3.0 | `tutuh` | +| manguayan | **`ma-nguay-an`** | 3.0 | `nguay` | +| pennsylvania | **`pennsylv-an-ia`** | 3.0 | `pennsylv` | +| manmatutuhon | **`ma-nmatutuh-on`** | 3.0 | `nmatutuh` | +| machulijan | **`ma-chulij-an`** | 3.0 | `chulij` | +| manofisinan | **`ma-nofisin-an`** | 3.0 | `nofisin` | +| masasangan | **`ma-sasang-an`** | 3.0 | `sasang` | +| matematika | **`ma-tematika`** | 1.5 | `tematika` | +| organisasion | **`organisas-ion`** | 1.5 | `organisas` | +| aplikasion | **`aplikas-ion`** | 1.5 | `aplikas` | +| adelanton | **`adelant-on`** | 1.5 | `adelant` | +| bibliografia | **`bibliograf-ia`** | 1.5 | `bibliograf` | +| manamerikanu | **`ma-namerikanu`** | 1.5 | `namerikanu` | +| indonesia | **`indones-ia`** | 1.5 | `indones` | + +### 6.6 Linguistic Interpretation + +> **Automated Insight:** +The language CH appears to be more isolating or has a highly fixed vocabulary. Word-level models perform nearly as well as subword models, indicating fewer productive morphological processes. --- -## 6. Summary & Recommendations +## 7. Summary & Recommendations ![Performance Dashboard](visualizations/performance_dashboard.png) @@ -331,11 +485,12 @@ Below are text samples generated from each Markov chain model: | Component | Recommended | Rationale | |-----------|-------------|-----------| -| Tokenizer | **32k BPE** | Best compression (4.11x) with low UNK rate | -| N-gram | **5-gram** | Lowest perplexity (262) | -| Markov | **Context-4** | Highest predictability (96.2%) | +| Tokenizer | **16k BPE** | Best compression (4.24x) | +| N-gram | **3-gram** | Lowest perplexity (134) | +| Markov | **Context-4** | Highest predictability (97.9%) | | Embeddings | **100d** | Balanced semantic capture and isotropy | + --- ## Appendix: Metrics Glossary & Interpretation Guide @@ -525,7 +680,8 @@ If you use these models in your research, please cite: author = {Kamali, Omar}, title = {Wikilangs: Open NLP Models for Wikipedia Languages}, year = {2025}, - publisher = {HuggingFace}, + doi = {10.5281/zenodo.18073153}, + publisher = {Zenodo}, url = {https://huggingface.co/wikilangs} institution = {Omneity Labs} } @@ -541,7 +697,8 @@ MIT License - Free for academic and commercial use. - đŸ€— Models: [huggingface.co/wikilangs](https://huggingface.co/wikilangs) - 📊 Data: [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) - đŸ‘€ Author: [Omar Kamali](https://huggingface.co/omarkamali) +- đŸ€ Sponsor: [Featherless AI](https://featherless.ai) --- *Generated by Wikilangs Models Pipeline* -*Report Date: 2025-12-28 22:40:52* +*Report Date: 2026-01-03 10:06:23* diff --git a/models/embeddings/monolingual/ch_128d.bin b/models/embeddings/monolingual/ch_128d.bin index e8acab7497bd04c045453725d31353462c88c2f4..2bd44c5203ee1c93df2e60e05553cb348f2fb19b 100644 --- a/models/embeddings/monolingual/ch_128d.bin +++ b/models/embeddings/monolingual/ch_128d.bin @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:c70139eede0356c2aeb600acc0358b3d5553ac4276060a02b299bcffe2777ed7 -size 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+ "encoding_method": "rope", + "dim": 64 }, - "vocab_size": 546 + "vocab_size": 515 } \ No newline at end of file diff --git a/models/subword_markov/ch_markov_ctx1_subword.parquet b/models/subword_markov/ch_markov_ctx1_subword.parquet index 714fc32bfd27c5e64e4e0b088e25b11517ee7d99..da132691e79b06698235c2bc734046065e1e335f 100644 --- a/models/subword_markov/ch_markov_ctx1_subword.parquet +++ b/models/subword_markov/ch_markov_ctx1_subword.parquet @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:2d8bcbadf13f8c04b3bf774db4566c21081c309daade48a05ab547c4d55019eb -size 18449 +oid sha256:f6da9b7f5e8c16da9d614a2d3c0d921e4219da4002fa71393d3c02e1a9f995e1 +size 17474 diff --git a/models/subword_markov/ch_markov_ctx1_subword_metadata.json b/models/subword_markov/ch_markov_ctx1_subword_metadata.json index 729eea5b127242c6727acd919f8920775b135f68..434f1a80694e12561cbe597b3c8c517e256427ba 100644 --- a/models/subword_markov/ch_markov_ctx1_subword_metadata.json +++ b/models/subword_markov/ch_markov_ctx1_subword_metadata.json @@ -2,6 +2,6 @@ "context_size": 1, "variant": "subword", "language": "ch", - "unique_contexts": 235, - "total_transitions": 176029 + "unique_contexts": 226, + "total_transitions": 151624 } \ No newline at end of file diff --git a/models/subword_markov/ch_markov_ctx2_subword.parquet b/models/subword_markov/ch_markov_ctx2_subword.parquet index b3bbc23ccf439705130ab3941363e40cb961445f..5f12bd59189bfb5892b74cffb6e5c53175e09ea3 100644 --- a/models/subword_markov/ch_markov_ctx2_subword.parquet +++ b/models/subword_markov/ch_markov_ctx2_subword.parquet @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:392ca8b7bc544928287f25cc942dc9006c35872f09a7d9b575d39b713087ca72 -size 77639 +oid sha256:264f038954ff6478301dcba734b78924744419ec3051afb803ea4a1d438a05f0 +size 67586 diff --git a/models/subword_markov/ch_markov_ctx2_subword_metadata.json b/models/subword_markov/ch_markov_ctx2_subword_metadata.json index 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