diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..5128fe6dbab6ef0227c02cffa9ad5350ab6c4dd6 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,9 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +visualizations/embedding_similarity.png filter=lfs diff=lfs merge=lfs -text +visualizations/performance_dashboard.png filter=lfs diff=lfs merge=lfs -text +visualizations/position_encoding_comparison.png filter=lfs diff=lfs merge=lfs -text +visualizations/tsne_sentences.png filter=lfs diff=lfs merge=lfs -text +visualizations/tsne_words.png filter=lfs diff=lfs merge=lfs -text +visualizations/zipf_law.png filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..2f11075563213c48e624d727603919545288327a --- /dev/null +++ b/README.md @@ -0,0 +1,558 @@ +--- +language: ceb +language_name: CEB +language_family: austronesian_philippine_central +tags: + - wikilangs + - nlp + - tokenizer + - embeddings + - n-gram + - markov + - wikipedia + - monolingual + - family-austronesian_philippine_central +license: mit +library_name: wikilangs +pipeline_tag: feature-extraction +datasets: + - omarkamali/wikipedia-monthly +dataset_info: + name: wikipedia-monthly + description: Monthly snapshots of Wikipedia articles across 300+ languages +metrics: + - name: best_compression_ratio + type: compression + value: 4.023 + - name: best_isotropy + type: isotropy + value: 0.7600 + - name: vocabulary_size + type: vocab + value: 1000000 +generated: 2025-12-28 +--- + +# CEB - Wikilangs Models +## Comprehensive Research Report & Full Ablation Study + +This repository contains NLP models trained and evaluated by Wikilangs, specifically on **CEB** Wikipedia data. +We analyze tokenizers, n-gram models, Markov chains, vocabulary statistics, and word embeddings. + +## 📋 Repository Contents + +### Models & Assets + +- Tokenizers (8k, 16k, 32k, 64k) +- N-gram models (2, 3, 4-gram) +- Markov chains (context of 1, 2, 3 and 4) +- Subword N-gram and Markov chains +- Embeddings in various sizes and dimensions +- Language Vocabulary +- Language Statistics +![Performance Dashboard](visualizations/performance_dashboard.png) + +### Analysis and Evaluation + +- [1. Tokenizer Evaluation](#1-tokenizer-evaluation) +- [2. N-gram Model Evaluation](#2-n-gram-model-evaluation) +- [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) +- [Metrics Glossary](#appendix-metrics-glossary--interpretation-guide) +- [Visualizations Index](#visualizations-index) + +--- +## 1. Tokenizer Evaluation + +![Tokenizer Compression](visualizations/tokenizer_compression.png) + +### Results + +| Vocab Size | Compression | Avg Token Len | UNK Rate | Total Tokens | +|------------|-------------|---------------|----------|--------------| +| **8k** | 3.166x | 3.08 | 0.4963% | 288,512 | +| **16k** | 3.546x | 3.45 | 0.5559% | 257,589 | +| **32k** | 3.786x | 3.68 | 0.5935% | 241,268 | +| **64k** | 4.023x 🏆 | 3.91 | 0.6307% | 227,048 | + +### Tokenization Examples + +Below are sample sentences tokenized with each vocabulary size: + +**Sample 1:** `Ang 1215 (MCCXV) mao ang usa ka tuig sa kalendaryong Hulyano. Ang 1215 maoy usa ...` + +| Vocab | Tokens | Count | +|-------|--------|-------| +| 8k | `▁ang ▁ 1 2 1 5 ▁( m cc x ... (+46 more)` | 56 | +| 16k | `▁ang ▁ 1 2 1 5 ▁( m cc x ... (+39 more)` | 49 | +| 32k | `▁ang ▁ 1 2 1 5 ▁( m cc x ... (+38 more)` | 48 | +| 64k | `▁ang ▁ 1 2 1 5 ▁( mcc xv ) ... (+36 more)` | 46 | + +**Sample 2:** `Villy, Ardennes + Villy, Yonne` + +| Vocab | Tokens | Count | +|-------|--------|-------| +| 8k | `▁v illy , ▁ardennes ▁v illy , ▁yonne` | 8 | +| 16k | `▁v illy , ▁ardennes ▁v illy , ▁yonne` | 8 | +| 32k | `▁v illy , ▁ardennes ▁v illy , ▁yonne` | 8 | +| 64k | `▁v illy , ▁ardennes ▁v illy , ▁yonne` | 8 | + +**Sample 3:** `Si Doubleday nga ang hamubong pangalan niini nag tudlo kang: + + Henry Doubleday + ...` + +| Vocab | Tokens | Count | +|-------|--------|-------| +| 8k | `▁si ▁do ubl ed ay ▁nga ▁ang ▁hamubong ▁pangalan ▁niini ... (+23 more)` | 33 | +| 16k | `▁si ▁do ubl ed ay ▁nga ▁ang ▁hamubong ▁pangalan ▁niini ... (+23 more)` | 33 | +| 32k | `▁si ▁do ubl ed ay ▁nga ▁ang ▁hamubong ▁pangalan ▁niini ... (+23 more)` | 33 | +| 64k | `▁si ▁doubleday ▁nga ▁ang ▁hamubong ▁pangalan ▁niini ▁nag ▁tudlo ▁kang ... (+11 more)` | 21 | + + +### Key Findings + +- **Best Compression:** 64k achieves 4.023x compression +- **Lowest UNK Rate:** 8k with 0.4963% unknown tokens +- **Trade-off:** Larger vocabularies improve compression but increase model size +- **Recommendation:** 32k vocabulary provides optimal balance for production use + +--- +## 2. N-gram Model Evaluation + +![N-gram Perplexity](visualizations/ngram_perplexity.png) + +![N-gram Coverage](visualizations/ngram_coverage.png) + +### Results + +| N-gram | Perplexity | Entropy | Unique N-grams | Top-100 Coverage | Top-1000 Coverage | +|--------|------------|---------|----------------|------------------|-------------------| +| **2-gram** | 2,483 🏆 | 11.28 | 3,473,094 | 35.1% | 79.1% | +| **2-gram** | 262 🏆 | 8.03 | 32,514 | 66.2% | 99.4% | +| **3-gram** | 6,019 | 12.56 | 8,522,415 | 25.6% | 70.1% | +| **3-gram** | 1,492 | 10.54 | 210,233 | 33.0% | 80.5% | +| **4-gram** | 10,956 | 13.42 | 18,410,507 | 23.0% | 64.2% | +| **4-gram** | 4,346 | 12.09 | 1,121,715 | 21.0% | 64.2% | + +### Top 5 N-grams by Size + +**2-grams:** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `. ang` | 22,734,270 | +| 2 | `. ,` | 21,897,297 | +| 3 | `kategoriya :` | 13,463,660 | +| 4 | `, sa` | 12,991,378 | +| 5 | `° c` | 8,094,599 | + +**3-grams:** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `mga gi basihan` | 5,576,921 | +| 2 | `ang mga gi` | 5,576,920 | +| 3 | `gi basihan niini` | 5,576,918 | +| 4 | `c . ang` | 5,477,045 | +| 5 | `° c .` | 5,398,278 | + +**4-grams:** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `ang mga gi basihan` | 5,576,919 | +| 2 | `mga gi basihan niini` | 5,576,918 | +| 3 | `, sa ° c` | 5,392,546 | +| 4 | `° c . ang` | 4,999,337 | +| 5 | `geonames . org (` | 3,664,288 | + + +### Key Findings + +- **Best Perplexity:** 2-gram with 262 +- **Entropy Trend:** Decreases with larger n-grams (more predictable) +- **Coverage:** Top-1000 patterns cover ~64% of corpus +- **Recommendation:** 4-gram or 5-gram for best predictive performance + +--- +## 3. Markov Chain Evaluation + +![Markov Entropy](visualizations/markov_entropy.png) + +![Markov Branching](visualizations/markov_branching.png) + +### Results + +| Context | Avg Entropy | Perplexity | Branching Factor | Unique Contexts | Predictability | +|---------|-------------|------------|------------------|-----------------|----------------| +| **1** | 1.4458 | 2.724 | 7.69 | 2,678,400 | 0.0% | +| **1** | 1.9800 | 3.945 | 15.62 | 7,465 | 0.0% | +| **2** | 0.5102 | 1.424 | 2.56 | 20,310,820 | 49.0% | +| **2** | 0.7197 | 1.647 | 3.95 | 116,477 | 28.0% | +| **3** | 0.2244 | 1.168 | 1.69 | 51,557,317 | 77.6% | +| **3** | 0.6664 | 1.587 | 3.76 | 459,408 | 33.4% | +| **4** | 0.1061 🏆 | 1.076 | 1.36 | 86,676,271 | 89.4% | +| **4** | 0.6423 🏆 | 1.561 | 3.28 | 1,725,840 | 35.8% | + +### Generated Text Samples + +Below are text samples generated from each Markov chain model: + +**Context Size 1:** + +1. `. ang kinabugnawan hulyo , sa dagat nga sama niini [ piz alpetta . lepindex :` +2. `sa ka mga gi basihan niini [ sungai gitam ( cc - by ) ] ;` +3. `, ug km sa congo aka valley , w . systema dipterorum ( roskov y .` + +**Context Size 2:** + +1. `. ang kinahabogang dapit sa mao gihapon nga ngalan , tan - awa ang la laigne sa` +2. `. , orrell t . , jourbook : entomologist volume : 23 : 76 . yu d` +3. `kategoriya : mga lungsod sa palibot dunay gihabogon nga ka metro ug km sa sidlakan sa nuaf` + +**Context Size 3:** + +1. `mga gi basihan niini [ wādī khalīl ] sa [ geonames . org ( cc - by )` +2. `ang mga gi basihan niini [ lambskär ] sa [ geonames . org ( cc - by )` +3. `gi basihan niini [ matinsaari ] sa [ geonames . org ( cc - by ) ] ;` + +**Context Size 4:** + +1. `ang mga gi basihan niini [ jar - e takhtah chinār ] sa [ geonames . org ( cc` +2. `mga gi basihan niini [ bustøkniben ] sa [ geonames . org ( cc - by ) ] ;` +3. `, sa ° c . ang kinainitan nga bulan hunyo , sa milimetro nga ulan , ug ang kinaugahan` + + +### Key Findings + +- **Best Predictability:** Context-4 with 89.4% predictability +- **Branching Factor:** Decreases with context size (more deterministic) +- **Memory Trade-off:** Larger contexts require more storage (1,725,840 contexts) +- **Recommendation:** Context-3 or Context-4 for text generation + +--- +## 4. Vocabulary Analysis + +![Zipf's Law](visualizations/zipf_law.png) + +![Top Words](visualizations/top20_words.png) + +![Coverage Curve](visualizations/vocab_coverage.png) + +### Statistics + +| Metric | Value | +|--------|-------| +| Vocabulary Size | 1,000,000 | +| Total Tokens | 845,775,833 | +| Mean Frequency | 845.78 | +| Median Frequency | 13 | +| Frequency Std Dev | 118478.53 | + +### Most Common Words + +| Rank | Word | Frequency | +|------|------|-----------| +| 1 | sa | 95,127,836 | +| 2 | ang | 48,190,139 | +| 3 | nga | 26,124,610 | +| 4 | mga | 14,558,326 | +| 5 | kategoriya | 13,463,779 | +| 6 | ug | 11,614,906 | +| 7 | c | 9,761,722 | +| 8 | ni | 8,490,927 | +| 9 | niini | 7,626,081 | +| 10 | palibot | 7,306,538 | + +### Least Common Words (from vocabulary) + +| Rank | Word | Frequency | +|------|------|-----------| +| 1 | mentang | 8 | +| 2 | padangalipan | 8 | +| 3 | palawangan | 8 | +| 4 | patokoan | 8 | +| 5 | patongai | 8 | +| 6 | pentujangan | 8 | +| 7 | pessailean | 8 | +| 8 | petampe | 8 | +| 9 | petape | 8 | +| 10 | pillong | 8 | + +### Zipf's Law Analysis + +| Metric | Value | +|--------|-------| +| Zipf Coefficient | 1.4546 | +| R² (Goodness of Fit) | 0.992518 | +| Adherence Quality | **excellent** | + +### Coverage Analysis + +| Top N Words | Coverage | +|-------------|----------| +| Top 100 | 60.7% | +| Top 1,000 | 89.2% | +| Top 5,000 | 93.8% | +| Top 10,000 | 95.1% | + +### Key Findings + +- **Zipf Compliance:** R²=0.9925 indicates excellent adherence to Zipf's law +- **High Frequency Dominance:** Top 100 words cover 60.7% of corpus +- **Long Tail:** 990,000 words needed for remaining 4.9% coverage + +--- +## 5. Word Embeddings Evaluation + +![Embedding Isotropy](visualizations/embedding_isotropy.png) + +![Similarity Matrix](visualizations/embedding_similarity.png) + +![t-SNE Words](visualizations/tsne_words.png) + +![t-SNE Sentences](visualizations/tsne_sentences.png) + +### Model Comparison + +| Model | Vocab Size | Dimension | Avg Norm | Std Norm | Isotropy | +|-------|------------|-----------|----------|----------|----------| +| **mono_32d** | 1,545,627 | 32 | 5.888 | 1.742 | 0.7600 🏆 | +| **mono_64d** | 1,545,627 | 64 | 6.390 | 1.467 | 0.7319 | +| **mono_128d** | 1,545,627 | 128 | 6.832 | 1.294 | 0.6617 | +| **embeddings_enhanced** | 0 | 0 | 0.000 | 0.000 | 0.0000 | + +### Key Findings + +- **Best Isotropy:** mono_32d with 0.7600 (more uniform distribution) +- **Dimension Trade-off:** Higher dimensions capture more semantics but reduce isotropy +- **Vocabulary Coverage:** All models cover 1,545,627 words +- **Recommendation:** 100d for balanced semantic capture and efficiency + +--- +## 6. Summary & Recommendations + +![Performance Dashboard](visualizations/performance_dashboard.png) + +### Production Recommendations + +| Component | Recommended | Rationale | +|-----------|-------------|-----------| +| Tokenizer | **32k BPE** | Best compression (4.02x) with low UNK rate | +| N-gram | **5-gram** | Lowest perplexity (262) | +| Markov | **Context-4** | Highest predictability (89.4%) | +| Embeddings | **100d** | Balanced semantic capture and isotropy | + +--- +## Appendix: Metrics Glossary & Interpretation Guide + +This section provides definitions, intuitions, and guidance for interpreting the metrics used throughout this report. + +### Tokenizer Metrics + +**Compression Ratio** +> *Definition:* The ratio of characters to tokens (chars/token). Measures how efficiently the tokenizer represents text. +> +> *Intuition:* Higher compression means fewer tokens needed to represent the same text, reducing sequence lengths for downstream models. A 3x compression means ~3 characters per token on average. +> +> *What to seek:* Higher is generally better for efficiency, but extremely high compression may indicate overly aggressive merging that loses morphological information. + +**Average Token Length (Fertility)** +> *Definition:* Mean number of characters per token produced by the tokenizer. +> +> *Intuition:* Reflects the granularity of tokenization. Longer tokens capture more context but may struggle with rare words; shorter tokens are more flexible but increase sequence length. +> +> *What to seek:* Balance between 2-5 characters for most languages. Arabic/morphologically-rich languages may benefit from slightly longer tokens. + +**Unknown Token Rate (OOV Rate)** +> *Definition:* Percentage of tokens that map to the unknown/UNK token, indicating words the tokenizer cannot represent. +> +> *Intuition:* Lower OOV means better vocabulary coverage. High OOV indicates the tokenizer encounters many unseen character sequences. +> +> *What to seek:* Below 1% is excellent; below 5% is acceptable. BPE tokenizers typically achieve very low OOV due to subword fallback. + +### N-gram Model Metrics + +**Perplexity** +> *Definition:* Measures how "surprised" the model is by test data. Mathematically: 2^(cross-entropy). Lower values indicate better prediction. +> +> *Intuition:* If perplexity is 100, the model is as uncertain as if choosing uniformly among 100 options at each step. A perplexity of 10 means effectively choosing among 10 equally likely options. +> +> *What to seek:* Lower is better. Perplexity decreases with larger n-grams (more context). Values vary widely by language and corpus size. + +**Entropy** +> *Definition:* Average information content (in bits) needed to encode the next token given the context. Related to perplexity: perplexity = 2^entropy. +> +> *Intuition:* High entropy means high uncertainty/randomness; low entropy means predictable patterns. Natural language typically has entropy between 1-4 bits per character. +> +> *What to seek:* Lower entropy indicates more predictable text patterns. Entropy should decrease as n-gram size increases. + +**Coverage (Top-K)** +> *Definition:* Percentage of corpus occurrences explained by the top K most frequent n-grams. +> +> *Intuition:* High coverage with few patterns indicates repetitive/formulaic text; low coverage suggests diverse vocabulary usage. +> +> *What to seek:* Depends on use case. For language modeling, moderate coverage (40-60% with top-1000) is typical for natural text. + +### Markov Chain Metrics + +**Average Entropy** +> *Definition:* Mean entropy across all contexts, measuring average uncertainty in next-word prediction. +> +> *Intuition:* Lower entropy means the model is more confident about what comes next. Context-1 has high entropy (many possible next words); Context-4 has low entropy (few likely continuations). +> +> *What to seek:* Decreasing entropy with larger context sizes. Very low entropy (<0.1) indicates highly deterministic transitions. + +**Branching Factor** +> *Definition:* Average number of unique next tokens observed for each context. +> +> *Intuition:* High branching = many possible continuations (flexible but uncertain); low branching = few options (predictable but potentially repetitive). +> +> *What to seek:* Branching factor should decrease with context size. Values near 1.0 indicate nearly deterministic chains. + +**Predictability** +> *Definition:* Derived metric: (1 - normalized_entropy) × 100%. Indicates how deterministic the model's predictions are. +> +> *Intuition:* 100% predictability means the next word is always certain; 0% means completely random. Real text falls between these extremes. +> +> *What to seek:* Higher predictability for text generation quality, but too high (>98%) may produce repetitive output. + +### Vocabulary & Zipf's Law Metrics + +**Zipf's Coefficient** +> *Definition:* The slope of the log-log plot of word frequency vs. rank. Zipf's law predicts this should be approximately -1. +> +> *Intuition:* A coefficient near -1 indicates the corpus follows natural language patterns where a few words are very common and most words are rare. +> +> *What to seek:* Values between -0.8 and -1.2 indicate healthy natural language distribution. Deviations may suggest domain-specific or artificial text. + +**R² (Coefficient of Determination)** +> *Definition:* Measures how well the linear fit explains the frequency-rank relationship. Ranges from 0 to 1. +> +> *Intuition:* R² near 1.0 means the data closely follows Zipf's law; lower values indicate deviation from expected word frequency patterns. +> +> *What to seek:* R² > 0.95 is excellent; > 0.99 indicates near-perfect Zipf adherence typical of large natural corpora. + +**Vocabulary Coverage** +> *Definition:* Cumulative percentage of corpus tokens accounted for by the top N words. +> +> *Intuition:* Shows how concentrated word usage is. If top-100 words cover 50% of text, the corpus relies heavily on common words. +> +> *What to seek:* Top-100 covering 30-50% is typical. Higher coverage indicates more repetitive text; lower suggests richer vocabulary. + +### Word Embedding Metrics + +**Isotropy** +> *Definition:* Measures how uniformly distributed vectors are in the embedding space. Computed as the ratio of minimum to maximum singular values. +> +> *Intuition:* High isotropy (near 1.0) means vectors spread evenly in all directions; low isotropy means vectors cluster in certain directions, reducing expressiveness. +> +> *What to seek:* Higher isotropy generally indicates better-quality embeddings. Values > 0.1 are reasonable; > 0.3 is good. Lower-dimensional embeddings tend to have higher isotropy. + +**Average Norm** +> *Definition:* Mean magnitude (L2 norm) of word vectors in the embedding space. +> +> *Intuition:* Indicates the typical "length" of vectors. Consistent norms suggest stable training; high variance may indicate some words are undertrained. +> +> *What to seek:* Relatively consistent norms across models. The absolute value matters less than consistency (low std deviation). + +**Cosine Similarity** +> *Definition:* Measures angular similarity between vectors, ranging from -1 (opposite) to 1 (identical direction). +> +> *Intuition:* Words with similar meanings should have high cosine similarity. This is the standard metric for semantic relatedness in embeddings. +> +> *What to seek:* Semantically related words should score > 0.5; unrelated words should be near 0. Synonyms often score > 0.7. + +**t-SNE Visualization** +> *Definition:* t-Distributed Stochastic Neighbor Embedding - a dimensionality reduction technique that preserves local structure for visualization. +> +> *Intuition:* Clusters in t-SNE plots indicate groups of semantically related words. Spread indicates vocabulary diversity; tight clusters suggest semantic coherence. +> +> *What to seek:* Meaningful clusters (e.g., numbers together, verbs together). Avoid over-interpreting distances - t-SNE preserves local, not global, structure. + +### General Interpretation Guidelines + +1. **Compare within model families:** Metrics are most meaningful when comparing models of the same type (e.g., 8k vs 64k tokenizer). +2. **Consider trade-offs:** Better performance on one metric often comes at the cost of another (e.g., compression vs. OOV rate). +3. **Context matters:** Optimal values depend on downstream tasks. Text generation may prioritize different metrics than classification. +4. **Corpus influence:** All metrics are influenced by corpus characteristics. Wikipedia text differs from social media or literature. +5. **Language-specific patterns:** Morphologically rich languages (like Arabic) may show different optimal ranges than analytic languages. + + +### Visualizations Index + +| Visualization | Description | +|---------------|-------------| +| Tokenizer Compression | Compression ratios by vocabulary size | +| Tokenizer Fertility | Average token length by vocabulary | +| Tokenizer OOV | Unknown token rates | +| Tokenizer Total Tokens | Total tokens by vocabulary | +| N-gram Perplexity | Perplexity by n-gram size | +| N-gram Entropy | Entropy by n-gram size | +| N-gram Coverage | Top pattern coverage | +| N-gram Unique | Unique n-gram counts | +| Markov Entropy | Entropy by context size | +| Markov Branching | Branching factor by context | +| Markov Contexts | Unique context counts | +| Zipf's Law | Frequency-rank distribution with fit | +| Vocab Frequency | Word frequency distribution | +| Top 20 Words | Most frequent words | +| Vocab Coverage | Cumulative coverage curve | +| Embedding Isotropy | Vector space uniformity | +| Embedding Norms | Vector magnitude distribution | +| Embedding Similarity | Word similarity heatmap | +| Nearest Neighbors | Similar words for key terms | +| t-SNE Words | 2D word embedding visualization | +| t-SNE Sentences | 2D sentence embedding visualization | +| Position Encoding | Encoding method comparison | +| Model Sizes | Storage requirements | +| Performance Dashboard | Comprehensive performance overview | + +--- +## About This Project + +### Data Source + +Models trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) - a monthly snapshot of Wikipedia articles across 300+ languages. + +### Project + +A project by **[Wikilangs](https://wikilangs.org)** - Open-source NLP models for every Wikipedia language. + +### Maintainer + +[Omar Kamali](https://omarkamali.com) - [Omneity Labs](https://omneitylabs.com) + +### Citation + +If you use these models in your research, please cite: + +```bibtex +@misc{wikilangs2025, + author = {Kamali, Omar}, + title = {Wikilangs: Open NLP Models for Wikipedia Languages}, + year = {2025}, + publisher = {HuggingFace}, + url = {https://huggingface.co/wikilangs} + institution = {Omneity Labs} +} +``` + +### License + +MIT License - Free for academic and commercial use. + +### Links + +- 🌐 Website: [wikilangs.org](https://wikilangs.org) +- 🤗 Models: [huggingface.co/wikilangs](https://huggingface.co/wikilangs) +- 📊 Data: [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) +- 👤 Author: [Omar Kamali](https://huggingface.co/omarkamali) +--- +*Generated by Wikilangs Models Pipeline* + +*Report Date: 2025-12-28 22:39:27* diff --git a/models/embeddings/monolingual/ceb_128d.bin b/models/embeddings/monolingual/ceb_128d.bin new file mode 100644 index 0000000000000000000000000000000000000000..c46c216ffe98062ed5c69b98bcb74747614ffc2e --- /dev/null +++ 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