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..340062d474965bf7bff9e4876295415ac377ac07 --- /dev/null +++ b/README.md @@ -0,0 +1,560 @@ +--- +language: bcl +language_name: BCL +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: 4.640 + - name: best_isotropy + type: isotropy + value: 0.8200 + - name: vocabulary_size + type: vocab + value: 139464 +generated: 2025-12-28 +--- + +# BCL - Wikilangs Models +## Comprehensive Research Report & Full Ablation Study + +This repository contains NLP models trained and evaluated by Wikilangs, specifically on **BCL** 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** | 3.849x | 3.74 | 0.0148% | 391,873 | +| **16k** | 4.154x | 4.04 | 0.0160% | 363,086 | +| **32k** | 4.421x | 4.30 | 0.0170% | 341,132 | +| **64k** | 4.640x 🏆 | 4.51 | 0.0178% | 325,066 | + +### Tokenization Examples + +Below are sample sentences tokenized with each vocabulary size: + +**Sample 1:** `REDIRECT An Sanduguan` + +| Vocab | Tokens | Count | +|-------|--------|-------| +| 8k | `▁re dire ct ▁an ▁sand ug uan` | 7 | +| 16k | `▁re dire ct ▁an ▁sand ug uan` | 7 | +| 32k | `▁re direct ▁an ▁sand uguan` | 5 | +| 64k | `▁re direct ▁an ▁sand uguan` | 5 | + +**Sample 2:** `An sarong komyun asin banwaan sa Provincia nin Cosenza sa rehiyon Calabria kan ...` + +| Vocab | Tokens | Count | +|-------|--------|-------| +| 8k | `▁an ▁sarong ▁komyun ▁asin ▁banwaan ▁sa ▁provincia ▁nin ▁cos enza ... (+6 more)` | 16 | +| 16k | `▁an ▁sarong ▁komyun ▁asin ▁banwaan ▁sa ▁provincia ▁nin ▁cosenza ▁sa ... (+5 more)` | 15 | +| 32k | `▁an ▁sarong ▁komyun ▁asin ▁banwaan ▁sa ▁provincia ▁nin ▁cosenza ▁sa ... (+5 more)` | 15 | +| 64k | `▁an ▁sarong ▁komyun ▁asin ▁banwaan ▁sa ▁provincia ▁nin ▁cosenza ▁sa ... (+5 more)` | 15 | + +**Sample 3:** `An sarong taon sa Gregoryanong kalendaryo. + + Enero +Pebrero + Marso +Abril + Mayo...` + +| Vocab | Tokens | Count | +|-------|--------|-------| +| 8k | `▁an ▁sarong ▁taon ▁sa ▁gregoryanong ▁kalendaryo . ▁enero ▁pebrero ▁marso ... (+9 more)` | 19 | +| 16k | `▁an ▁sarong ▁taon ▁sa ▁gregoryanong ▁kalendaryo . ▁enero ▁pebrero ▁marso ... (+9 more)` | 19 | +| 32k | `▁an ▁sarong ▁taon ▁sa ▁gregoryanong ▁kalendaryo . ▁enero ▁pebrero ▁marso ... (+9 more)` | 19 | +| 64k | `▁an ▁sarong ▁taon ▁sa ▁gregoryanong ▁kalendaryo . ▁enero ▁pebrero ▁marso ... (+9 more)` | 19 | + + +### Key Findings + +- **Best Compression:** 64k achieves 4.640x compression +- **Lowest UNK Rate:** 8k with 0.0148% 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** | 31,343 🏆 | 14.94 | 180,870 | 14.8% | 31.9% | +| **2-gram** | 262 🏆 | 8.03 | 8,566 | 68.4% | 98.8% | +| **3-gram** | 108,578 | 16.73 | 332,655 | 6.5% | 18.2% | +| **3-gram** | 2,285 | 11.16 | 64,437 | 30.5% | 69.8% | +| **4-gram** | 210,030 | 17.68 | 511,491 | 6.6% | 14.4% | +| **4-gram** | 13,379 | 13.71 | 345,622 | 17.2% | 41.0% | + +### Top 5 N-grams by Size + +**2-grams:** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `. an` | 41,934 | +| 2 | `sa mga` | 30,441 | +| 3 | `an mga` | 27,397 | +| 4 | `, asin` | 26,685 | +| 5 | `, an` | 24,473 | + +**3-grams:** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `kategorya : mga` | 16,293 | +| 2 | `. an mga` | 6,827 | +| 3 | `panluwas na takod` | 5,537 | +| 4 | `mga panluwas na` | 4,931 | +| 5 | `toltolan kategorya :` | 4,124 | + +**4-grams:** + +| Rank | N-gram | Count | +|------|--------|-------| +| 1 | `mga panluwas na takod` | 4,635 | +| 2 | `toltolan kategorya : mga` | 2,861 | +| 3 | `toltolan mga panluwas na` | 2,801 | +| 4 | `— — — —` | 2,785 | +| 5 | `. igwa ining sukol` | 2,225 | + + +### Key Findings + +- **Best Perplexity:** 2-gram with 262 +- **Entropy Trend:** Decreases with larger n-grams (more predictable) +- **Coverage:** Top-1000 patterns cover ~41% 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** | 0.6497 | 1.569 | 5.59 | 379,065 | 35.0% | +| **1** | 1.0949 | 2.136 | 6.69 | 6,611 | 0.0% | +| **2** | 0.3654 | 1.288 | 2.19 | 2,116,590 | 63.5% | +| **2** | 0.6035 | 1.519 | 3.87 | 44,194 | 39.6% | +| **3** | 0.1662 | 1.122 | 1.36 | 4,629,958 | 83.4% | +| **3** | 0.7134 | 1.640 | 3.84 | 171,168 | 28.7% | +| **4** | 0.0685 🏆 | 1.049 | 1.12 | 6,293,312 | 93.1% | +| **4** | 0.6518 🏆 | 1.571 | 2.96 | 656,881 | 34.8% | + +### Generated Text Samples + +Below are text samples generated from each Markov chain model: + +**Context Size 1:** + +1. `, sarong law jack white house of eastern europe award hale sa ' affaire jean nabiribid` +2. `sa mga padalian na english rosalía nagpirma sa banwaan kan ikasampulong kabilogan nin edukasyon si a...` +3. `na mga pagpreparar nin mayor na pigtuturing kan huring pararawitdawit , o tungkod " ) .` + +**Context Size 2:** + +1. `. an designadong zip code kaini iyo . susog ki milagros perfecto sanchez sa halipot na usipon` +2. `sa mga osipon sa pilipino na may titulong paghinanyog man , siya nagpoon na mag - audition` +3. `an mga botelya , pakete nin kakanon asin an responsibilidad . sa ibaba sa kabtang kaini .` + +**Context Size 3:** + +1. `kategorya : mga 2016 na kagadanan kategorya : mga tataramon na mansakan , iyo an pinagrekonstruhir n...` +2. `. an mga bitis nin manok sarong seryosong peligro nin pagkahilo sa susunod na taon huli sa iyo` +3. `panluwas na takod philatlas . com philippine standard geographic code local governance performance m...` + +**Context Size 4:** + +1. `mga panluwas na takod inactive volcanoes page ( arkibo ) kategorya : mga unibersidad asin kolehiyo s...` +2. `toltolan kategorya : mga armadong sanga kan mga partido pulitika kategorya : mga organisasyon natugd...` +3. `toltolan mga panluwas na takod philatlas . com philippine standard geographic code local governance ...` + + +### Key Findings + +- **Best Predictability:** Context-4 with 93.1% predictability +- **Branching Factor:** Decreases with context size (more deterministic) +- **Memory Trade-off:** Larger contexts require more storage (656,881 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 | 139,464 | +| Total Tokens | 6,306,562 | +| Mean Frequency | 45.22 | +| Median Frequency | 4 | +| Frequency Std Dev | 1750.04 | + +### Most Common Words + +| Rank | Word | Frequency | +|------|------|-----------| +| 1 | sa | 340,332 | +| 2 | na | 337,956 | +| 3 | an | 230,638 | +| 4 | kan | 226,231 | +| 5 | mga | 183,688 | +| 6 | nin | 132,320 | +| 7 | asin | 125,887 | +| 8 | sarong | 62,639 | +| 9 | si | 54,499 | +| 10 | the | 44,508 | + +### Least Common Words (from vocabulary) + +| Rank | Word | Frequency | +|------|------|-----------| +| 1 | zhaparova | 2 | +| 2 | altynbekov | 2 | +| 3 | wanatabe | 2 | +| 4 | megapaniki | 2 | +| 5 | kordon | 2 | +| 6 | sobringaran | 2 | +| 7 | khanid | 2 | +| 8 | ganish | 2 | +| 9 | archdioceseofcaceres | 2 | +| 10 | niceno | 2 | + +### Zipf's Law Analysis + +| Metric | Value | +|--------|-------| +| Zipf Coefficient | 1.0291 | +| R² (Goodness of Fit) | 0.993065 | +| Adherence Quality | **excellent** | + +### Coverage Analysis + +| Top N Words | Coverage | +|-------------|----------| +| Top 100 | 41.8% | +| Top 1,000 | 62.8% | +| Top 5,000 | 79.1% | +| Top 10,000 | 85.2% | + +### Key Findings + +- **Zipf Compliance:** R²=0.9931 indicates excellent adherence to Zipf's law +- **High Frequency Dominance:** Top 100 words cover 41.8% of corpus +- **Long Tail:** 129,464 words needed for remaining 14.8% 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** | 78,307 | 32 | 3.325 | 0.855 | 0.8200 🏆 | +| **mono_64d** | 78,307 | 64 | 3.871 | 0.899 | 0.8194 | +| **mono_128d** | 78,307 | 128 | 4.639 | 0.920 | 0.8065 | +| **embeddings_enhanced** | 0 | 0 | 0.000 | 0.000 | 0.0000 | + +### Key Findings + +- **Best Isotropy:** mono_32d with 0.8200 (more uniform distribution) +- **Dimension Trade-off:** Higher dimensions capture more semantics but reduce isotropy +- **Vocabulary Coverage:** All models cover 78,307 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 (4.64x) with low UNK rate | +| N-gram | **5-gram** | Lowest perplexity (262) | +| Markov | **Context-4** | Highest predictability (93.1%) | +| 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 00:25:48* diff --git a/models/embeddings/monolingual/bcl_128d.bin b/models/embeddings/monolingual/bcl_128d.bin new file mode 100644 index 0000000000000000000000000000000000000000..0916e099e7622b1c14a31d5261b90888ea2a7987 --- /dev/null +++ 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