Upload all models and assets for fr (latest)

.gitattributes

@@ -33,3 +33,10 @@ 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/embedding_tsne_multilingual.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

README.md

@@ -0,0 +1,230 @@
+---
+language: fr
+language_name: French
+language_family: romance_galloitalic
+tags:
+  - wikilangs
+  - nlp
+  - tokenizer
+  - embeddings
+  - n-gram
+  - markov
+  - wikipedia
+  - feature-extraction
+  - sentence-similarity
+  - tokenization
+  - n-grams
+  - markov-chain
+  - text-mining
+  - fasttext
+  - babelvec
+  - vocabulous
+  - vocabulary
+  - monolingual
+  - family-romance_galloitalic
+license: mit
+library_name: wikilangs
+pipeline_tag: text-generation
+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: 4.573
+  - name: best_isotropy
+    type: isotropy
+    value: 0.7808
+  - name: best_alignment_r10
+    type: alignment
+    value: 0.9680
+  - name: vocabulary_size
+    type: vocab
+    value: 1519124
+generated: 2026-03-03
+---
+
+# French — Wikilangs Models
+
+Open-source tokenizers, n-gram & Markov language models, vocabulary stats, and word embeddings trained on **French** Wikipedia by [Wikilangs](https://wikilangs.org).
+
+🌐 [Language Page](https://wikilangs.org/languages/fr/) · 🎮 [Playground](https://wikilangs.org/playground/?lang=fr) · 📊 [Full Research Report](RESEARCH_REPORT.md)
+
+## Language Samples
+
+Example sentences drawn from the French Wikipedia corpus:
+
+> Le décane est un alcane linéaire de formule brute qui possède 136 isomères. Ces diverses molécules comportent toutes dix [en grec δέκα (déca)] atomes de carbone. Notes et références linéaire du décane
+
+> Cette liste représente les plus importantes villes de l'Égypte antique ordonnées par nome et suivies des divinités qui y étaient adorées. Basse-Égypte
+
+> L'eicosane est un alcane linéaire de formule brute . Il possède isomères structuraux. Notes et références linéaire
+
+> Hapy se réfère à Hâpi : Génie à tête de singe de la mythologie égyptienne. Hâpy : Dieu du Nil dans la mythologie égyptienne.
+
+> L'heptane ou n-heptane est l'hydrocarbure saturé de la famille des alcanes linéaires de formule CH. Notes et références linéaire de l'heptane
+
+## Quick Start
+
+### Load the Tokenizer
+
+```python
+import sentencepiece as spm
+
+sp = spm.SentencePieceProcessor()
+sp.Load("fr_tokenizer_32k.model")
+
+text = "Lapon peut désigner : les Samis ; les langues sames ; Lapon, une ville du Soudan"
+tokens = sp.EncodeAsPieces(text)
+ids    = sp.EncodeAsIds(text)
+
+print(tokens)  # subword pieces
+print(ids)     # integer ids
+
+# Decode back
+print(sp.DecodeIds(ids))
+```
+
+<details>
+<summary><b>Tokenization examples (click to expand)</b></summary>
+
+**Sample 1:** `Lapon peut désigner : les Samis ; les langues sames ; Lapon, une ville du Soudan…`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁lap on ▁peut ▁désigner ▁: ▁les ▁sam is ▁; ▁les … (+27 more)` | 37 |
+| 16k | `▁lap on ▁peut ▁désigner ▁: ▁les ▁sam is ▁; ▁les … (+27 more)` | 37 |
+| 32k | `▁lap on ▁peut ▁désigner ▁: ▁les ▁sam is ▁; ▁les … (+26 more)` | 36 |
+| 64k | `▁lap on ▁peut ▁désigner ▁: ▁les ▁sam is ▁; ▁les … (+25 more)` | 35 |
+
+**Sample 2:** `Le pentadécane est un alcane linéaire de formule brute . Il possède 4 347 isomèr…`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁le ▁pent ad éc ane ▁est ▁un ▁al c ane … (+27 more)` | 37 |
+| 16k | `▁le ▁pent ad éc ane ▁est ▁un ▁alc ane ▁linéaire … (+22 more)` | 32 |
+| 32k | `▁le ▁pent ad éc ane ▁est ▁un ▁alc ane ▁linéaire … (+20 more)` | 30 |
+| 64k | `▁le ▁pent ad éc ane ▁est ▁un ▁alcane ▁linéaire ▁de … (+18 more)` | 28 |
+
+**Sample 3:** `L'eicosane est un alcane linéaire de formule brute . Il possède isomères structu…`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁l ' e ic os ane ▁est ▁un ▁al c … (+22 more)` | 32 |
+| 16k | `▁l ' e icos ane ▁est ▁un ▁alc ane ▁linéaire … (+16 more)` | 26 |
+| 32k | `▁l ' e icos ane ▁est ▁un ▁alc ane ▁linéaire … (+14 more)` | 24 |
+| 64k | `▁l ' e icos ane ▁est ▁un ▁alcane ▁linéaire ▁de … (+12 more)` | 22 |
+
+</details>
+
+### Load Word Embeddings
+
+```python
+from gensim.models import KeyedVectors
+
+# Aligned embeddings (cross-lingual, mapped to English vector space)
+wv = KeyedVectors.load("fr_embeddings_128d_aligned.kv")
+
+similar = wv.most_similar("word", topn=5)
+for word, score in similar:
+    print(f"  {word}: {score:.3f}")
+```
+
+### Load N-gram Model
+
+```python
+import pyarrow.parquet as pq
+
+df = pq.read_table("fr_3gram_word.parquet").to_pandas()
+print(df.head())
+```
+
+## Models Overview
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+| Category | Assets |
+|----------|--------|
+| Tokenizers | BPE at 8k, 16k, 32k, 64k vocab sizes |
+| N-gram models | 2 / 3 / 4 / 5-gram (word & subword) |
+| Markov chains | Context 1–5 (word & subword) |
+| Embeddings | 32d, 64d, 128d — mono & aligned |
+| Vocabulary | Full frequency list + Zipf analysis |
+| Statistics | Corpus & model statistics JSON |
+
+## Metrics Summary
+
+| Component | Model | Key Metric | Value |
+|-----------|-------|------------|-------|
+| Tokenizer | 8k BPE | Compression | 3.72x |
+| Tokenizer | 16k BPE | Compression | 4.08x |
+| Tokenizer | 32k BPE | Compression | 4.37x |
+| Tokenizer | 64k BPE | Compression | 4.57x 🏆 |
+| N-gram | 2-gram (subword) | Perplexity | 251 🏆 |
+| N-gram | 2-gram (word) | Perplexity | 197,170 |
+| N-gram | 3-gram (subword) | Perplexity | 1,988 |
+| N-gram | 3-gram (word) | Perplexity | 1,815,223 |
+| N-gram | 4-gram (subword) | Perplexity | 11,120 |
+| N-gram | 4-gram (word) | Perplexity | 5,518,864 |
+| N-gram | 5-gram (subword) | Perplexity | 46,850 |
+| N-gram | 5-gram (word) | Perplexity | 4,103,331 |
+| Markov | ctx-1 (subword) | Predictability | 0.0% |
+| Markov | ctx-1 (word) | Predictability | 9.5% |
+| Markov | ctx-2 (subword) | Predictability | 39.6% |
+| Markov | ctx-2 (word) | Predictability | 53.5% |
+| Markov | ctx-3 (subword) | Predictability | 36.4% |
+| Markov | ctx-3 (word) | Predictability | 74.5% |
+| Markov | ctx-4 (subword) | Predictability | 34.0% |
+| Markov | ctx-4 (word) | Predictability | 87.3% 🏆 |
+| Vocabulary | full | Size | 1,519,124 |
+| Vocabulary | full | Zipf R² | 0.9927 |
+| Embeddings | mono_32d | Isotropy | 0.7808 🏆 |
+| Embeddings | mono_64d | Isotropy | 0.7574 |
+| Embeddings | mono_128d | Isotropy | 0.6995 |
+| Embeddings | aligned_32d | Isotropy | 0.7808 |
+| Embeddings | aligned_64d | Isotropy | 0.7574 |
+| Embeddings | aligned_128d | Isotropy | 0.6995 |
+| Alignment | aligned_32d | R@1 / R@5 / R@10 | 48.2% / 74.8% / 82.4% |
+| Alignment | aligned_64d | R@1 / R@5 / R@10 | 70.8% / 89.6% / 94.2% |
+| Alignment | aligned_128d | R@1 / R@5 / R@10 | 81.2% / 93.4% / 96.8% 🏆 |
+
+📊 **[Full ablation study, per-model breakdowns, and interpretation guide →](RESEARCH_REPORT.md)**
+
+---
+
+## About
+
+Trained on [wikipedia-monthly](https://huggingface.co/datasets/omarkamali/wikipedia-monthly) — monthly snapshots of 300+ Wikipedia languages.
+
+A project by **[Wikilangs](https://wikilangs.org)** · Maintainer: [Omar Kamali](https://omarkamali.com) · [Omneity Labs](https://omneitylabs.com)
+
+### Citation
+
+```bibtex
+@misc{wikilangs2025,
+  author    = {Kamali, Omar},
+  title     = {Wikilangs: Open NLP Models for Wikipedia Languages},
+  year      = {2025},
+  doi       = {10.5281/zenodo.18073153},
+  publisher = {Zenodo},
+  url       = {https://huggingface.co/wikilangs},
+  institution = {Omneity Labs}
+}
+```
+
+### Links
+
+- 🌐 [wikilangs.org](https://wikilangs.org)
+- 🌍 [Language page](https://wikilangs.org/languages/fr/)
+- 🎮 [Playground](https://wikilangs.org/playground/?lang=fr)
+- 🤗 [HuggingFace models](https://huggingface.co/wikilangs)
+- 📊 [wikipedia-monthly dataset](https://huggingface.co/datasets/omarkamali/wikipedia-monthly)
+- 👤 [Omar Kamali](https://huggingface.co/omarkamali)
+- 🤝 Sponsor: [Featherless AI](https://featherless.ai)
+
+**License:** MIT — free for academic and commercial use.
+
+---
+*Generated by Wikilangs Pipeline · 2026-03-03 05:41:40*

RESEARCH_REPORT.md

@@ -0,0 +1,686 @@
+# French — Full Ablation Study & Research Report
+
+Detailed evaluation of all model variants trained on **French** Wikipedia data by [Wikilangs](https://wikilangs.org).
+
+👈 [Back to README](README.md)
+
+## 📋 Repository Contents
+
+### Models & Assets
+
+- Tokenizers (8k, 16k, 32k, 64k)
+- 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 (aligned and unaligned)
+- 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. Morphological Analysis (Experimental)](#6--morphological-analysis-experimental)
+- [7. Summary & Recommendations](#7-summary--recommendations)
+- [Metrics Glossary](#appendix-metrics-glossary--interpretation-guide)
+- [Visualizations Index](#visualizations-index)
+
+---
+## 1. Tokenizer Evaluation
+
+![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** | 3.723x | 3.72 | 0.0810% | 7,061,086 |
+| **16k** | 4.078x | 4.08 | 0.0887% | 6,446,468 |
+| **32k** | 4.368x | 4.37 | 0.0950% | 6,018,653 |
+| **64k** | 4.573x 🏆 | 4.57 | 0.0994% | 5,748,614 |
+
+### Tokenization Examples
+
+Below are sample sentences tokenized with each vocabulary size:
+
+**Sample 1:** `Lapon peut désigner : les Samis ; les langues sames ; Lapon, une ville du Soudan...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁lap on ▁peut ▁désigner ▁: ▁les ▁sam is ▁; ▁les ... (+27 more)` | 37 |
+| 16k | `▁lap on ▁peut ▁désigner ▁: ▁les ▁sam is ▁; ▁les ... (+27 more)` | 37 |
+| 32k | `▁lap on ▁peut ▁désigner ▁: ▁les ▁sam is ▁; ▁les ... (+26 more)` | 36 |
+| 64k | `▁lap on ▁peut ▁désigner ▁: ▁les ▁sam is ▁; ▁les ... (+25 more)` | 35 |
+
+**Sample 2:** `Le pentadécane est un alcane linéaire de formule brute . Il possède 4 347 isomèr...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁le ▁pent ad éc ane ▁est ▁un ▁al c ane ... (+27 more)` | 37 |
+| 16k | `▁le ▁pent ad éc ane ▁est ▁un ▁alc ane ▁linéaire ... (+22 more)` | 32 |
+| 32k | `▁le ▁pent ad éc ane ▁est ▁un ▁alc ane ▁linéaire ... (+20 more)` | 30 |
+| 64k | `▁le ▁pent ad éc ane ▁est ▁un ▁alcane ▁linéaire ▁de ... (+18 more)` | 28 |
+
+**Sample 3:** `L'eicosane est un alcane linéaire de formule brute . Il possède isomères structu...`
+
+| Vocab | Tokens | Count |
+|-------|--------|-------|
+| 8k | `▁l ' e ic os ane ▁est ▁un ▁al c ... (+22 more)` | 32 |
+| 16k | `▁l ' e icos ane ▁est ▁un ▁alc ane ▁linéaire ... (+16 more)` | 26 |
+| 32k | `▁l ' e icos ane ▁est ▁un ▁alc ane ▁linéaire ... (+14 more)` | 24 |
+| 64k | `▁l ' e icos ane ▁est ▁un ▁alcane ▁linéaire ▁de ... (+12 more)` | 22 |
+
+
+### Key Findings
+
+- **Best Compression:** 64k achieves 4.573x compression
+- **Lowest UNK Rate:** 8k with 0.0810% 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 Unique](visualizations/ngram_unique.png)
+
+![N-gram Coverage](visualizations/ngram_coverage.png)
+
+### Results
+
+| N-gram | Variant | Perplexity | Entropy | Unique N-grams | Top-100 Coverage | Top-1000 Coverage |
+|--------|---------|------------|---------|----------------|------------------|-------------------|
+| **2-gram** | Word | 197,170 | 17.59 | 5,674,755 | 9.3% | 21.3% |
+| **2-gram** | Subword | 251 🏆 | 7.97 | 40,333 | 68.1% | 99.4% |
+| **3-gram** | Word | 1,815,223 | 20.79 | 18,029,220 | 2.4% | 9.5% |
+| **3-gram** | Subword | 1,988 | 10.96 | 269,239 | 30.3% | 73.7% |
+| **4-gram** | Word | 5,518,864 | 22.40 | 36,868,834 | 1.8% | 8.6% |
+| **4-gram** | Subword | 11,120 | 13.44 | 1,563,238 | 15.8% | 42.2% |
+| **5-gram** | Word | 4,103,331 | 21.97 | 28,227,926 | 2.4% | 11.6% |
+| **5-gram** | Subword | 46,850 | 15.52 | 5,742,636 | 8.7% | 25.7% |
+
+### Top 5 N-grams by Size
+
+**2-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `de la` | 5,296,650 |
+| 2 | `de l` | 3,236,403 |
+| 3 | `à la` | 1,514,334 |
+| 4 | `à l` | 1,261,623 |
+| 5 | `dans le` | 992,683 |
+
+**3-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `de la commune` | 330,769 |
+| 2 | `notes et références` | 310,459 |
+| 3 | `occupation des sols` | 189,915 |
+| 4 | `et de la` | 154,877 |
+| 5 | `le nom de` | 144,766 |
+
+**4-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `l occupation des sols` | 124,755 |
+| 2 | `occupation des sols de` | 93,658 |
+| 3 | `des sols de la` | 93,553 |
+| 4 | `sols de la commune` | 93,442 |
+| 5 | `notes et références voir` | 79,475 |
+
+**5-grams (Word):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `occupation des sols de la` | 93,478 |
+| 2 | `des sols de la commune` | 93,402 |
+| 3 | `l occupation des sols de` | 93,364 |
+| 4 | `notes et références voir aussi` | 79,373 |
+| 5 | `notes et références liens externes` | 68,549 |
+
+**2-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `e _` | 125,785,698 |
+| 2 | `s _` | 74,291,364 |
+| 3 | `_ d` | 73,439,085 |
+| 4 | `_ l` | 55,706,551 |
+| 5 | `e s` | 54,301,349 |
+
+**3-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e` | 41,554,921 |
+| 2 | `e s _` | 37,119,081 |
+| 3 | `d e _` | 32,944,492 |
+| 4 | `e _ d` | 22,149,546 |
+| 5 | `l e _` | 20,731,208 |
+
+**4-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e _` | 30,047,475 |
+| 2 | `_ l a _` | 16,205,522 |
+| 3 | `e _ d e` | 12,867,124 |
+| 4 | `_ l e _` | 11,621,670 |
+| 5 | `_ e t _` | 11,489,728 |
+
+**5-grams (Subword):**
+
+| Rank | N-gram | Count |
+|------|--------|-------|
+| 1 | `_ d e _ l` | 9,678,287 |
+| 2 | `e _ d e _` | 9,546,481 |
+| 3 | `_ d e s _` | 7,709,198 |
+| 4 | `_ l e s _` | 7,167,363 |
+| 5 | `e _ l a _` | 6,536,044 |
+
+
+### Key Findings
+
+- **Best Perplexity:** 2-gram (subword) with 251
+- **Entropy Trend:** Decreases with larger n-grams (more predictable)
+- **Coverage:** Top-1000 patterns cover ~26% of corpus
+- **Recommendation:** 4-gram or 5-gram for best predictive performance
+
+---
+## 3. Markov Chain Evaluation
+
+![Markov Entropy](visualizations/markov_entropy.png)
+
+![Markov Contexts](visualizations/markov_contexts.png)
+
+![Markov Branching](visualizations/markov_branching.png)
+
+### Results
+
+| Context | Variant | Avg Entropy | Perplexity | Branching Factor | Unique Contexts | Predictability |
+|---------|---------|-------------|------------|------------------|-----------------|----------------|
+| **1** | Word | 0.9052 | 1.873 | 14.72 | 3,729,596 | 9.5% |
+| **1** | Subword | 1.2049 | 2.305 | 9.45 | 20,472 | 0.0% |
+| **2** | Word | 0.4648 | 1.380 | 3.15 | 54,852,526 | 53.5% |
+| **2** | Subword | 0.6045 | 1.520 | 3.87 | 193,340 | 39.6% |
+| **3** | Word | 0.2551 | 1.193 | 1.71 | 172,431,097 | 74.5% |
+| **3** | Subword | 0.6357 | 1.554 | 3.88 | 747,541 | 36.4% |
+| **4** | Word | 0.1275 🏆 | 1.092 | 1.26 | 294,675,620 | 87.3% |
+| **4** | Subword | 0.6602 | 1.580 | 3.61 | 2,901,861 | 34.0% |
+
+### Generated Text Samples (Word-based)
+
+Below are text samples generated from each word-based Markov chain model:
+
+**Context Size 1:**
+
+1. `de titan éditions plon 424 p haffner inventaire de paris occupation des modèles différents les début...`
+2. `la haine féroce le bon pasteur afro américaine par la saison régulière se basent sur le`
+3. `le sprinteur britannique de l insigne homologué au rat dû à l ambitieux antipater d apel`
+
+**Context Size 2:**
+
+1. `de la légion d honneur fleurs chapelle de la mer alt gauche vignette cimetière protestant de maulbro...`
+2. `de l œuvre est un dessinateur et peintre ses tableaux de derain h 10 min 50 s`
+3. `à la fin du la classe 1 avec un chevalet en butée dans ces moments là le`
+
+**Context Size 3:**
+
+1. `de la commune est de 325 soit un indicateur de concentration d emploi de 85 2 la répartition`
+2. `notes et références liens externes sud coréen sorti en d aventure américain d aventure américain met...`
+3. `occupation des sols center carte des infrastructures et de l électromagnétisme universel de vitalité...`
+
+**Context Size 4:**
+
+1. `l occupation des sols carte des infrastructures et de l occupation des sols de la commune telle qu e...`
+2. `occupation des sols de la commune telle qu elle ressort de la base de données européenne d occupatio...`
+3. `des sols de la commune en clc risques majeurs le territoire de la commune durant quinze ans christop...`
+
+
+### Generated Text Samples (Subword-based)
+
+Below are text samples generated from each subword-based Markov chain model:
+
+**Context Size 1:**
+
+1. `_partene_doy_e_s`
+2. `e_larit_dus_iome`
+3. `avide_cèsouéoù_c`
+
+**Context Size 2:**
+
+1. `e_jam_à_les_du_de`
+2. `s_thé_ilite_la_wa`
+3. `_de_sa_comist_est`
+
+**Context Size 3:**
+
+1. `_de_prementrest_un`
+2. `es_non_de_balling_`
+3. `de_canadieurs_la_p`
+
+**Context Size 4:**
+
+1. `_de_saxe,_le_cumulu`
+2. `_la_capitalie._8_si`
+3. `e_de_de_fumer_l'éco`
+
+
+### Key Findings
+
+- **Best Predictability:** Context-4 (word) with 87.3% predictability
+- **Branching Factor:** Decreases with context size (more deterministic)
+- **Memory Trade-off:** Larger contexts require more storage (2,901,861 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 | 1,519,124 |
+| Total Tokens | 496,742,137 |
+| Mean Frequency | 326.99 |
+| Median Frequency | 4 |
+| Frequency Std Dev | 37954.61 |
+
+### Most Common Words
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | de | 30,301,637 |
+| 2 | la | 16,417,649 |
+| 3 | le | 11,875,190 |
+| 4 | et | 11,702,997 |
+| 5 | l | 10,150,468 |
+| 6 | en | 9,437,564 |
+| 7 | à | 9,348,887 |
+| 8 | des | 7,741,717 |
+| 9 | d | 7,508,960 |
+| 10 | les | 7,283,372 |
+
+### Least Common Words (from vocabulary)
+
+| Rank | Word | Frequency |
+|------|------|-----------|
+| 1 | caracallæ | 2 |
+| 2 | santapaulina | 2 |
+| 3 | publinf | 2 |
+| 4 | vijñānabhairava | 2 |
+| 5 | benbor | 2 |
+| 6 | kunpan | 2 |
+| 7 | moderndrawings | 2 |
+| 8 | jexiste | 2 |
+| 9 | ⴰⵣⵎⵎⵓⵔ | 2 |
+| 10 | pseudocarcinus | 2 |
+
+### Zipf's Law Analysis
+
+| Metric | Value |
+|--------|-------|
+| Zipf Coefficient | 1.0347 |
+| R² (Goodness of Fit) | 0.992664 |
+| Adherence Quality | **excellent** |
+
+### Coverage Analysis
+
+| Top N Words | Coverage |
+|-------------|----------|
+| Top 100 | 44.8% |
+| Top 1,000 | 63.9% |
+| Top 5,000 | 79.7% |
+| Top 10,000 | 85.5% |
+
+### Key Findings
+
+- **Zipf Compliance:** R²=0.9927 indicates excellent adherence to Zipf's law
+- **High Frequency Dominance:** Top 100 words cover 44.8% of corpus
+- **Long Tail:** 1,509,124 words needed for remaining 14.5% 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)
+
+
+### 5.1 Cross-Lingual Alignment
+
+![Alignment Quality](visualizations/embedding_alignment_quality.png)
+
+![Multilingual t-SNE](visualizations/embedding_tsne_multilingual.png)
+
+
+### 5.2 Model Comparison
+
+| Model | Dimension | Isotropy | Semantic Density | Alignment R@1 | Alignment R@10 |
+|-------|-----------|----------|------------------|---------------|----------------|
+| **mono_32d** | 32 | 0.7808 🏆 | 0.3764 | N/A | N/A |
+| **mono_64d** | 64 | 0.7574 | 0.3033 | N/A | N/A |
+| **mono_128d** | 128 | 0.6995 | 0.2569 | N/A | N/A |
+| **aligned_32d** | 32 | 0.7808 | 0.3878 | 0.4820 | 0.8240 |
+| **aligned_64d** | 64 | 0.7574 | 0.3079 | 0.7080 | 0.9420 |
+| **aligned_128d** | 128 | 0.6995 | 0.2657 | 0.8120 | 0.9680 |
+
+### Key Findings
+
+- **Best Isotropy:** mono_32d with 0.7808 (more uniform distribution)
+- **Semantic Density:** Average pairwise similarity of 0.3163. Lower values indicate better semantic separation.
+- **Alignment Quality:** Aligned models achieve up to 81.2% R@1 in cross-lingual retrieval.
+- **Recommendation:** 128d aligned for best cross-lingual performance
+
+---
+## 6.  Morphological Analysis (Experimental)
+
+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 | **5.000** | High morphological productivity | Reliable analysis |
+| Idiomaticity Gap | **-0.550** | 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 |
+|--------|----------|
+| `-a` | altschloss, aën, alfège |
+| `-s` | similairesselon, shochugeiko, sodapop |
+| `-c` | cugn, crocefisso, colomer |
+| `-m` | maracanaú, mandriole, morzine |
+| `-ma` | maracanaú, mandriole, mastaï |
+| `-d` | drăculeștibasarab, déchirent, durégnatons |
+| `-p` | pame, pichery, pleased |
+| `-b` | bingoto, blennies, brusuglio |
+
+#### Productive Suffixes
+| Suffix | Examples |
+|--------|----------|
+| `-e` | pame, mandriole, morzine |
+| `-s` | altschloss, blennies, durégnatons |
+| `-es` | blennies, moyensles, fraternelles |
+| `-n` | cugn, similairesselon, fransson |
+| `-a` | occasionna, exea, paçoca |
+| `-t` | déchirent, lovefist, crabbet |
+| `-r` | colomer, elvir, eckersbacher |
+| `-i` | wubi, idiomorphini, oroi |
+
+### 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.
+
+| Stem | Cohesion | Substitutability | Examples |
+|------|----------|------------------|----------|
+| `ient` | 2.44x | 382 contexts | nient, fient, rient |
+| `aphi` | 2.02x | 140 contexts | aphis, aphia, aphid |
+| `ienn` | 1.59x | 391 contexts | ienne, vienn, fienne |
+| `ogra` | 1.62x | 276 contexts | logra, bogra, fogra |
+| `éren` | 1.81x | 150 contexts | héren, kéren, érenn |
+| `ontr` | 1.59x | 266 contexts | montr, ontra, kontr |
+| `tiqu` | 1.49x | 318 contexts | tiqui, tique, tiqué |
+| `aiso` | 1.66x | 172 contexts | baiso, gaiso, daiso |
+| `utre` | 1.56x | 215 contexts | butre, outre, autre |
+| `niqu` | 1.42x | 248 contexts | nique, niqué, niqua |
+| `rtic` | 1.44x | 179 contexts | artic, hrtica, artici |
+| `onal` | 1.50x | 136 contexts | conal, monal, sonal |
+
+### 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 |
+|--------|--------|-----------|----------|
+| `-c` | `-s` | 143 words | couleuses, cammaerts |
+| `-m` | `-s` | 137 words | morians, merckens |
+| `-c` | `-e` | 121 words | clémentine, classessite |
+| `-p` | `-s` | 115 words | pétards, psylles |
+| `-a` | `-s` | 114 words | alvaros, aldrinus |
+| `-s` | `-s` | 107 words | sportpaleis, shaggys |
+| `-p` | `-e` | 105 words | pédagogique, poursuiveuse |
+| `-m` | `-e` | 102 words | maastrichtle, martinofiministre |
+| `-a` | `-e` | 95 words | alboize, autoparodie |
+| `-s` | `-e` | 92 words | salicyline, studiesthe |
+
+### 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 |
+|------|-----------------|------------|------|
+| sarrabeyrouse | **`sarrabeyrou-s-e`** | 7.5 | `s` |
+| grippement | **`grippem-e-nt`** | 7.5 | `e` |
+| égalementsite | **`égalements-i-te`** | 7.5 | `i` |
+| rediviser | **`redivi-s-er`** | 7.5 | `s` |
+| ambrosini | **`ambrosi-n-i`** | 7.5 | `n` |
+| rencontrerai | **`rencontrer-a-i`** | 7.5 | `a` |
+| détroitsaison | **`détroitsai-s-on`** | 7.5 | `s` |
+| monpalais | **`monpal-a-is`** | 7.5 | `a` |
+| vieumaison | **`vieumai-s-on`** | 7.5 | `s` |
+| caractéristise | **`caractéristi-s-e`** | 7.5 | `s` |
+| circonvient | **`circonvi-e-nt`** | 7.5 | `e` |
+| tilehurst | **`tilehur-s-t`** | 7.5 | `s` |
+| chongsheng | **`chongsh-e-ng`** | 7.5 | `e` |
+| bloemfontein | **`bloemfont-e-in`** | 7.5 | `e` |
+| voorspoel | **`voorspo-e-l`** | 7.5 | `e` |
+
+### 6.6 Linguistic Interpretation
+
+> **Automated Insight:**
+The language French shows high morphological productivity. The subword models are significantly more efficient than word models, suggesting a rich system of affixation or compounding.
+
+---
+## 7. Summary & Recommendations
+
+![Performance Dashboard](visualizations/performance_dashboard.png)
+
+### Production Recommendations
+
+| Component | Recommended | Rationale |
+|-----------|-------------|-----------|
+| Tokenizer | **64k BPE** | Best compression (4.57x) |
+| N-gram | **2-gram** | Lowest perplexity (251) |
+| Markov | **Context-4** | Highest predictability (87.3%) |
+| 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 |
+
+---
+👈 [Back to README](README.md)
+
+*Generated by Wikilangs Pipeline · 2026-03-03 09:31:27*

models/embeddings/aligned/fr_128d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9cc99440e53e80d8c657bdf435e90f747c5c3c8ab1c3768bb4d80cbfd8ab1628
+size 2629947255

models/embeddings/aligned/fr_128d.meta.json

@@ -0,0 +1 @@
+{"lang": "fr", "dim": 128, "max_seq_len": 512, "is_aligned": true}

models/embeddings/aligned/fr_128d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:413129e71f1e456937a6643f19be8dd41b9373f9dca4fb5450ad9e5b08cb2cef
+size 65664

models/embeddings/aligned/fr_128d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "fr",
+  "dimension": 128,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 343276,
+  "vocab_size": 1539741
+}

models/embeddings/aligned/fr_32d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c4d323482c458ed1f94f1dbf4a8f3eabe1cd6355834c7bcf88f45ee1c82f177f
+size 679426167

models/embeddings/aligned/fr_32d.meta.json

@@ -0,0 +1 @@
+{"lang": "fr", "dim": 32, "max_seq_len": 512, "is_aligned": true}

models/embeddings/aligned/fr_32d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:51c9982fbfc53f22e6c179d9b545f21a3bce3bf876cc60336ceeb459119fbc29
+size 4224

models/embeddings/aligned/fr_32d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "fr",
+  "dimension": 32,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 343276,
+  "vocab_size": 1539741
+}

models/embeddings/aligned/fr_64d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d0f9e8f10de4ce5d350e08be7b2eddf9d6cfafea1e14074030974d8044c70493
+size 1329599863

models/embeddings/aligned/fr_64d.meta.json

@@ -0,0 +1 @@
+{"lang": "fr", "dim": 64, "max_seq_len": 512, "is_aligned": true}

models/embeddings/aligned/fr_64d.projection.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dcad10b66a19653d05fa662d1e97c381ff1929915aa8811803fe18f3c3dd8fee
+size 16512

models/embeddings/aligned/fr_64d_metadata.json

@@ -0,0 +1,8 @@
+{
+  "language": "fr",
+  "dimension": 64,
+  "version": "aligned",
+  "hub_language": "en",
+  "seed_vocab_size": 343276,
+  "vocab_size": 1539741
+}

models/embeddings/monolingual/fr_128d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9cc99440e53e80d8c657bdf435e90f747c5c3c8ab1c3768bb4d80cbfd8ab1628
+size 2629947255

models/embeddings/monolingual/fr_128d.meta.json

@@ -0,0 +1 @@
+{"lang": "fr", "dim": 128, "max_seq_len": 512, "is_aligned": false}

models/embeddings/monolingual/fr_128d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "fr",
+  "dimension": 128,
+  "version": "monolingual",
+  "training_params": {
+    "algorithm": "skipgram",
+    "min_count": 5,
+    "window": 5,
+    "negative": 5,
+    "epochs": 5,
+    "encoding_method": "rope",
+    "dim": 128,
+    "threads": 40
+  },
+  "vocab_size": 1539741
+}

models/embeddings/monolingual/fr_32d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c4d323482c458ed1f94f1dbf4a8f3eabe1cd6355834c7bcf88f45ee1c82f177f
+size 679426167

models/embeddings/monolingual/fr_32d.meta.json

@@ -0,0 +1 @@
+{"lang": "fr", "dim": 32, "max_seq_len": 512, "is_aligned": false}

models/embeddings/monolingual/fr_32d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "fr",
+  "dimension": 32,
+  "version": "monolingual",
+  "training_params": {
+    "algorithm": "skipgram",
+    "min_count": 5,
+    "window": 5,
+    "negative": 5,
+    "epochs": 5,
+    "encoding_method": "rope",
+    "dim": 32,
+    "threads": 40
+  },
+  "vocab_size": 1539741
+}

models/embeddings/monolingual/fr_64d.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d0f9e8f10de4ce5d350e08be7b2eddf9d6cfafea1e14074030974d8044c70493
+size 1329599863

models/embeddings/monolingual/fr_64d.meta.json

@@ -0,0 +1 @@
+{"lang": "fr", "dim": 64, "max_seq_len": 512, "is_aligned": false}

models/embeddings/monolingual/fr_64d_metadata.json

@@ -0,0 +1,16 @@
+{
+  "language": "fr",
+  "dimension": 64,
+  "version": "monolingual",
+  "training_params": {
+    "algorithm": "skipgram",
+    "min_count": 5,
+    "window": 5,
+    "negative": 5,
+    "epochs": 5,
+    "encoding_method": "rope",
+    "dim": 64,
+    "threads": 40
+  },
+  "vocab_size": 1539741
+}

models/subword_markov/fr_markov_ctx1_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5d17c98d8321d716fcf12e6bab322e7cbf598563170cd2f02e13296180b4ea46
+size 1210329

models/subword_markov/fr_markov_ctx1_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 1,
+  "variant": "subword",
+  "language": "fr",
+  "unique_contexts": 20472,
+  "total_transitions": 3031878233
+}

models/subword_markov/fr_markov_ctx2_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:20202469e2f7dd6fc1b6845e57b06c94a6f724f8aec6c4315bbfcd5ef6651eae
+size 6033562

models/subword_markov/fr_markov_ctx2_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 2,
+  "variant": "subword",
+  "language": "fr",
+  "unique_contexts": 193340,
+  "total_transitions": 3031378233
+}

models/subword_markov/fr_markov_ctx3_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a417d0293b24fc0282d69ac6274e7dad1b68b68984c132c92588f4d7feda0597
+size 23077214

models/subword_markov/fr_markov_ctx3_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 3,
+  "variant": "subword",
+  "language": "fr",
+  "unique_contexts": 747541,
+  "total_transitions": 3030878233
+}

models/subword_markov/fr_markov_ctx4_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f9516571ba1c6e37dbbfd558e73b96d91143fe7986b60e29ff16ae1a2a3760b3
+size 84475073

models/subword_markov/fr_markov_ctx4_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "context_size": 4,
+  "variant": "subword",
+  "language": "fr",
+  "unique_contexts": 2901861,
+  "total_transitions": 3030378233
+}

models/subword_ngram/fr_2gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2d4b77ee5a14d10ba6aa3ee12abadc29333d5d9c35c9ca2c92cf68af5deeccd0
+size 560596

models/subword_ngram/fr_2gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 2,
+  "variant": "subword",
+  "language": "fr",
+  "unique_ngrams": 40333,
+  "total_ngrams": 3031878233
+}

models/subword_ngram/fr_3gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:96914fbf6dcf6e06557f5d941050f7072ead044f0f6bf350ccbd86802cf406d3
+size 3338532

models/subword_ngram/fr_3gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 3,
+  "variant": "subword",
+  "language": "fr",
+  "unique_ngrams": 269239,
+  "total_ngrams": 3031378233
+}

models/subword_ngram/fr_4gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7cb2fe77386fcf95b60edf94e789cbbd2860d9ae0a7372b104234f29d8ea782a
+size 18941023

models/subword_ngram/fr_4gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 4,
+  "variant": "subword",
+  "language": "fr",
+  "unique_ngrams": 1563238,
+  "total_ngrams": 3030878233
+}

models/subword_ngram/fr_5gram_subword.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fa8f85d6a07900eab7c970d6dd66cbb66159a7afa3cefbbe88f091c447f7134d
+size 69614434

models/subword_ngram/fr_5gram_subword_metadata.json

@@ -0,0 +1,7 @@
+{
+  "n": 5,
+  "variant": "subword",
+  "language": "fr",
+  "unique_ngrams": 5742636,
+  "total_ngrams": 3030378233
+}

models/tokenizer/fr_tokenizer_16k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:34b2bd3a0a66001512884c9e895e94cf771c60278e70120773885aeb56b770ea
+size 514148

models/tokenizer/fr_tokenizer_32k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e9645d21c7d752b5666b2d68980d989775bbb87f1235a3408f44b1a9301d3b8
+size 797139

models/tokenizer/fr_tokenizer_64k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bf2efb1603cb200e1de968b5313537e68a4086cf8e7c9871e7bb9c9c1f5adf8d
+size 1368995

models/tokenizer/fr_tokenizer_8k.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f56968e4af5c25f7999e0afb640bd43507b1252956fe24abeb4b5da197fc914e
+size 375620

models/vocabulary/fr_vocabulary.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:747d5ebdb71636d1c45957debecf91a0bfec2e5b33cac918bbb08b7c6688b0af
+size 22772237