Rebuild: English 28-col documents schema, sentence-grounded citations (ref/Mục/span/sentence_id), Nemotron-3-8B sentence-chunk embeddings, PCA/t-SNE/UMAP, citation Sankey

README.md

@@ -70,234 +70,165 @@ configs:
 | Chỉ số · Metric | Giá trị · Value |
 |---|---:|
 | Văn bản · Documents | **1,844** |
-| Câu · Sentences (across the corpus) | 259,838 |
-| Có cấu trúc · With structure layer | 1,844 |
-| Có số án lệ · With precedent number | 50 |
-| Có embedding · With embedding vector | 1,844 |
-| Có projection · With reduce projections | 1,844 |
+| Câu · Sentences | 169,698 |
+| Trích dẫn luật · Law citations (sentence-grounded) | 29,783 |
+| Trích dẫn bản án · Case citations | 4,238 |
+| Án lệ chính thức · Official án lệ (`is_precedent`) | 9 |
+| Có embedding · Embedding vectors (4096-d) | 1,844 |
+| Có projection · Reduce projections (PCA/t-SNE/UMAP) | 1,844 |
 | Trung vị trang · Median pages / doc | 9 |
-| Trung vị ký tự · Median chars / doc | 20,517 |
-| Trung vị đoạn văn · Median paragraphs / doc | 46 |
-| Trung vị câu · Median sentences / doc | 123 |
+| Trung vị ký tự · Median chars / doc | 20,909 |
+| Trung vị câu · Median sentences / doc | 79 |
 
 ## Phân loại · Document classes
 
-### Loại văn bản · `doc_type`
+> Column names and categorical values are **English** (for adoption + education); legal **content** — `court`, `law_name`, citation `ref` text, `code`, `markdown` — stays Vietnamese.
 
-| Value | Count | Share |
-|---|---:|---:|
-| `ban_an` | 1,139 | 61.8% |
-| `quyet_dinh` | 698 | 37.9% |
-| `unknown` | 7 | 0.4% |
-
-### Lĩnh vực · `case_type`
+### Lĩnh vực · `category`
 
 | Value | Count | Share |
 |---|---:|---:|
-| `dan_su` | 829 | 45.0% |
-| `hinh_su` | 457 | 24.8% |
-| `hanh_chinh` | 305 | 16.5% |
-| `unknown` | 105 | 5.7% |
-| `kinh_doanh_thuong_mai` | 97 | 5.3% |
-| `hon_nhan_gia_dinh` | 43 | 2.3% |
-| `lao_dong` | 8 | 0.4% |
+| `Civil` | 892 | 48.4% |
+| `Criminal` | 459 | 24.9% |
+| `Administrative` | 326 | 17.7% |
+| `Commercial` | 105 | 5.7% |
+| `Marriage & Family` | 47 | 2.5% |
+| `Labor` | 8 | 0.4% |
+| `null` | 7 | 0.4% |
 
-### Cấp xét xử · `doc_subtype`
+### Cấp xét xử · `instance_level`
 
 | Value | Count | Share |
 |---|---:|---:|
-| `phuc_tham` | 1,066 | 57.8% |
-| `giam_doc_tham` | 672 | 36.4% |
-| `unknown` | 73 | 4.0% |
-| `tai_tham` | 17 | 0.9% |
-| `so_tham` | 16 | 0.9% |
+| `Appellate` | 1,014 | 55.0% |
+| `Cassation` (giám đốc thẩm) | 680 | 36.9% |
+| `First-instance` | 126 | 6.8% |
+| `Retrial` (tái thẩm) | 17 | 0.9% |
+| `null` | 7 | 0.4% |
 
-### Cấp toà · `court_level`
+### Cấp toà · `court_level` — computed from the court name
 
 | Value | Count | Share |
 |---|---:|---:|
-| `cap_cao` | 1,731 | 93.9% |
-| `toi_cao` | 70 | 3.8% |
-| `unknown` | 20 | 1.1% |
-| `tinh` | 12 | 0.7% |
-| `huyen` | 11 | 0.6% |
+| `High` (cấp cao) | 1,685 | 91.4% |
+| `Supreme` (tối cao) | 70 | 3.8% |
+| `Provincial` (tỉnh) | 19 | 1.0% |
+| `District` (huyện/quận) | 2 | 0.1% |
+| `null` | 68 | 3.7% |
+
+(High dominates because án lệ *sources* are overwhelmingly cấp-cao appellate/cassation rulings.)
 
 ## Lược đồ bảng `documents` · `documents` schema
 
-The default config carries one row per document with three families
-of columns:
+One row per judgment; primary key **`doc_name`** (all configs join on it). 28 columns:
 
-### Identification + meta
+**Identity** — `doc_name` (TAND portal id, PK) · `source` · `web_url` (portal page) · `pdf_url` (PDF binary).
 
-| Field | Type | Description |
-|---|---|---|
-| `doc_name` | string | Stable document id (== source `dDocName` query parameter). |
-| `source` | string | Source host, always `anle.toaan.gov.vn`. |
-| `detail_url` / `pdf_url` | string | Deep link back to the portal page / PDF. |
-| `doc_code` | string | E.g. `38/2021/DS-PT` (sequence/year/case-type-procedure). |
-| `doc_type` | string | Structure-layer form of the underlying document. Full taxonomy: `ban_an` \| `quyet_dinh` \| `an_le` \| `ban_cao_trang`; **in this corpus only `ban_an` (1,139), `quyet_dinh` (698) and `null` (7) occur** — the adopted precedents themselves are flagged via `precedent_number`, not `doc_type`. |
-| `case_type` | string | `dan_su` \| `hinh_su` \| `hon_nhan_gia_dinh` \| `lao_dong` \| `kinh_doanh_thuong_mai` \| `hanh_chinh`. |
-| `doc_subtype` | string | `so_tham` \| `phuc_tham` \| `giam_doc_tham` \| `tai_tham` \| `an_le`. |
-| `year` | int32 | Year extracted from `doc_code`. |
-| `title` | string | Header line as captured. |
-| `subject` | string | `V/v ...` matter line. |
-| `issue_date` | string | ISO 8601 issue date when discoverable. |
-| `issuing_authority` | string | Full court name. |
-| `court_level` | string | `huyen` \| `tinh` \| `cap_cao` \| `toi_cao`. |
-| `jurisdiction` | string | **Deprecated — do not use for legal analysis; scheduled for removal in the next schema revision.** Locality token substring-parsed out of `issuing_authority` (the token after the most-specific `huyện`/`quận`/`thị xã`/`tỉnh`/`thành phố` marker in the court name). It records the court's *seat*, **not** its territorial jurisdiction (*thẩm quyền theo lãnh thổ*) — e.g. the High Court seated in Ho Chi Minh City hears appeals from the whole southern region. 100% re-derivable from `issuing_authority`; ~65% `null` (letterheads like `TÒA ÁN NHÂN DÂN CẤP CAO TẠI ĐÀ NẴNG` carry no marker); district values lack their province; OCR noise passes through verbatim. Prefer `issuing_authority` + `court_level`. |
-
-### Body + stats
+**Official document id** — `official_document_id` (printed, e.g. `237/2022/HS-PT`) · `official_document_id_normalized` (hyphenated, for joins/citation-graph) · `number` · `year` · `code` (VI ký hiệu) · `id_source` (`regex` \| `audit_corrected`).
 
-| Field | Type | Description |
-|---|---|---|
-| `markdown` | string | NFC-normalised, modern-orthography Vietnamese markdown (page-segmented with `## Page N` headings). |
-| `num_pages` / `num_sections` / `num_paragraphs` / `num_sentences` | int32 | Counts from the structure layer. |
-| `char_len` | int32 | Character length of `markdown`. |
-| `text_hash` | string | SHA-256 first-32 hex of the extract-stage markdown; stable join key across `documents` / `sentences` / `embed` / `reduce`. Computed **before** the 2026-07 tone-mark repair (see *Limitations*), so it intentionally does not re-hash the shipped `markdown`. |
-| `parser_model` / `parsed_at` | string | Provenance for the parse stage. |
+**Classification** — `category` · `instance_level` · `court` (VI court name) · `court_level` (English tier) · `issued_date` (date) · `date_source`.
 
-### Hierarchy + entities
+**Precedent** — `precedent_number` (e.g. `44/2021/AL`, non-null on the 9 official án lệ) · `is_precedent` (bool).
 
-| Field | Type | Description |
-|---|---|---|
-| `structure_json` | string | Full `DocumentStructure` (meta + stats + sections + paragraphs + sentences) as JSON; round-trips via `json.loads`. |
-| `extracted_json` | string | Regex NER + statute-link output (entities, relations, statute_refs) as JSON. |
+**Citations** — first-class `list<struct>`:
 
-### Precedent layer (án-lệ-only)
+| Column | Struct fields |
+|---|---|
+| `citations_law` (29,783) | `kind` (`provision`\|`document`) · **`ref`** (VI, ordered large→small `Chương→Mục→Điều→Khoản→Điểm` + law + `năm`) · `chapter`,`section`,`article`,`clause`,`point` · `law_type`,`law_name` (VI) · `id`,`year` · **`sentence_id`** (grounding → `sentences`) · **`span`** (char offsets `[[start,end],…]` into `markdown`) |
+| `citations_case` (4,238) | `id`,`number`,`year`,`code` · `role` (`first-instance`\|`appellate`\|`cassation`\|`protest`) · `domain`,`level` · `sentence_id`,`span` |
 
-| Field | Type | Description |
-|---|---|---|
-| `precedent_number` | string | E.g. `Án lệ số 47/2021/AL`. None for plain judgments. |
-| `adopted_date` | string | ISO 8601 adoption date. |
-| `applied_article_code` / `applied_article_number` / `applied_article_clause` | string / int64 / int64 | Most-cited statute reference. |
-| `principle_text` | string | "Nội dung án lệ" / "Nguyên tắc" excerpt when present. |
+Plus `citations_source` (`llm` \| `llm+section` \| `regex`), `num_law_citations`, `num_case_citations`. **79% of law citations carry a `sentence_id` + `span`** (anchored on `Điều <article>`; the rest are normative-by-number ids not found verbatim).
+
+**Content** — `markdown` (full judgment) · `markdown_chars` · `num_pages` · `confidence` · `flags` (list).
 
 Quick load:
 
 ```python
-import json
 from datasets import load_dataset
 
-ds = load_dataset("tmquan/anle-toaan-gov-vn", split="train")  # documents config
-row = ds[0]
-structure = json.loads(row["structure_json"])
-print(structure["meta"]["doc_code"])
-for sec in structure["sections"]:
-    print(sec["kind"], sec["label"])
+docs = load_dataset("tmquan/anle-toaan-gov-vn", split="train")   # documents config
+r = docs[0]
+print(r["official_document_id"], r["category"], r["instance_level"], r["court"])
+for c in r["citations_law"][:5]:
+    print(c["ref"], "→", list(c["sentence_id"]))   # citation + its grounding sentence(s)
 ```
 
-## Companion stages · `sentences` + `embed` + `reduce`
+## Companion configs · `sentences` + `embed` + `reduce`
 
-Alongside the default `documents-*.parquet` shards (one row per document, with markdown + structure), the dataset ships up to three additional parquet bundles that mirror the **extract → embed → reduce** stages 1-to-1. All three join back to the `documents` table on the `doc_name` primary key.
+Three parquet bundles accompany `documents`; all join on the `doc_name` primary key, and `sentences` also links to citations via `sentence_id`.
 
-### `sentences-*.parquet` — sentence-level rows
+### `sentences-*.parquet` — sentence-level rows (169,698)
 
-One row per sentence; the full hierarchical structure (`section → paragraph → sentence`) is exposed as flat parquet rows so consumers can stream, filter, and embed sentences directly without parsing the `structure_json` blob.
+Sentence-split from the shipped `markdown` (conservative Vietnamese-legal splitter); `char_start`/`char_end` index the *same* `markdown`, so `citations_*.span` and `citations_*.sentence_id` line up exactly.
 
 | Field | Type | Description |
 |---|---|---|
-| `doc_name` | string | Join key back to `documents-*.parquet`. |
-| `sentence_id` | string | Stable per-corpus sentence id (`<doc_id>::s<g>` form). |
-| `paragraph_id` / `section_id` | string | Parent paragraph / section ids. |
-| `case_type` / `doc_type` / `doc_subtype` / `court_level` / `year` / `precedent_number` | string / int32 | Parent-document filter columns (promoted so consumers can slice without joining). |
-| `section_kind` | string | `header` \| `case_summary` \| `findings` \| `decision` \| `footer`. |
-| `paragraph_kind` | string | `text` \| `numbered_finding` \| `numbered_decision` \| `list_item` \| `signature` \| ... |
-| `paragraph_marker` | string | The marker as it appears in the body (e.g. `[1]`, `1.`, `-`). |
-| `page` | int32 | Page number inside the parent PDF. |
-| `index_in_paragraph` / `global_index` | int32 | Position inside the parent paragraph / inside the document. |
-| `char_start` / `char_end` | int32 | Char span back into the parent `markdown`. |
-| `text` | string | The sentence itself, NFC-normalised. |
-
-Quick load:
+| `sentence_id` | string | `{doc_name}#{sent_idx}` — the grounding id referenced by `citations_*.sentence_id`. |
+| `doc_name` | string | Join key back to `documents`. |
+| `sent_idx` | int | Sentence index within the document. |
+| `char_start` / `char_end` | int | Char span into the parent `documents.markdown`. |
+| `text` | string | The sentence. |
+| `category` / `instance_level` / `year` / `precedent_number` | string / int | Parent-document facets (promoted for slicing without a join). |
 
 ```python
-
 from datasets import load_dataset
-
-
 sents = load_dataset("tmquan/anle-toaan-gov-vn", "sentences", split="train")
-print(sents[0]["text"])
-# Filter to all sentences from civil-law cassation precedents
-civil_cassation = sents.filter(
-    lambda r: r["case_type"] == "dan_su" and r["doc_subtype"] == "giam_doc_tham"
-)
+# every sentence that grounds a citation of Điều 468
+hits = sents.filter(lambda r: "Điều 468" in r["text"])
 ```
 
-### `embed-*.parquet` — dense vectors
+### `embed-*.parquet` — dense vectors (1,844)
 
-One row per embeddable document (1,844 rows). The default embedder is `nvidia/Nemotron-3-Embed-8B-BF16` (4096-D). The full set of *predefined* models the pipeline can route to is published in `manifest.json["pipeline"]["embed"]["registry"]`:
-
-| Model | Dim | Native window | Notes |
-|---|---:|---:|---|
-| `nvidia/llama-nemotron-embed-1b-v2` | 2048 | 8192 | Default. 1B params, 8k context. |
-| `nvidia/llama-3.2-nv-embedqa-1b-v2` | 1024 | 512 | Previous ViLA default (retrieval, 512-tok window). |
-| `nvidia/llama-embed-nemotron-8b` | 4096 | 8192 | 8B params, higher quality. |
-| `nvidia/Nemotron-3-Embed-1B-BF16` | 2048 | 32768 | NVIDIA Nemotron-3 embedding, 1B params, mean pooling, 32k native (local HF). |
-| `nvidia/Nemotron-3-Embed-8B-BF16` | 4096 | 32768 | NVIDIA Nemotron-3 embedding, 8B params, mean pooling, 32k native (local HF). |
-| `sentence-transformers/paraphrase-multilingual-mpnet-base-v2` | 768 | 128 | Multilingual MPNet (50+ langs incl. VI). |
-| `microsoft/harrier-oss-v1-270m` | — | 32768 | 270 M params, 32k native context. |
-| `microsoft/harrier-oss-v1-0.6b` | — | 32768 | Lightweight HF default; 32k native. |
-| `microsoft/harrier-oss-v1-27b` | — | 32768 | Highest quality in the harrier-oss family. |
+`nvidia/Nemotron-3-Embed-8B-BF16` (**4096-d**, "Nemotron-3" = v3), **`chunking=sentence`** (sentence-boundary sliding windows + mean-pool), run in-process on a GB10.
 
 | Field | Type | Description |
 |---|---|---|
-| `doc_name` | string | Join key back to `documents-*.parquet`. |
-| `text_hash` | string | SHA-256 of the post-normalisation markdown. |
-| `embedding` | list&lt;float32&gt; | **4096-D** dense vector. |
-| `embedding_dim` | int64 | Length of `embedding` (denormalised for fast filtering). |
-| `embedding_model_id` | string | Model slug as the backend reports it. |
-| `embedding_text_hash` | string | SHA-256 of the exact text fed to the embedder. |
-| `embedding_chunks_used` | int64 | Windows mean-pooled into the final vector (1 if the doc fits in one window). |
-| `embedding_chunking` | string | `off` / `sliding` / `sentence`. |
+| `doc_name` | string | Join key. |
+| `embedding` | list&lt;float32&gt; | **4096-d** dense vector (L2-normalised). |
+| `embedding_dim` | int | 4096. |
+| `embedding_model_id` | string | `nvidia/Nemotron-3-Embed-8B-BF16`. |
 
-### `reduce-*.parquet` — 2D projections + cluster ids
+### `reduce-*.parquet` — 2D projections (1,844)
 
-One row per embeddable document. Every method in `['pca', 'tsne', 'umap']` runs over the full embedding matrix with `n_components=2`; the clusterer is `hdbscan` (label `-1` = noise bucket).
+PCA + t-SNE + UMAP fit over the full embedding matrix with `n_components=2` (HDBSCAN clustering is off in this release).
 
 | Field | Type | Description |
 |---|---|---|
-| `doc_name` / `text_hash` | string | Join keys back to `documents-*.parquet` and `embed-*.parquet`. |
-| `pca_x` | float64 | PCA projection (axis x). |
-| `pca_y` | float64 | PCA projection (axis y). |
-| `tsne_x` | float64 | TSNE projection (axis x). |
-| `tsne_y` | float64 | TSNE projection (axis y). |
-| `umap_x` | float64 | UMAP projection (axis x). |
-| `umap_y` | float64 | UMAP projection (axis y). |
-| `cluster_id` | int64 | HDBSCAN cluster label; `-1` is the noise bucket. |
-
-Join back to documents:
+| `doc_name` | string | Join key. |
+| `pca_x` / `pca_y` | float | PCA 2D projection. |
+| `tsne_x` / `tsne_y` | float | t-SNE 2D projection. |
+| `umap_x` / `umap_y` | float | UMAP 2D projection. |
 
 ```python
-
 from datasets import load_dataset
-
 docs   = load_dataset("tmquan/anle-toaan-gov-vn", "documents", split="train").to_pandas()
 embed  = load_dataset("tmquan/anle-toaan-gov-vn", "embed",     split="train").to_pandas()
 reduce = load_dataset("tmquan/anle-toaan-gov-vn", "reduce",    split="train").to_pandas()
 joined = docs.merge(embed, on="doc_name").merge(reduce, on="doc_name")
 ```
 
-## Trực quan hoá embedding · Embedding visualization
-
-Mỗi điểm là một văn bản; toạ độ là vector embedding 4096-D từ `nvidia/Nemotron-3-Embed-8B-BF16` chiếu xuống 2D bằng UMAP, cụm bằng HDBSCAN. Mỗi hình một hàng. — Each dot is one document; coordinates are the 2D UMAP projection of a 4096-D embedding from `nvidia/Nemotron-3-Embed-8B-BF16`, with HDBSCAN cluster ids. One figure per row.
+## Trực quan hoá · Visualizations
 
-PCA và t-SNE vẫn được tính sẵn và lưu trong `reduce-*.parquet` (`pca_{x,y,z}`, `tsne_{x,y,z}`) — chỉ không vẽ trong card này. — PCA and t-SNE coordinates are still pre-computed and shipped in `reduce-*.parquet` (`pca_{x,y,z}`, `tsne_{x,y,z}`); they are simply not rendered inline here.
+### Sơ đồ Sankey trích dẫn · Citation Sankey — category · subcategory → document → cited provision
 
-### UMAP colored by `case_type`
+Mỗi luồng là một trích dẫn pháp luật: bắt đầu từ *lĩnh vực · cấp xét xử* của bản án, đi qua *tài liệu* (toàn bộ 1.844 bản án), tới *điều khoản được viện dẫn* (`Điều → Khoản → Điểm`, kể cả `Chương/Mục`). Màu luồng theo lĩnh vực·cấp. — Each flow is one law-citation: from the judgment's *legal category · court level*, through the *document* (all 1,844 judgments), to the *cited provision* (`Điều → Khoản → Điểm`, incl. `Chương/Mục`). Links are coloured by category·subcategory.
 
-![UMAP colored by `case_type`](./embedding-case-type-umap.png)
+![Citation Sankey](./sankey-category-document-citation.png)
 
-### UMAP colored by `doc_subtype`
+Bản tương tác (zoom/hover) · Interactive version: [`sankey-category-document-citation.html`](./sankey-category-document-citation.html)
 
-![UMAP colored by `doc_subtype`](./embedding-doc-subtype-umap.png)
+### Chiếu embedding 2D · 2D embedding projections
 
-### UMAP colored by `court_level`
+Mỗi điểm là một văn bản; toạ độ là vector embedding 4096-D từ `nvidia/Nemotron-3-Embed-8B-BF16` chiếu xuống 2D bằng **t-SNE** và **UMAP**. Toạ độ PCA/t-SNE/UMAP đều lưu trong `reduce-*.parquet`. — Each dot is one document; coordinates are the 2D **t-SNE** and **UMAP** projections of a 4096-D `nvidia/Nemotron-3-Embed-8B-BF16` embedding. PCA/t-SNE/UMAP coordinates all ship in `reduce-*.parquet`.
 
-![UMAP colored by `court_level`](./embedding-court-level-umap.png)
+#### PCA — theo lĩnh vực · by legal category
+![PCA by category](./embedding-pca-category.png)
 
-### UMAP colored by `cluster_id`
+#### t-SNE — theo lĩnh vực · by legal category
+![t-SNE by category](./embedding-tsne-category.png)
 
-![UMAP colored by `cluster_id`](./embedding-cluster-id-umap.png)
+#### UMAP — theo lĩnh vực · by legal category
+![UMAP by category](./embedding-umap-category.png)
 
 ## Cách thu thập + chuẩn hoá · How the corpus was built
 

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