Dataset + kanana/qwen3 embeddings

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README.md

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+---
+language:
+  - ko
+  - en
+license: other
+license_name: cc-by-sa-2.0-kr
+license_link: https://creativecommons.org/licenses/by-sa/2.0/kr/
+homepage: https://github.com/taylorsm9/korean_proverbs
+pretty_name: Korean proverbs → English (Urimalsaem)
+tags:
+  - korean
+  - proverbs
+  - urimalsaem
+  - machine-translation
+  - 속담
+size_categories:
+  - 10K<n<100K
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: data/korean_proverbs_en.parquet
+annotations_creators:
+  - machine-generated
+source_datasets:
+  - original
+task_categories:
+  - translation
+---
+
+# Korean proverbs → English
+
+Every proverb (속담) entry in the Urimalsaem dictionary (우리말샘, National Institute of Korean Language), machine-translated to English. Code and pipeline details: <https://github.com/taylorsm9/korean_proverbs>.
+
+## Structure of this dataset (read this first)
+
+Urimalsaem is a sense-level dictionary: a single proverb can have more than one recorded meaning, and each meaning is a separate entry. This dataset follows that structure.
+
+- **One row per dictionary sense.** 10,763 rows total.
+- **9,612 unique proverbs.** 1,094 proverbs have two or more senses; the other 8,518 have one.
+- When a proverb has multiple senses, the same `ko` string appears on multiple rows, each with its own `definition_ko` and its own English translation. All senses of a given proverb share the same `headword_raw`.
+- `sense_no` is Urimalsaem's own ordering within a headword (`"001"`, `"002"`, …). This dataset does not pick a primary sense for you.
+
+**If you want one row per proverb**, you need to pick a sense yourself. The simplest options:
+
+```python
+# Keep the lowest-numbered sense per proverb (Urimalsaem's own order):
+one_per = df.sort_values(["headword_raw", "sense_no"]).drop_duplicates("headword_raw", keep="first")
+```
+
+## Scale
+
+| Measure | Value |
+|---|---|
+| Total rows | 10,763 |
+| Unique proverbs (unique `headword_raw`) | 9,612 |
+| Proverbs with multiple senses | 1,094 |
+
+## Load it
+
+```python
+from datasets import load_dataset
+ds = load_dataset("NLPatVCU/korean_proverb", split="train")
+df = ds.to_pandas()
+```
+
+Or load the parquet directly:
+
+```python
+import pandas as pd
+from huggingface_hub import hf_hub_download
+path = hf_hub_download("NLPatVCU/korean_proverb", "data/korean_proverbs_en.parquet", repo_type="dataset")
+df = pd.read_parquet(path)
+```
+
+## Columns
+
+| Column | Description |
+|---|---|
+| `id` | Urimalsaem `target_code` for this sense |
+| `headword_raw` | Raw headword text, including Urimalsaem's `[A/B]` / `(C)` variant notation |
+| `ko` | Canonical form of the proverb (variant notation stripped) |
+| `sense_no` | Sense number within this proverb, as assigned by Urimalsaem (`"001"`, `"002"`, …) |
+| `type` | Urimalsaem's usage-category label for this sense (`일반어` is the standard-usage code; see Urimalsaem for the other codes) |
+| `definition_ko` | Dictionary definition (synonym tag and following synonym list are stripped out) |
+| `synonyms_ko` | The stripped synonym list: proverb-length strings that are their own dictionary entries |
+| `proverb_en` | English translation of the proverb |
+| `definition_en` | English translation of the definition |
+
+## Companion embeddings
+
+Vector embeddings of the Korean and English texts, computed with two models, are shipped as separate parquet files:
+
+```
+embeddings/
+  kanana.parquet         # 1,792-dim
+  qwen3.parquet          # 4,096-dim
+```
+
+Each row carries `id` plus four `list[float32]` columns: `ko_embedding`, `definition_ko_embedding`, `proverb_en_embedding`, `definition_en_embedding`. Join on `id`:
+
+```python
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+df  = pd.read_parquet(hf_hub_download("NLPatVCU/korean_proverb", "data/korean_proverbs_en.parquet", repo_type="dataset"))
+emb = pd.read_parquet(hf_hub_download("NLPatVCU/korean_proverb", "embeddings/kanana.parquet",       repo_type="dataset"))
+joined = df.merge(emb, on="id")
+```
+
+Both the proverb side and the definition side are embedded with the same prompt, so they share one space (useful for cosine similarity between a proverb and its own meaning). For Qwen3-Embedding the prompt is:
+
+```
+Instruct: Given a Korean or English text, retrieve texts with the same meaning
+Query:
+```
+
+## Source
+
+- 우리말샘 (Urimalsaem), National Institute of Korean Language (국립국어원).
+- Dump dated 2026-06-02, via the GitHub mirror [`spellcheck-ko/korean-dict-nikl`](https://github.com/spellcheck-ko/korean-dict-nikl), pinned to commit `42c0d01`, folder `opendict/` only.
+
+## License
+
+- The **dataset** (Korean proverbs, Korean definitions, English translations): **CC-BY-SA-2.0-KR**, matching Urimalsaem. See <https://creativecommons.org/licenses/by-sa/2.0/kr/>. Attribution: 국립국어원 (NIKL), 우리말샘.
+- `embeddings/kanana.parquet`: **CC-BY-NC-4.0** (non-commercial only), inherited from `kakaocorp/kanana-nano-2.1b-embedding`.
+- `embeddings/qwen3.parquet`: **Apache-2.0**, inherited from `Qwen/Qwen3-Embedding-8B`.
+
+## Translation setup
+
+- Model: `nvidia/Qwen3.5-122B-A10B-NVFP4` served by vLLM 0.28.0 on an NVIDIA DGX Spark.
+- Thinking off (`enable_thinking: false`) on every request.
+- Sampling: `temperature=0.7, top_p=0.8, top_k=20, min_p=0, presence_penalty=0, repetition_penalty=1.0, max_tokens=1024`.
+- Seed per row: `int(id)`.
+- Output constrained to a Pydantic JSON schema via `response_format`.
+- No translation hit the `max_tokens=1024` ceiling. The longest completion seen was 156 tokens.
+
+## Limitations
+
+- Machine-translated; no human review.
+- Multiple rows share the same `ko` when a proverb has multiple senses. Dedupe on `headword_raw` if you want unique proverb strings (see "Structure" above).
+- Only the canonical form of each proverb is translated; variant notation in `headword_raw` is not expanded.
+
+## Citation
+
+If you use this dataset, please cite the source dictionary:
+
+> 국립국어원 (National Institute of Korean Language). 우리말샘 (Urimalsaem).
+> Available via [`spellcheck-ko/korean-dict-nikl`](https://github.com/spellcheck-ko/korean-dict-nikl).

data/korean_proverbs_en.parquet

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embeddings/README.md

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+# Embeddings
+
+Per-model parquet files. Each file carries one row per proverb-sense (`id`
+joins back to the main dataset), with four `list[float32]` columns:
+
+- `ko_embedding`: embedding of the Korean proverb (`ko`)
+- `definition_ko_embedding`: embedding of the Korean definition
+- `proverb_en_embedding`: embedding of the English translation
+- `definition_en_embedding`: embedding of the English definition
+
+| File | Model | Dim | License |
+|---|---|---|---|
+| `kanana.parquet` | `kakaocorp/kanana-nano-2.1b-embedding` | 1,792 | **CC-BY-NC-4.0** (non-commercial) |
+| `qwen3.parquet`  | `Qwen/Qwen3-Embedding-8B`              | 4,096 | Apache-2.0 |
+
+All texts are embedded with the model's query prompt applied identically on
+both sides (both proverb and definition are treated as queries), so a
+same-row proverb and definition live in the same embedding space.
+
+Both models receive the **same** instruction string, applied verbatim as a
+prefix to every text (Korean and English, proverb and definition):
+
+```
+Instruct: Given a Korean or English text, retrieve texts with the same meaning
+Query: 
+```
+
+Per-model specifics:
+
+- `kanana.parquet` (`kakaocorp/kanana-nano-2.1b-embedding`): the prefix
+  above is prepended to every input; pooling uses `include_prompt=false`
+  (mean-pool over non-prompt tokens), per the Kanana model card.
+- `qwen3.parquet` (`Qwen/Qwen3-Embedding-8B`): the prefix above is passed
+  as the model's `custom_prompt` (the Qwen3-Embedding `Instruct: … \nQuery: `
+  template), applied identically to every input.
+
+Vectors are L2-normalized float32.

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