---
license: odc-by
task_categories:
- token-classification
- text-classification
language:
- cs
tags:
- czech
- punctuation
- pos-tagging
- syntax
- neuro-symbolic
- nanoGPT
size_categories:
- 10K<n<100K
pretty_name: Czech Punctuation, POS and Syntactic Dataset
configs:
- config_name: default
  data_files:
  - split: train
    path: train/train_data.parquet
  - split: validation
    path: validation/validation_data.parquet
  - split: test
    path: test/test_data.parquet
---

# Czech Punctuation, POS and Syntactic Dataset 🇨🇿
### A High-Quality Dataset for Punctuation Restoration and Neuro-Symbolic LLM Grounding

This dataset is a structured, linguistically annotated corpus of the Czech language, specifically designed for **Punctuation Restoration tasks**, **Part-of-Speech (POS) tagging**, and **token-level syntax embedding** (such as nanoGPT custom metadata training). 

Unlike pure raw text corpora, this dataset provides a deterministic 1:1 token-level mapping of morphological features (word class, grammatical case) and dependency syntax roles (subject, predicate, object) directly derived via Stanford's state-of-the-art **Stanza pipeline** (trained on the Prague Dependency Treebank standard).

---

## 📊 Dataset Structure

The dataset contains three official subsets split using a strict, reproducible **80% / 10% / 10%** distribution with seed shuffling:
* **Train:** 80% of sentences
* **Validation:** 10% of sentences
* **Test:** 10% of sentences

### Data Schema
Every split is delivered in the optimized Apache Parquet format utilizing the following strict schema layout:

| Column Name | Type | Description |
| :--- | :--- | :--- |
| `segment` | `string` | The original raw Czech sentence structure (text segment). |
| `punctuation_type` | `string` | Classification target category based on the sentence ending/comma presence (`čárka`, `tečka`, `otazník`, `vykřičník`, `žádná`). |
| `tokens_annotation` | `list (struct)` | An array of structured token profiles (excluding raw punctuation marks). |

#### Inside `tokens_annotation` Struct:
* `slovo` (*string*): The clean isolated token/word.
* `slovni_druh` (*string*): Czech morphological category (`podstatne_jmeno`, `sloveso`, `zajmeno`, `prislovce`, `predlozka`, `jiny`).
* `vetny_clen` (*string*): Syntactic dependency function (`podmet`, `prisudek`, `predmet`, `jiny`).
* `pad` (*int64*): Real Czech grammatical case number (`1` to `7`), or `0` for indeclinable word classes.

---

## 💻 Usage Example

You can instantly load this dataset using the Hugging Face `datasets` library:

```python
from datasets import load_dataset

# Load the full dataset splits
dataset = load_dataset("KRadim/czech-punctuation-pos-syntax")

# Access individual splits
train_data = dataset["train"]
validation_data = dataset["validation"]
test_data = dataset["test"]

# Inspect an example row
print(train_data[0])
```

## JSON Record Preview

```json
{
  "segment": "Co je na nich tak tajného?",
  "punctuation_type": "otazník",
  "tokens_annotation": [
    { "slovo": "Co", "slovni_druh": "zajmeno", "vetny_clen": "podmet", "pad": 1 },
    { "slovo": "je", "slovni_druh": "sloveso", "vetny_clen": "jiny", "pad": 0 },
    { "slovo": "na", "slovni_druh": "predlozka", "vetny_clen": "jiny", "pad": 6 },
    { "slovo": "nich", "slovni_druh": "zajmeno", "vetny_clen": "jiny", "pad": 6 },
    { "slovo": "tak", "slovni_druh": "prislovce", "vetny_clen": "jiny", "pad": 0 },
    { "slovo": "tajného", "slovni_druh": "jiny", "vetny_clen": "prisudek", "pad": 2 }
  ]
}
```

## 🛠️ Pipeline Construction Method

1. **Ingestion & Balance:** Aggregated raw multi-domain source datasets uniformly distributed across 5 structural punctuation categories.
2. **Deep Neural Annotation:** Processed locally using Stanford Stanza's Czech syntax parser. This ensures authentic dependency parsing and prevents the data hallucinations often introduced by Generative LLMs.
3. **Symbolic Normalization:** Native UPOS tags and feature vectors mapped cleanly to Czech vocabulary definitions (`podmet`, `prisudek`, pádové reprezentace).
4. **Cloud-Native Serialization:** Combined, shuffled (`seed=42`), sliced, and cast into an explicit PyArrow Table Layout with Snappy compression for instantaneous loading times.

## Detailed procedure for creating a dataset:

1. **Part 1:** ⚙️ [Create Czech text dataset for training](https://www.kaggle.com/code/radimkzl/czech-text-dataset)
2. **Part 2:** 🔬 [CZECH NEURO-SYMBOLIC DATASET TRANSFORMER (STANZA VERSION)](https://www.kaggle.com/code/radimkzl/enrichment-czech-text-dataset)
3. **Part 3:** 💾 [FINAL PRODUCTION EXPORT & HUGGING FACE PUSH (80/10/10 SPLIT)](https://www.kaggle.com/code/radimkzl/export-czech-punctuation-pos-syntax-dataset#FINAL-PRODUCTION-EXPORT-&-HUGGING-FACE-PUSH-(80/10/10-SPLIT))

## 📜 Licensing & Acknowledgments

The text annotations and structural database components are compiled from open Czech resources and processed via the Stanford Stanza framework. This dataset is made available under the **Open Data Commons Attribution License (ODC-BY v1.0)**. 
You are free to share, modify, and use the dataset for commercial or non-commercial purposes, provided that you attribute the source appropriately.