README.md

---
dataset_info:
  features:
  - name: id
    dtype: int64
  - name: latin
    dtype: string
  - name: german
    dtype: string
  - name: source
    dtype: string
  - name: tag
    dtype: string
  - name: score
    dtype: float64
  splits:
  - name: train
    num_bytes: 154924781
    num_examples: 406011
  download_size: 91837604
  dataset_size: 154924781
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- translation
language:
- la
- de
size_categories:
- 100K<n<1M
pretty_name: Latin-German-Textcorpus
---

# 📜 Latin-German Textcorpus

This dataset consists of 406,011 Latin-German parallel sentences (sentence pairs).
Each entry contains a Latin sentence and its corresponding German translation.
The sentence pairs were collected and processed from various websites and online sources.


## 📄 Dataset Schema

The dataset contains the following columns:

* **id**: A **unique identifier** for each entry.
* **latin**: The sentence in **Latin**.
* **german**: The **German** translation.
* **source**: The origin or reference from where the entry was taken.
* **tag**: A thematic **category** or label indicating the entry's historical period. Possible values are: **'ANCIENT'**, **'MEDIEVAL'**, **'MODERN'**, or **'UNKNOWN'**.
* **score**: A numerical **relevance** or similarity score. A value of **-1** indicates the score is undefined, while any value **>= 1** represents a valid score.


## 📚 Citation / References

Falls Sie dieses Modell in Ihrer Forschung verwenden, bitten wir Sie, die zugrundeliegende Masterarbeit wie folgt zu zitieren:

**Masterarbeit (Zenodo DOI):**

Wenzel, M. (2025). Translatio ex Machina: Neuronale Maschinelle Übersetzung vom Lateinischen ins Deutsche [Zenodo]. Unveröffentlichte Masterarbeit, Fachhochschule Südwestfalen
 
DOI: **[10.5281/zenodo.17940090](https://doi.org/10.5281/zenodo.17940090)**

-----

## 💻 Usage

### Understanding the `score` Feature

The **`score`** column indicates the method and quality of the sentence alignment:

  * **`score` = -1:** This value signifies that the Latin and German sentences were **manually aligned** or by a special tooling.
  * **`score` \>= 1:** This value indicates that the alignment was **calculated by an automated alignment tool**.
      * **Interpretation:** A higher score suggests a **better alignment quality**.
      * **Recommendation:** For high-confidence automated alignments, we recommend using only entries where the score is **>= 1.2**.

### Loading and Filtering the Dataset

You can easily filter the dataset to select only high-quality alignments (manual alignments OR high-scoring automated alignments) using the `filter()` method:

```python
from datasets import load_dataset, DatasetDict

# 1. Load the initial dataset (contains only the "train" split)
dataset = load_dataset("fhswf/latin-german-parallel")
train_dataset = dataset["train"]

# 2. Filter the dataset to include only high-quality alignments:
#    - Entries with score == -1 (manual alignment)
#    - Entries with score >= 1.2 (high-confidence automated alignment)
def filter_by_score(example):
    return example["score"] == -1 or example["score"] >= 1.2

high_quality_train = train_dataset.filter(filter_by_score)

# Optional: Proceed with splitting the high-quality data
temp_splits = high_quality_train.train_test_split(test_size=0.01, seed=42)

test_validation_splits = temp_splits["test"].train_test_split(test_size=0.5, seed=42)

dataset = DatasetDict({
    "train": temp_splits["train"],
    "validation": test_validation_splits["train"],
    "test": test_validation_splits["test"],
})
```