Datasets:
task_categories:
- translation
language:
- de
- en
Dataset Description:
Dataset Name: English-German Translation Pairs for Machine Learning.
Note: This dataset mainly focuses on formal conversation.
Dataset Overview:
This dataset is a meticulously curated collection of English-German translation pairs, designed specifically for training machine learning models, particularly those focused on machine translation tasks. With a total of 48,400,531 translation pairs, this dataset offers an extensive and diverse set of examples that can significantly enhance the training of models aiming to achieve high accuracy in English-to-German and German-to-English translation.
Key Features:
Size: The dataset contains over 48 million translation pairs, making it one of the largest publicly available datasets for English-German machine translation.
Format: The data is provided in a JSONL (JSON Lines) format, which is optimized for easy and efficient training in various machine learning frameworks.
Language Pair: English (Source) to German (Target) translations.
Text Characteristics:
- Average English Words per Sentence: 18.43
- Average German Words per Sentence: 17.09
Quality: The dataset has been carefully constructed to ensure high-quality translations, making it an ideal resource for training and evaluating translation models.
Potential Use Cases:
- Training machine translation models (e.g., Transformer-based models).
- Developing bilingual corpora for research in natural language processing (NLP).
- Enhancing the performance of multilingual models.
- Evaluating the effectiveness of different translation algorithms.
How to Use:
The dataset can be easily integrated into training pipelines that support JSONL format. Researchers and developers can use this dataset to fine-tune pre-trained models or to train new models from scratch, focusing on improving translation accuracy and fluency between English and German.
License:
Creative Commons Attribution (CC-BY) license.
Contributors:
Darth-Vaderr(Data Scientist, Germany) (@X: Im_Mr_Lazy)