Korean Translation Dataset For NLP Models by Kaggle | Other

About this Dataset

Korean Translation Dataset For NLP Models

Korean Translation Dataset for NLP Models

Translated Instructions and Input-Output Pairs in Korean

By nlpai-lab (From Huggingface) [source]

About this dataset

This dataset provides a collection of translations from English to Korean for NLP models such as GPT4ALL, Dolly, and Vicuna Data. The translations were generated using the DeepL API. It contains three columns: instruction represents the instruction given to the model for the translation task, input is the input text that needs to be translated from English to Korean, and output is the corresponding translated text in Korean. The dataset aims to facilitate research and development in natural language processing tasks by providing a reliable source of translated data

How to use the dataset

This dataset contains Korean translations of instructions, inputs, and outputs for various NLP models including GPT4ALL, Dolly, and Vicuna Data. The translations were generated using the DeepL API.

Description of Columns

The dataset consists of the following columns:

instruction: This column contains the original instruction given to the model for the translation task.

input: This column contains the input text in English that needs to be translated to Korean.

output: This column contains the translated text in Korean.

How to Utilize this Dataset

You can use this dataset for various natural language processing (NLP) tasks such as machine translation or training language models specifically focused on English-Korean translation.

Here are a few steps on how you can utilize this dataset effectively:

Importing Data: Load or import the provided train.csv file into your Python environment or preferred programming language.

Data Preprocessing: Clean and preprocess both input and output texts if needed. You may consider tokenization, removing stopwords, or any other preprocessing techniques that align with your specific task requirements.

Model Training: Utilize deep learning frameworks like PyTorch or TensorFlow to develop your NLP model focused on English-Korean translation using this prepared dataset as training data.

Evaluation & Fine-tuning: Evaluate your trained model's performance using suitable metrics such as BLEU score or perplexity measurement techniques specific to machine translation tasks. Fine-tune your model by iterating over different architectures and hyperparameters based on evaluation results until desired performance is achieved.

Inference & Deployment: Once you are satisfied with your trained model's performance, use it for making predictions on unseen English texts which need translation into Korean within any application where it can provide meaningful value.

Remember that this dataset was translated using DeepL API; thus, you can leverage these translations as a starting point for your NLP projects. However, it is essential to validate and further refine the translations according to your specific use case or domain requirements.

Good luck with your NLP projects using this Korean Translation Dataset!

Research Ideas

Training and evaluating machine translation models: This dataset can be used to train and evaluate machine translation models for translating English text to Korean. The instruction column provides specific instructions given to the model, while the input column contains the English text that needs to be translated. The output column contains the corresponding translations in Korean.

Language learning and practice: This dataset can be used by language learners who want to practice translating English text into Korean. Users can compare their own translations with the provided translations in the output column to improve their language skills.

Benchmarking different translation APIs or models: The dataset includes translations generated using the DeepL API, but it can also be used as a benchmark for comparing other translation APIs or models. By comparing the performance of different systems on this dataset, researchers and developers can gain insights into the strengths and weaknesses of different translation approaches

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: train.csv

Column name	Description
instruction (translated instruction)	This column contains the translated instructions provided to the model for each translation task. (Text)
input (translated input text)	This column contains the translated input text that needs to be translated from English to Korean. (Text)
output (translated output text)	This column contains the translated output in Korean after performing translation on the given input. (Text)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit nlpai-lab (From Huggingface).

Tables

Train

@kaggle.thedevastator_korean_translation_dataset_for_nlp_models.train

120.11 MB
152630 rows
4 columns


CREATE TABLE train (
  "id" VARCHAR,
  "instruction" VARCHAR,
  "input" VARCHAR,
  "output" VARCHAR
);