LongAlpaca 12K

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Neural Machine Translation Yukang

LongAlpaca - Generating instruct datasets from language models (longform)

By Huggingface Hub [source]

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About this dataset

The LongAlpaca-12k dataset provides researchers with an invaluable resource for machine learning applications. Consisting of approximately 12000 instructional Yukang documents paired with their associated translated outputs and inputs, the dataset presents various opportunities to explore how neural machine translation can be used to cultivate successful interpreters. This data offers a vast array of informative features ranging from file names and language metrics that can be used in a multitude of tasks, including sequence-to-sequence translation, natural language understanding, and more. Widely acclaimed by data scientists as an admirable benchmark for quantitative leading edge training models in both academia and industry, LongAlpaca-12k is essential for anyone hoping to discover the true potential of Yukang Machine Translation (YMT). With its abundance of variables accessible at your fingertips choices abound as to how you may take full advantage this valuable open source dataset desired by many in the ML space

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How to use the dataset

How to Use this Dataset

The LongAlpaca-12k dataset is an open-source machine translation dataset consisting of approximately 12,000 instructional Yukang documents and their corresponding translated input and output translations. It is perfect for anyone interested in Yukang NMT for research purposes. The data can be used for a variety of tasks, such as language modeling, sequence-to-sequence translation, natural language understanding and more.

In order to use this dataset in the most effective way possible, we suggest taking the following steps:

• Download the Train.csv file from Kaggle containing the document pairings as well as various metrics associated with each text pair;
• Get familiar with each column: ‘file’ will be important when referring to specific entries within your dataset; ‘output’ contains output translations while ‘input’ contains the original texts;
• Analyze descriptive features such as word count/length or quality score per entry if available (tells you how close machine's guess was to human translation);
• Select a subset of the data that suits your particular needs – e.g., labeled sentiment analysis or version control statements;
  5 . Create training, validation and test sets if necessary (divide documents into three sections);
  6 . Build models applying different strategies such as BERT or XLM – experiment with which works best on different tasks; 7 . Validate findings using annotation tools like Doccano - quantify bias through human annotators; liberate historical documents previously unavailable due by geographic restrictions! 8 . Deploy models into production process recognizing that training & testing set performance not necessarily reflects real world scenarios – i t could vary significantly under varying circumstances!

Research Ideas

• Developing a sequence-to-sequence model to automatically generate translations for Yukang instructional texts.
• Exploring the semantic relationship between different language pairs by using a distance matrix approach.
• Creating a deep learning model to evaluate the quality of machine generated translations by comparing them with the original inputs and outputs in this dataset

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

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit Huggingface Hub.