Alpaca Cleaned

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Alpaca Cleaned

Improving Pretrained Language Model Understanding

By Huggingface Hub [source]

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About this dataset

Alpaca is the perfect dataset for fine-tuning your language models to better understand and follow instructions, capable of taking you beyond standard Natural Language Processing (NLP) abilities! This curated, cleaned dataset provides you with over 52,000 expertly crafted instructions and demonstrations generated by OpenAI's text-davinci-003 engine - all in English (BCP-47 en). Improve the quality of your language models with fields such as instruction, output, and input which have been designed to enhance every aspect of their comprehension. The data here has gone through rigorous cleaning to ensure there are no errors or biases present; allowing you to trust that this data will result in improved performance for any language model that uses it! Get ready to see what Alpaca can do for your NLP needs

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Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides a unique and valuable resource for anyone who wishes to create, develop and train language models. Alpaca provides users with 52,000 instruction-demonstration pairs generated by OpenAI's text-davinci-003 engine.

The data included in this dataset is formatted into 3 columns: “instruction”, “output” and “input.” All the data is written in English (BCP-47 en).

To make the most out of this dataset it is recommended to:

• Familiarize yourself with the instructions in the instruction column as these provide guidance on how to use the other two columns; input and output.

• Once comfortable with understanding the instructions columns move onto exploring what you are provided within each 14 sets of triplets – instruction, output and input – that are included in this clean version of Alpaca.

• Read through many examples paying attention to any areas where you feel more clarification could be added or could be further improved upon for better understanding of language models however bear in mind that these examples have been cleaned from any errors or biases found from original dataset

• Get inspired! As mentioned earlier there are more than 52k sets provided meaning having much flexibility for varying training strategies or unique approaches when creating your own language model!

• Finally while not essential it may be helpful to have familiarity with OpenAI's text-davinci engine as well as enjoy playing around with different parameters/options depending on what type of outcomes you wish achieve

Research Ideas

• Developing natural language processing (NLP) tasks that aim to better automate and interpret instructions given by humans.
• Training machine learning models of robotic agents to be able to understand natural language commands, as well as understand the correct action that needs to be taken in response.
• Creating a system that can generate personalized instructions and feedback in real time based on language models, catering specifically to each individual user's preferences or needs

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

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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.