# Summary

This is a Thai 🇹🇭-instructed dataset translated from cleaned version of the original Alpaca Dataset released by Stanford using Google Cloud Translation, contain 52,000 instructions and demonstrations generated by OpenAI's `text-davinci-003` engine. 
This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.

The following issues have been identified in the original release and fixed in this dataset:
1. **Hallucinations:** Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer.
2. **Merged Instructions:** There were many instructions that were merged together in the original dataset for some reason.
3. **Empty outputs:** Some entries in the original dataset had empty outputs.
4. **Empty code examples:** Some descriptions in the original dataset were missing code examples, making it difficult to understand the intended behavior of the code.
5. **Instructions to generate images:** Some descriptions in the original dataset included instructions to generate images, something obviously not possible.
6. **N/A outputs:** Some code snippets in the original dataset had N/A outputs.
7. **Inconsistent input field:** The original dataset had inconsistent usage of the input field when it was supposed to be empty.
8. **Wrong answers:** Some instructions/questions in the original dataset had incorrect answers. About 80% of the math problems are estimated to have incorrect answers.
9. **Non-Sensical/Unclear instructions:** Many instructions are unclear, we try to clarify (or re-write) if instructions are non-sensical. Instructions that are slightly unclear, but where one could deduce the meaning are not altered.
10. **Extraneous escape and control characters:** The original dataset had several entries with extraneous escape and control characters.

### Original Alpaca Dataset Summary

Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's `text-davinci-003` engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.

The authors built on the data generation pipeline from [Self-Instruct framework](https://github.com/yizhongw/self-instruct) and made the following modifications:

- The `text-davinci-003` engine to generate the instruction data instead of `davinci`.
- A [new prompt](https://github.com/tatsu-lab/stanford_alpaca/blob/main/prompt.txt) was written that explicitly gave the requirement of instruction generation to `text-davinci-003`.
- Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.
- The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.
- Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.


The authors built on the data generation pipeline from [Self-Instruct framework](https://github.com/yizhongw/self-instruct) and made the following modifications:

- The `text-davinci-003` engine to generate the instruction data instead of `davinci`.
- A [new prompt](https://github.com/tatsu-lab/stanford_alpaca/blob/main/prompt.txt) was written that explicitly gave the requirement of instruction generation to `text-davinci-003`.
- Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.
- The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.
- Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.

This produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500). 
In a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by [Self-Instruct](https://github.com/yizhongw/self-instruct/blob/main/data/seed_tasks.jsonl).


Supported Tasks: 
- Training LLMs
- Synthetic Data Generation
- Data Augmentation
  
Languages: Thai
Version: 1.0
---
task_categories:question-answering task_categories:summarization size_categories:10K<n<100K language:th license:cc-by-sa-3.0 instruction-finetuning region:us
{"language": ["th"], "license": "cc-by-sa-3.0", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering", "summarization"], "tags": ["instruction-finetuning"]}
TAGS
#task_categories-question-answering #task_categories-summarization #size_categories-10K<n<100K #language-Thai #license-cc-by-sa-3.0 #instruction-finetuning #region-us 

# Summary

This is a Thai 🇹🇭-instructed dataset translated from cleaned version of the original Alpaca Dataset released by Stanford using Google Cloud Translation, contain 52,000 instructions and demonstrations generated by OpenAI's 'text-davinci-003' engine. 
This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.

The following issues have been identified in the original release and fixed in this dataset:
1. Hallucinations: Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer.
2. Merged Instructions: There were many instructions that were merged together in the original dataset for some reason.
3. Empty outputs: Some entries in the original dataset had empty outputs.
4. Empty code examples: Some descriptions in the original dataset were missing code examples, making it difficult to understand the intended behavior of the code.
5. Instructions to generate images: Some descriptions in the original dataset included instructions to generate images, something obviously not possible.
6. N/A outputs: Some code snippets in the original dataset had N/A outputs.
7. Inconsistent input field: The original dataset had inconsistent usage of the input field when it was supposed to be empty.
8. Wrong answers: Some instructions/questions in the original dataset had incorrect answers. About 80% of the math problems are estimated to have incorrect answers.
9. Non-Sensical/Unclear instructions: Many instructions are unclear, we try to clarify (or re-write) if instructions are non-sensical. Instructions that are slightly unclear, but where one could deduce the meaning are not altered.
10. Extraneous escape and control characters: The original dataset had several entries with extraneous escape and control characters.

### Original Alpaca Dataset Summary

Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's 'text-davinci-003' engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.

The authors built on the data generation pipeline from Self-Instruct framework and made the following modifications:

- The 'text-davinci-003' engine to generate the instruction data instead of 'davinci'.
- A new prompt was written that explicitly gave the requirement of instruction generation to 'text-davinci-003'.
- Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.
- The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.
- Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.


The authors built on the data generation pipeline from Self-Instruct framework and made the following modifications:

- The 'text-davinci-003' engine to generate the instruction data instead of 'davinci'.
- A new prompt was written that explicitly gave the requirement of instruction generation to 'text-davinci-003'.
- Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.
- The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.
- Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.

This produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500). 
In a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by Self-Instruct.


Supported Tasks: 
- Training LLMs
- Synthetic Data Generation
- Data Augmentation
  
Languages: Thai
Version: 1.0
---
# Summary

This is a Thai 🇹🇭-instructed dataset translated from cleaned version of the original Alpaca Dataset released by Stanford using Google Cloud Translation, contain 52,000 instructions and demonstrations generated by OpenAI's 'text-davinci-003' engine. 
This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.

The following issues have been identified in the original release and fixed in this dataset:
1. Hallucinations: Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer.
2. Merged Instructions: There were many instructions that were merged together in the original dataset for some reason.
3. Empty outputs: Some entries in the original dataset had empty outputs.
4. Empty code examples: Some descriptions in the original dataset were missing code examples, making it difficult to understand the intended behavior of the code.
5. Instructions to generate images: Some descriptions in the original dataset included instructions to generate images, something obviously not possible.
6. N/A outputs: Some code snippets in the original dataset had N/A outputs.
7. Inconsistent input field: The original dataset had inconsistent usage of the input field when it was supposed to be empty.
8. Wrong answers: Some instructions/questions in the original dataset had incorrect answers. About 80% of the math problems are estimated to have incorrect answers.
9. Non-Sensical/Unclear instructions: Many instructions are unclear, we try to clarify (or re-write) if instructions are non-sensical. Instructions that are slightly unclear, but where one could deduce the meaning are not altered.
10. Extraneous escape and control characters: The original dataset had several entries with extraneous escape and control characters. ### Original Alpaca Dataset Summary

Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's 'text-davinci-003' engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.

The authors built on the data generation pipeline from Self-Instruct framework and made the following modifications:

- The 'text-davinci-003' engine to generate the instruction data instead of 'davinci'.
- A new prompt was written that explicitly gave the requirement of instruction generation to 'text-davinci-003'.
- Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.
- The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.
- Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.


The authors built on the data generation pipeline from Self-Instruct framework and made the following modifications:

- The 'text-davinci-003' engine to generate the instruction data instead of 'davinci'.
- A new prompt was written that explicitly gave the requirement of instruction generation to 'text-davinci-003'.
- Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.
- The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.
- Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.

This produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500). 
In a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by Self-Instruct.


Supported Tasks: 
- Training LLMs
- Synthetic Data Generation
- Data Augmentation
  
Languages: Thai
Version: 1.0
---
TAGS
#task_categories-question-answering #task_categories-summarization #size_categories-10K<n<100K #language-Thai #license-cc-by-sa-3.0 #instruction-finetuning #region-us 
 # Summary

This is a Thai 🇹🇭-instructed dataset translated from cleaned version of the original Alpaca Dataset released by Stanford using Google Cloud Translation, contain 52,000 instructions and demonstrations generated by OpenAI's 'text-davinci-003' engine. 
This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.

The following issues have been identified in the original release and fixed in this dataset:
1. Hallucinations: Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer.
2. Merged Instructions: There were many instructions that were merged together in the original dataset for some reason.
3. Empty outputs: Some entries in the original dataset had empty outputs.
4. Empty code examples: Some descriptions in the original dataset were missing code examples, making it difficult to understand the intended behavior of the code.
5. Instructions to generate images: Some descriptions in the original dataset included instructions to generate images, something obviously not possible.
6. N/A outputs: Some code snippets in the original dataset had N/A outputs.
7. Inconsistent input field: The original dataset had inconsistent usage of the input field when it was supposed to be empty.
8. Wrong answers: Some instructions/questions in the original dataset had incorrect answers. About 80% of the math problems are estimated to have incorrect answers.
9. Non-Sensical/Unclear instructions: Many instructions are unclear, we try to clarify (or re-write) if instructions are non-sensical. Instructions that are slightly unclear, but where one could deduce the meaning are not altered.
10. Extraneous escape and control characters: The original dataset had several entries with extraneous escape and control characters. ### Original Alpaca Dataset Summary

Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's 'text-davinci-003' engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.

The authors built on the data generation pipeline from Self-Instruct framework and made the following modifications:

- The 'text-davinci-003' engine to generate the instruction data instead of 'davinci'.
- A new prompt was written that explicitly gave the requirement of instruction generation to 'text-davinci-003'.
- Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.
- The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.
- Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.


The authors built on the data generation pipeline from Self-Instruct framework and made the following modifications:

- The 'text-davinci-003' engine to generate the instruction data instead of 'davinci'.
- A new prompt was written that explicitly gave the requirement of instruction generation to 'text-davinci-003'.
- Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.
- The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.
- Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.

This produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500). 
In a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by Self-Instruct.


Supported Tasks: 
- Training LLMs
- Synthetic Data Generation
- Data Augmentation
  
Languages: Thai
Version: 1.0
---
passage: TAGS
#task_categories-question-answering #task_categories-summarization #size_categories-10K<n<100K #language-Thai #license-cc-by-sa-3.0 #instruction-finetuning #region-us 
# Summary

This is a Thai 🇹🇭-instructed dataset translated from cleaned version of the original Alpaca Dataset released by Stanford using Google Cloud Translation, contain 52,000 instructions and demonstrations generated by OpenAI's 'text-davinci-003' engine. 
This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.

The following issues have been identified in the original release and fixed in this dataset:
1. Hallucinations: Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer.
2. Merged Instructions: There were many instructions that were merged together in the original dataset for some reason.
3. Empty outputs: Some entries in the original dataset had empty outputs.
4. Empty code examples: Some descriptions in the original dataset were missing code examples, making it difficult to understand the intended behavior of the code.
5. Instructions to generate images: Some descriptions in the original dataset included instructions to generate images, something obviously not possible.
6. N/A outputs: Some code snippets in the original dataset had N/A outputs.
7. Inconsistent input field: The original dataset had inconsistent usage of the input field when it was supposed to be empty.
8. Wrong answers: Some instructions/questions in the original dataset had incorrect answers. About 80% of the math problems are estimated to have incorrect answers.
9. Non-Sensical/Unclear instructions: Many instructions are unclear, we try to clarify (or re-write) if instructions are non-sensical. Instructions that are slightly unclear, but where one could deduce the meaning are not altered.
10. Extraneous escape and control characters: The original dataset had several entries with extraneous escape and control characters.