
## Dataset Description

- **Homepage:** [teleprint.me](https://teleprint.me)
- **Repository:** [phi-1](https://huggingface.co/datasets/teleprint-me/phi-1)
- **Paper:** [2306.11644v1](https://arxiv.org/abs/2306.11644v1)
- **Leaderboard:** [Link to the leaderboard]
- **Point of Contact:** [aberrio@teleprint.me](aberrio@teleprint.me)

### Dataset Summary

This dataset is created for training the phi-1 model, based on the paper
"Textbooks are All You Need". It contains high-quality data derived from various
textbooks, transformed and synthesized using OpenAI's GPT-3.5 and GPT-4 models.

For optimal results, it is recommended to train models with the following
parameters and sequence lengths:

- For a model with 350M parameters, use a sequence length of 2048.
- For a model with 700M parameters, use a sequence length of 4096.
- For a model with 1.3B parameters, use a sequence length of 8096.

Please note that the dataset is currently in its initial phase of planning and
collection. The process involves preparing the data, extracting it, formatting
it, chunking it, and preparing it for synthesis. Scripts for preparing and
processing the data for the model will be developed. Once the data is generated,
it will undergo a review and revision process to ensure its quality and
relevance.

These recommendations and notes are based on the dataset creator's initial plans
and may be subject to change as the project progresses.

**NOTE**: Due to the nature of this dataset, it cannot be released without
obtaining permissions from the respective publishers and/or authors. If you are
an author or publisher and have any concerns about this repository, please feel
free to email me.

If you are an author or publisher and would like to grant permission for the use
of your work, your support would be greatly appreciated. Please note that in
order for the dataset to be released, permissions would need to be unanimous
from all involved parties.

In the absence of such permissions, I will respect the copyrights of the
copyrighted materials and exercise my right to Fair Use with my own physical
property for personal use.

**This dataset is NOT intended for commercial purposes**. Its primary purpose is
for research in machine learning and AI software development. If a model is
created using this dataset, it will be shared under the same license.

Any proceeds derived from donations will be primarily used for the development
of the dataset and the model.

### Supported Tasks and Leaderboards

- `text-generation`: The dataset can be used to train a model for chat-like text
  generation, more specifically, for generating explanations and examples in the
  context of arithmetic, algebra, geometry, trigonometry, calculus, algorithms
  and data structures, design patterns, and the python programming language.

### Languages

The text in the dataset is in English.

## Dataset Structure

### Data Instances

A data instance consists of a dialogue between a user and an assistant,
discussing a topic in arithmetic, algebra, geometry, trigonometry, calculus,
algorithms and data structures, design patterns, or the Python programming
language. The dialogue is structured as a list of turns, each turn containing
the role ("user" or "assistant") and the content of the turn.

### Data Fields

- `role`: a string indicating the role of the speaker in the dialogue ("system",
  "user", "assistant", "function").
- `content`: a string containing the content of the speaker's turn in the
  dialogue.

### Data Splits

The dataset is split into a training set, a validation set, and a test set. The
exact sizes and proportions of these splits will depend on the final size of the
dataset.

## Dataset Creation

### Curation Rationale

The dataset is being created to train a model capable of generating explanations
and examples in the context of various mathematical and computer science topics.
The goal is to create an AI assistant that can provide clear, accurate, and
pedagogically sound responses to user queries on these topics.

### Source Data

#### Initial Data Collection and Normalization

The data is collected from a variety of textbooks covering arithmetic, algebra,
geometry, trigonometry, calculus, algorithms and data structures, design
patterns, and the Python programming language. The textbooks used include:

- Barron's Arithmetic The Easy Way Fourth Edition
- Blitzer Introductory Algebra for College Students Fifth Edition
- McDougal Littell Geometry
- Blitzer Intermediate Algebra for College Students 5th Edition
- Trigonometry Sixth Edition
- Pearson College Algebra Fourth Edition
- Hughes-Hallet Applied Calculus 5th Edition
- CLRS Introduction to Algorithms Third Edition

In addition to the textbooks, the dataset also includes material from the
following online resources:

- [C reference](https://en.cppreference.com/w/c)
- [Cpp reference](https://en.cppreference.com/w/cpp)
- [Python Standard Library](https://docs.python.org/3/)

These resources provide up-to-date information and examples for the C, C++, and
Python programming languages. The creators of the Cppreference site also provide
[archives](https://en.cppreference.com/w/Cppreference:Archives) of their site
for offline use. Code samples synthesized by OpenAI's GPT models, curated by the
dataset creator, are also included in the dataset.

**Note:** The creator of this dataset owns physical copies of all the textbooks
listed above. The data from these sources are transformed into a dialogue format
using OpenAI's GPT-3.5 and GPT-4 models. The resulting dialogues are then used
as the training data for the phi-1 model. This dataset does not include the full
content of the source textbooks. Instead, it consists of transformations and
syntheses of the original content. Anyone who wants access to the full original
content should purchase or otherwise legally access the textbooks themselves.

#### Who are the source language producers?

The original language data was created by a variety of authors and educators,
who wrote the textbooks and other materials used as sources for this dataset.
These include:

- Barron's Arithmetic The Easy Way Fourth Edition - Edward Williams, Katie
  Prindle
- Blitzer Introductory Algebra for College Students Fifth Edition - Robert
  Blitzer
- McDougal Littell Geometry - Ron Larson, Laurie Boswell, Timothy D. Kanold, Lee
  Stiff
- Blitzer Intermediate Algebra for College Students 5th Edition - Robert Blitzer
- Trigonometry Sixth Edition - Charles P. McKeague, Mark D. Turner
- Pearson College Algebra Fourth Edition - Robert F. Blitzer
- Hughes-Hallet Applied Calculus 5th Edition - Deborah Hughes-Hallett, Andrew M.
  Gleason, Patti Frazer Lock, Daniel E. Flath, Sheldon P. Gordon, David O.
  Lomen, David Lovelock, William G. McCallum, Brad G. Osgood, Andrew Pasquale,
  Jeff Tecosky-Feldman, Joseph Thrash, Karen R. Rhea, Thomas W. Tucker
- CLRS Introduction to Algorithms Third Edition - Thomas H. Cormen, Charles E.
  Leiserson, Ronald L. Rivest, Clifford Stein

In addition to these authors, the developers of OpenAI's GPT-3.5 and GPT-4
models also contributed to the creation of the language data, as these models
were used to transform the source material into a dialogue format.

### Annotations

#### Annotation process

The dataset does not contain any explicit annotations. However, the data is
curated and synthesized using OpenAI's GPT-3.5 and GPT-4 models. The process
involves transforming the source material into a dialogue format suitable for
training the phi-1 model. The dataset creator, an independent learner with a
strong interest in computer science, reviewed and curated the synthesized
dialogues to ensure their quality and relevance.

#### Who are the annotators?

The dataset creator, an independent learner who has studied computer science
extensively in a self-directed manner, performed the curation and review of the
synthesized dialogues.

### Personal and Sensitive Information

The dataset does not contain any personal or sensitive information. All the data
is derived from publicly available textbooks and online resources. Any names or
other potential identifiers in the source material have been removed or
anonymized.

### Social Impact of Dataset

The dataset is intended to support the development of AI models capable of
providing detailed explanations and examples in the context of arithmetic,
algebra, geometry, trigonometry, calculus, algorithms and data structures,
design patterns, and the python programming language. The potential social
impact is significant, as such models could greatly enhance self-directed
learning and provide valuable educational support to students worldwide.

However, it's important to note that the quality and usefulness of the AI models
trained on this dataset will depend on the quality of the data itself. If the
data is inaccurate or biased, the models could propagate these inaccuracies and
biases, potentially leading to misinformation or unfair outcomes.

### Discussion of Biases

The dataset is based on a variety of textbooks and online resources, which may
contain their own inherent biases. For example, textbooks often reflect the
perspectives and biases of their authors, which can influence the way
information is presented. These biases could potentially be reflected in the
dataset and in any models trained on it.

### Other Known Limitations

At this stage of the dataset creation process, it's difficult to identify all
potential limitations. However, one potential limitation is that the dataset may
not cover all possible topics or perspectives within the fields it addresses.
The dataset creator will continue to monitor and assess the dataset for
limitations as the work progresses.

## Additional Information

### Dataset Curators

The dataset was curated by an independent learner with a strong interest in
computer science. The curator has studied the subject matter in a self-directed
manner, using a variety of resources including textbooks and online materials.
The curation process also involved the use of OpenAI's GPT-3.5 and GPT-4 models
to synthesize dialogues based on the source material.

### Licensing Information

This dataset is released under the Creative Commons
Attribution-NonCommercial-ShareAlike 3.0 International (CC BY-NC-SA 3.0)
license.

### Citation Information

As this dataset is a compilation of various sources synthesized and curated for
the purpose of training the phi-1 model, please ensure to cite the original
sources when using this dataset. If referencing the dataset directly, please
refer to this repository.

license:cc-by-nc-sa-3.0 arxiv:2306.11644 region:us
{"license": "cc-by-nc-sa-3.0", "title": "Phi-1 Model Dataset", "date": "2023-07-03"}
TAGS
#license-cc-by-nc-sa-3.0 #arxiv-2306.11644 #region-us 


## Dataset Description

- Homepage: URL
- Repository: phi-1
- Paper: 2306.11644v1
- Leaderboard: [Link to the leaderboard]
- Point of Contact: aberrio@URL

### Dataset Summary

This dataset is created for training the phi-1 model, based on the paper
"Textbooks are All You Need". It contains high-quality data derived from various
textbooks, transformed and synthesized using OpenAI's GPT-3.5 and GPT-4 models.

For optimal results, it is recommended to train models with the following
parameters and sequence lengths:

- For a model with 350M parameters, use a sequence length of 2048.
- For a model with 700M parameters, use a sequence length of 4096.
- For a model with 1.3B parameters, use a sequence length of 8096.

Please note that the dataset is currently in its initial phase of planning and
collection. The process involves preparing the data, extracting it, formatting
it, chunking it, and preparing it for synthesis. Scripts for preparing and
processing the data for the model will be developed. Once the data is generated,
it will undergo a review and revision process to ensure its quality and
relevance.

These recommendations and notes are based on the dataset creator's initial plans
and may be subject to change as the project progresses.

NOTE: Due to the nature of this dataset, it cannot be released without
obtaining permissions from the respective publishers and/or authors. If you are
an author or publisher and have any concerns about this repository, please feel
free to email me.

If you are an author or publisher and would like to grant permission for the use
of your work, your support would be greatly appreciated. Please note that in
order for the dataset to be released, permissions would need to be unanimous
from all involved parties.

In the absence of such permissions, I will respect the copyrights of the
copyrighted materials and exercise my right to Fair Use with my own physical
property for personal use.

This dataset is NOT intended for commercial purposes. Its primary purpose is
for research in machine learning and AI software development. If a model is
created using this dataset, it will be shared under the same license.

Any proceeds derived from donations will be primarily used for the development
of the dataset and the model.

### Supported Tasks and Leaderboards

- 'text-generation': The dataset can be used to train a model for chat-like text
  generation, more specifically, for generating explanations and examples in the
  context of arithmetic, algebra, geometry, trigonometry, calculus, algorithms
  and data structures, design patterns, and the python programming language.

### Languages

The text in the dataset is in English.

## Dataset Structure

### Data Instances

A data instance consists of a dialogue between a user and an assistant,
discussing a topic in arithmetic, algebra, geometry, trigonometry, calculus,
algorithms and data structures, design patterns, or the Python programming
language. The dialogue is structured as a list of turns, each turn containing
the role ("user" or "assistant") and the content of the turn.

### Data Fields

- 'role': a string indicating the role of the speaker in the dialogue ("system",
  "user", "assistant", "function").
- 'content': a string containing the content of the speaker's turn in the
  dialogue.

### Data Splits

The dataset is split into a training set, a validation set, and a test set. The
exact sizes and proportions of these splits will depend on the final size of the
dataset.

## Dataset Creation

### Curation Rationale

The dataset is being created to train a model capable of generating explanations
and examples in the context of various mathematical and computer science topics.
The goal is to create an AI assistant that can provide clear, accurate, and
pedagogically sound responses to user queries on these topics.

### Source Data

#### Initial Data Collection and Normalization

The data is collected from a variety of textbooks covering arithmetic, algebra,
geometry, trigonometry, calculus, algorithms and data structures, design
patterns, and the Python programming language. The textbooks used include:

- Barron's Arithmetic The Easy Way Fourth Edition
- Blitzer Introductory Algebra for College Students Fifth Edition
- McDougal Littell Geometry
- Blitzer Intermediate Algebra for College Students 5th Edition
- Trigonometry Sixth Edition
- Pearson College Algebra Fourth Edition
- Hughes-Hallet Applied Calculus 5th Edition
- CLRS Introduction to Algorithms Third Edition

In addition to the textbooks, the dataset also includes material from the
following online resources:

- C reference
- Cpp reference
- Python Standard Library

These resources provide up-to-date information and examples for the C, C++, and
Python programming languages. The creators of the Cppreference site also provide
archives of their site
for offline use. Code samples synthesized by OpenAI's GPT models, curated by the
dataset creator, are also included in the dataset.

Note: The creator of this dataset owns physical copies of all the textbooks
listed above. The data from these sources are transformed into a dialogue format
using OpenAI's GPT-3.5 and GPT-4 models. The resulting dialogues are then used
as the training data for the phi-1 model. This dataset does not include the full
content of the source textbooks. Instead, it consists of transformations and
syntheses of the original content. Anyone who wants access to the full original
content should purchase or otherwise legally access the textbooks themselves.

#### Who are the source language producers?

The original language data was created by a variety of authors and educators,
who wrote the textbooks and other materials used as sources for this dataset.
These include:

- Barron's Arithmetic The Easy Way Fourth Edition - Edward Williams, Katie
  Prindle
- Blitzer Introductory Algebra for College Students Fifth Edition - Robert
  Blitzer
- McDougal Littell Geometry - Ron Larson, Laurie Boswell, Timothy D. Kanold, Lee
  Stiff
- Blitzer Intermediate Algebra for College Students 5th Edition - Robert Blitzer
- Trigonometry Sixth Edition - Charles P. McKeague, Mark D. Turner
- Pearson College Algebra Fourth Edition - Robert F. Blitzer
- Hughes-Hallet Applied Calculus 5th Edition - Deborah Hughes-Hallett, Andrew M.
  Gleason, Patti Frazer Lock, Daniel E. Flath, Sheldon P. Gordon, David O.
  Lomen, David Lovelock, William G. McCallum, Brad G. Osgood, Andrew Pasquale,
  Jeff Tecosky-Feldman, Joseph Thrash, Karen R. Rhea, Thomas W. Tucker
- CLRS Introduction to Algorithms Third Edition - Thomas H. Cormen, Charles E.
  Leiserson, Ronald L. Rivest, Clifford Stein

In addition to these authors, the developers of OpenAI's GPT-3.5 and GPT-4
models also contributed to the creation of the language data, as these models
were used to transform the source material into a dialogue format.

### Annotations

#### Annotation process

The dataset does not contain any explicit annotations. However, the data is
curated and synthesized using OpenAI's GPT-3.5 and GPT-4 models. The process
involves transforming the source material into a dialogue format suitable for
training the phi-1 model. The dataset creator, an independent learner with a
strong interest in computer science, reviewed and curated the synthesized
dialogues to ensure their quality and relevance.

#### Who are the annotators?

The dataset creator, an independent learner who has studied computer science
extensively in a self-directed manner, performed the curation and review of the
synthesized dialogues.

### Personal and Sensitive Information

The dataset does not contain any personal or sensitive information. All the data
is derived from publicly available textbooks and online resources. Any names or
other potential identifiers in the source material have been removed or
anonymized.

### Social Impact of Dataset

The dataset is intended to support the development of AI models capable of
providing detailed explanations and examples in the context of arithmetic,
algebra, geometry, trigonometry, calculus, algorithms and data structures,
design patterns, and the python programming language. The potential social
impact is significant, as such models could greatly enhance self-directed
learning and provide valuable educational support to students worldwide.

However, it's important to note that the quality and usefulness of the AI models
trained on this dataset will depend on the quality of the data itself. If the
data is inaccurate or biased, the models could propagate these inaccuracies and
biases, potentially leading to misinformation or unfair outcomes.

### Discussion of Biases

The dataset is based on a variety of textbooks and online resources, which may
contain their own inherent biases. For example, textbooks often reflect the
perspectives and biases of their authors, which can influence the way
information is presented. These biases could potentially be reflected in the
dataset and in any models trained on it.

### Other Known Limitations

At this stage of the dataset creation process, it's difficult to identify all
potential limitations. However, one potential limitation is that the dataset may
not cover all possible topics or perspectives within the fields it addresses.
The dataset creator will continue to monitor and assess the dataset for
limitations as the work progresses.

## Additional Information

### Dataset Curators

The dataset was curated by an independent learner with a strong interest in
computer science. The curator has studied the subject matter in a self-directed
manner, using a variety of resources including textbooks and online materials.
The curation process also involved the use of OpenAI's GPT-3.5 and GPT-4 models
to synthesize dialogues based on the source material.

### Licensing Information

This dataset is released under the Creative Commons
Attribution-NonCommercial-ShareAlike 3.0 International (CC BY-NC-SA 3.0)
license.



As this dataset is a compilation of various sources synthesized and curated for
the purpose of training the phi-1 model, please ensure to cite the original
sources when using this dataset. If referencing the dataset directly, please
refer to this repository.

## Dataset Description

- Homepage: URL
- Repository: phi-1
- Paper: 2306.11644v1
- Leaderboard: [Link to the leaderboard]
- Point of Contact: aberrio@URL ### Dataset Summary

This dataset is created for training the phi-1 model, based on the paper
"Textbooks are All You Need". It contains high-quality data derived from various
textbooks, transformed and synthesized using OpenAI's GPT-3.5 and GPT-4 models.

For optimal results, it is recommended to train models with the following
parameters and sequence lengths:

- For a model with 350M parameters, use a sequence length of 2048.
- For a model with 700M parameters, use a sequence length of 4096.
- For a model with 1.3B parameters, use a sequence length of 8096.

Please note that the dataset is currently in its initial phase of planning and
collection. The process involves preparing the data, extracting it, formatting
it, chunking it, and preparing it for synthesis. Scripts for preparing and
processing the data for the model will be developed. Once the data is generated,
it will undergo a review and revision process to ensure its quality and
relevance.

These recommendations and notes are based on the dataset creator's initial plans
and may be subject to change as the project progresses.

NOTE: Due to the nature of this dataset, it cannot be released without
obtaining permissions from the respective publishers and/or authors. If you are
an author or publisher and have any concerns about this repository, please feel
free to email me.

If you are an author or publisher and would like to grant permission for the use
of your work, your support would be greatly appreciated. Please note that in
order for the dataset to be released, permissions would need to be unanimous
from all involved parties.

In the absence of such permissions, I will respect the copyrights of the
copyrighted materials and exercise my right to Fair Use with my own physical
property for personal use.

This dataset is NOT intended for commercial purposes. Its primary purpose is
for research in machine learning and AI software development. If a model is
created using this dataset, it will be shared under the same license.

Any proceeds derived from donations will be primarily used for the development
of the dataset and the model. ### Supported Tasks and Leaderboards

- 'text-generation': The dataset can be used to train a model for chat-like text
  generation, more specifically, for generating explanations and examples in the
  context of arithmetic, algebra, geometry, trigonometry, calculus, algorithms
  and data structures, design patterns, and the python programming language. ### Languages

The text in the dataset is in English. ## Dataset Structure ### Data Instances

A data instance consists of a dialogue between a user and an assistant,
discussing a topic in arithmetic, algebra, geometry, trigonometry, calculus,
algorithms and data structures, design patterns, or the Python programming
language. The dialogue is structured as a list of turns, each turn containing
the role ("user" or "assistant") and the content of the turn. ### Data Fields

- 'role': a string indicating the role of the speaker in the dialogue ("system",
  "user", "assistant", "function").
- 'content': a string containing the content of the speaker's turn in the
  dialogue. ### Data Splits

The dataset is split into a training set, a validation set, and a test set. The
exact sizes and proportions of these splits will depend on the final size of the
dataset. ## Dataset Creation ### Curation Rationale

The dataset is being created to train a model capable of generating explanations
and examples in the context of various mathematical and computer science topics.
The goal is to create an AI assistant that can provide clear, accurate, and
pedagogically sound responses to user queries on these topics. ### Source Data #### Initial Data Collection and Normalization

The data is collected from a variety of textbooks covering arithmetic, algebra,
geometry, trigonometry, calculus, algorithms and data structures, design
patterns, and the Python programming language. The textbooks used include:

- Barron's Arithmetic The Easy Way Fourth Edition
- Blitzer Introductory Algebra for College Students Fifth Edition
- McDougal Littell Geometry
- Blitzer Intermediate Algebra for College Students 5th Edition
- Trigonometry Sixth Edition
- Pearson College Algebra Fourth Edition
- Hughes-Hallet Applied Calculus 5th Edition
- CLRS Introduction to Algorithms Third Edition

In addition to the textbooks, the dataset also includes material from the
following online resources:

- C reference
- Cpp reference
- Python Standard Library

These resources provide up-to-date information and examples for the C, C++, and
Python programming languages. The creators of the Cppreference site also provide
archives of their site
for offline use. Code samples synthesized by OpenAI's GPT models, curated by the
dataset creator, are also included in the dataset.

Note: The creator of this dataset owns physical copies of all the textbooks
listed above. The data from these sources are transformed into a dialogue format
using OpenAI's GPT-3.5 and GPT-4 models. The resulting dialogues are then used
as the training data for the phi-1 model. This dataset does not include the full
content of the source textbooks. Instead, it consists of transformations and
syntheses of the original content. Anyone who wants access to the full original
content should purchase or otherwise legally access the textbooks themselves. #### Who are the source language producers?

The original language data was created by a variety of authors and educators,
who wrote the textbooks and other materials used as sources for this dataset.
These include:

- Barron's Arithmetic The Easy Way Fourth Edition - Edward Williams, Katie
  Prindle
- Blitzer Introductory Algebra for College Students Fifth Edition - Robert
  Blitzer
- McDougal Littell Geometry - Ron Larson, Laurie Boswell, Timothy D. Kanold, Lee
  Stiff
- Blitzer Intermediate Algebra for College Students 5th Edition - Robert Blitzer
- Trigonometry Sixth Edition - Charles P. McKeague, Mark D. Turner
- Pearson College Algebra Fourth Edition - Robert F. Blitzer
- Hughes-Hallet Applied Calculus 5th Edition - Deborah Hughes-Hallett, Andrew M.
  Gleason, Patti Frazer Lock, Daniel E. Flath, Sheldon P. Gordon, David O.
  Lomen, David Lovelock, William G. McCallum, Brad G. Osgood, Andrew Pasquale,
  Jeff Tecosky-Feldman, Joseph Thrash, Karen R. Rhea, Thomas W. Tucker
- CLRS Introduction to Algorithms Third Edition - Thomas H. Cormen, Charles E.
  Leiserson, Ronald L. Rivest, Clifford Stein

In addition to these authors, the developers of OpenAI's GPT-3.5 and GPT-4
models also contributed to the creation of the language data, as these models
were used to transform the source material into a dialogue format. ### Annotations #### Annotation process

The dataset does not contain any explicit annotations. However, the data is
curated and synthesized using OpenAI's GPT-3.5 and GPT-4 models. The process
involves transforming the source material into a dialogue format suitable for
training the phi-1 model. The dataset creator, an independent learner with a
strong interest in computer science, reviewed and curated the synthesized
dialogues to ensure their quality and relevance. #### Who are the annotators?

The dataset creator, an independent learner who has studied computer science
extensively in a self-directed manner, performed the curation and review of the
synthesized dialogues. ### Personal and Sensitive Information

The dataset does not contain any personal or sensitive information. All the data
is derived from publicly available textbooks and online resources. Any names or
other potential identifiers in the source material have been removed or
anonymized. ### Social Impact of Dataset

The dataset is intended to support the development of AI models capable of
providing detailed explanations and examples in the context of arithmetic,
algebra, geometry, trigonometry, calculus, algorithms and data structures,
design patterns, and the python programming language. The potential social
impact is significant, as such models could greatly enhance self-directed
learning and provide valuable educational support to students worldwide.

However, it's important to note that the quality and usefulness of the AI models
trained on this dataset will depend on the quality of the data itself. If the
data is inaccurate or biased, the models could propagate these inaccuracies and
biases, potentially leading to misinformation or unfair outcomes. ### Discussion of Biases

The dataset is based on a variety of textbooks and online resources, which may
contain their own inherent biases. For example, textbooks often reflect the
perspectives and biases of their authors, which can influence the way
information is presented. These biases could potentially be reflected in the
dataset and in any models trained on it. ### Other Known Limitations

At this stage of the dataset creation process, it's difficult to identify all
potential limitations. However, one potential limitation is that the dataset may
not cover all possible topics or perspectives within the fields it addresses.
The dataset creator will continue to monitor and assess the dataset for
limitations as the work progresses. ## Additional Information ### Dataset Curators

The dataset was curated by an independent learner with a strong interest in
computer science. The curator has studied the subject matter in a self-directed
manner, using a variety of resources including textbooks and online materials.
The curation process also involved the use of OpenAI's GPT-3.5 and GPT-4 models
to synthesize dialogues based on the source material. ### Licensing Information

This dataset is released under the Creative Commons
Attribution-NonCommercial-ShareAlike 3.0 International (CC BY-NC-SA 3.0)
license.



As this dataset is a compilation of various sources synthesized and curated for
the purpose of training the phi-1 model, please ensure to cite the original
sources when using this dataset. If referencing the dataset directly, please
refer to this repository.
TAGS
#license-cc-by-nc-sa-3.0 #arxiv-2306.11644 #region-us 
 ## Dataset Description

- Homepage: URL
- Repository: phi-1
- Paper: 2306.11644v1
- Leaderboard: [Link to the leaderboard]
- Point of Contact: aberrio@URL ### Dataset Summary

This dataset is created for training the phi-1 model, based on the paper
"Textbooks are All You Need". It contains high-quality data derived from various
textbooks, transformed and synthesized using OpenAI's GPT-3.5 and GPT-4 models.

For optimal results, it is recommended to train models with the following
parameters and sequence lengths:

- For a model with 350M parameters, use a sequence length of 2048.
- For a model with 700M parameters, use a sequence length of 4096.
- For a model with 1.3B parameters, use a sequence length of 8096.

Please note that the dataset is currently in its initial phase of planning and
collection. The process involves preparing the data, extracting it, formatting
it, chunking it, and preparing it for synthesis. Scripts for preparing and
processing the data for the model will be developed. Once the data is generated,
it will undergo a review and revision process to ensure its quality and
relevance.

These recommendations and notes are based on the dataset creator's initial plans
and may be subject to change as the project progresses.

NOTE: Due to the nature of this dataset, it cannot be released without
obtaining permissions from the respective publishers and/or authors. If you are
an author or publisher and have any concerns about this repository, please feel
free to email me.

If you are an author or publisher and would like to grant permission for the use
of your work, your support would be greatly appreciated. Please note that in
order for the dataset to be released, permissions would need to be unanimous
from all involved parties.

In the absence of such permissions, I will respect the copyrights of the
copyrighted materials and exercise my right to Fair Use with my own physical
property for personal use.

This dataset is NOT intended for commercial purposes. Its primary purpose is
for research in machine learning and AI software development. If a model is
created using this dataset, it will be shared under the same license.

Any proceeds derived from donations will be primarily used for the development
of the dataset and the model. ### Supported Tasks and Leaderboards

- 'text-generation': The dataset can be used to train a model for chat-like text
  generation, more specifically, for generating explanations and examples in the
  context of arithmetic, algebra, geometry, trigonometry, calculus, algorithms
  and data structures, design patterns, and the python programming language. ### Languages

The text in the dataset is in English. ## Dataset Structure ### Data Instances

A data instance consists of a dialogue between a user and an assistant,
discussing a topic in arithmetic, algebra, geometry, trigonometry, calculus,
algorithms and data structures, design patterns, or the Python programming
language. The dialogue is structured as a list of turns, each turn containing
the role ("user" or "assistant") and the content of the turn. ### Data Fields

- 'role': a string indicating the role of the speaker in the dialogue ("system",
  "user", "assistant", "function").
- 'content': a string containing the content of the speaker's turn in the
  dialogue. ### Data Splits

The dataset is split into a training set, a validation set, and a test set. The
exact sizes and proportions of these splits will depend on the final size of the
dataset. ## Dataset Creation ### Curation Rationale

The dataset is being created to train a model capable of generating explanations
and examples in the context of various mathematical and computer science topics.
The goal is to create an AI assistant that can provide clear, accurate, and
pedagogically sound responses to user queries on these topics. ### Source Data #### Initial Data Collection and Normalization

The data is collected from a variety of textbooks covering arithmetic, algebra,
geometry, trigonometry, calculus, algorithms and data structures, design
patterns, and the Python programming language. The textbooks used include:

- Barron's Arithmetic The Easy Way Fourth Edition
- Blitzer Introductory Algebra for College Students Fifth Edition
- McDougal Littell Geometry
- Blitzer Intermediate Algebra for College Students 5th Edition
- Trigonometry Sixth Edition
- Pearson College Algebra Fourth Edition
- Hughes-Hallet Applied Calculus 5th Edition
- CLRS Introduction to Algorithms Third Edition

In addition to the textbooks, the dataset also includes material from the
following online resources:

- C reference
- Cpp reference
- Python Standard Library

These resources provide up-to-date information and examples for the C, C++, and
Python programming languages. The creators of the Cppreference site also provide
archives of their site
for offline use. Code samples synthesized by OpenAI's GPT models, curated by the
dataset creator, are also included in the dataset.

Note: The creator of this dataset owns physical copies of all the textbooks
listed above. The data from these sources are transformed into a dialogue format
using OpenAI's GPT-3.5 and GPT-4 models. The resulting dialogues are then used
as the training data for the phi-1 model. This dataset does not include the full
content of the source textbooks. Instead, it consists of transformations and
syntheses of the original content. Anyone who wants access to the full original
content should purchase or otherwise legally access the textbooks themselves. #### Who are the source language producers?

The original language data was created by a variety of authors and educators,
who wrote the textbooks and other materials used as sources for this dataset.
These include:

- Barron's Arithmetic The Easy Way Fourth Edition - Edward Williams, Katie
  Prindle
- Blitzer Introductory Algebra for College Students Fifth Edition - Robert
  Blitzer
- McDougal Littell Geometry - Ron Larson, Laurie Boswell, Timothy D. Kanold, Lee
  Stiff
- Blitzer Intermediate Algebra for College Students 5th Edition - Robert Blitzer
- Trigonometry Sixth Edition - Charles P. McKeague, Mark D. Turner
- Pearson College Algebra Fourth Edition - Robert F. Blitzer
- Hughes-Hallet Applied Calculus 5th Edition - Deborah Hughes-Hallett, Andrew M.
  Gleason, Patti Frazer Lock, Daniel E. Flath, Sheldon P. Gordon, David O.
  Lomen, David Lovelock, William G. McCallum, Brad G. Osgood, Andrew Pasquale,
  Jeff Tecosky-Feldman, Joseph Thrash, Karen R. Rhea, Thomas W. Tucker
- CLRS Introduction to Algorithms Third Edition - Thomas H. Cormen, Charles E.
  Leiserson, Ronald L. Rivest, Clifford Stein

In addition to these authors, the developers of OpenAI's GPT-3.5 and GPT-4
models also contributed to the creation of the language data, as these models
were used to transform the source material into a dialogue format. ### Annotations #### Annotation process

The dataset does not contain any explicit annotations. However, the data is
curated and synthesized using OpenAI's GPT-3.5 and GPT-4 models. The process
involves transforming the source material into a dialogue format suitable for
training the phi-1 model. The dataset creator, an independent learner with a
strong interest in computer science, reviewed and curated the synthesized
dialogues to ensure their quality and relevance. #### Who are the annotators?

The dataset creator, an independent learner who has studied computer science
extensively in a self-directed manner, performed the curation and review of the
synthesized dialogues. ### Personal and Sensitive Information

The dataset does not contain any personal or sensitive information. All the data
is derived from publicly available textbooks and online resources. Any names or
other potential identifiers in the source material have been removed or
anonymized. ### Social Impact of Dataset

The dataset is intended to support the development of AI models capable of
providing detailed explanations and examples in the context of arithmetic,
algebra, geometry, trigonometry, calculus, algorithms and data structures,
design patterns, and the python programming language. The potential social
impact is significant, as such models could greatly enhance self-directed
learning and provide valuable educational support to students worldwide.

However, it's important to note that the quality and usefulness of the AI models
trained on this dataset will depend on the quality of the data itself. If the
data is inaccurate or biased, the models could propagate these inaccuracies and
biases, potentially leading to misinformation or unfair outcomes. ### Discussion of Biases

The dataset is based on a variety of textbooks and online resources, which may
contain their own inherent biases. For example, textbooks often reflect the
perspectives and biases of their authors, which can influence the way
information is presented. These biases could potentially be reflected in the
dataset and in any models trained on it. ### Other Known Limitations

At this stage of the dataset creation process, it's difficult to identify all
potential limitations. However, one potential limitation is that the dataset may
not cover all possible topics or perspectives within the fields it addresses.
The dataset creator will continue to monitor and assess the dataset for
limitations as the work progresses. ## Additional Information ### Dataset Curators

The dataset was curated by an independent learner with a strong interest in
computer science. The curator has studied the subject matter in a self-directed
manner, using a variety of resources including textbooks and online materials.
The curation process also involved the use of OpenAI's GPT-3.5 and GPT-4 models
to synthesize dialogues based on the source material. ### Licensing Information

This dataset is released under the Creative Commons
Attribution-NonCommercial-ShareAlike 3.0 International (CC BY-NC-SA 3.0)
license.



As this dataset is a compilation of various sources synthesized and curated for
the purpose of training the phi-1 model, please ensure to cite the original
sources when using this dataset. If referencing the dataset directly, please
refer to this repository.
passage: TAGS
#license-cc-by-nc-sa-3.0 #arxiv-2306.11644 #region-us 
## Dataset Description

- Homepage: URL
- Repository: phi-1
- Paper: 2306.11644v1
- Leaderboard: [Link to the leaderboard]
- Point of Contact: aberrio@URL passage: ### Dataset Summary

This dataset is created for training the phi-1 model, based on the paper
"Textbooks are All You Need". It contains high-quality data derived from various
textbooks, transformed and synthesized using OpenAI's GPT-3.5 and GPT-4 models.

For optimal results, it is recommended to train models with the following
parameters and sequence lengths:

- For a model with 350M parameters, use a sequence length of 2048.
- For a model with 700M parameters, use a sequence length of 4096.
- For a model with 1.3B parameters, use a sequence length of 8096.

Please note that the dataset is currently in its initial phase of planning and
collection. The process involves preparing the data, extracting it, formatting
it, chunking it, and preparing it for synthesis. Scripts for preparing and
processing the data for the model will be developed. Once the data is generated,
it will undergo a review and revision process to ensure its quality and
relevance.

These recommendations and notes are based on the dataset creator's initial plans
and may be subject to change as the project progresses.

NOTE: Due to the nature of this dataset, it cannot be released without
obtaining permissions from the respective publishers and/or authors. If you are
an author or publisher and have any concerns about this repository, please feel
free to email me.

If you are an author or publisher and would like to grant permission for the use
of your work, your support would be greatly appreciated. Please note that in
order for the dataset to be released, permissions would need to be unanimous
from all involved parties.

In the absence of such permissions, I will respect the copyrights of the
copyrighted materials and exercise my right to Fair Use with my own physical
property for personal use.

This dataset is NOT intended for commercial purposes. Its primary purpose is
for research in machine learning and AI software development. If a model is
created using this dataset, it will be shared under the same license.

Any proceeds derived from donations will be primarily used for the development
of the dataset and the model.### Supported Tasks and Leaderboards

- 'text-generation': The dataset can be used to train a model for chat-like text
  generation, more specifically, for generating explanations and examples in the
  context of arithmetic, algebra, geometry, trigonometry, calculus, algorithms
  and data structures, design patterns, and the python programming language.### Languages

The text in the dataset is in English.## Dataset Structure### Data Instances

A data instance consists of a dialogue between a user and an assistant,
discussing a topic in arithmetic, algebra, geometry, trigonometry, calculus,
algorithms and data structures, design patterns, or the Python programming
language. The dialogue is structured as a list of turns, each turn containing
the role ("user" or "assistant") and the content of the turn.### Data Fields

- 'role': a string indicating the role of the speaker in the dialogue ("system",
  "user", "assistant", "function").
- 'content': a string containing the content of the speaker's turn in the
  dialogue.### Data Splits

The dataset is split into a training set, a validation set, and a test set. The
exact sizes and proportions of these splits will depend on the final size of the
dataset.## Dataset Creation### Curation Rationale

The dataset is being created to train a model capable of generating explanations
and examples in the context of various mathematical and computer science topics.
The goal is to create an AI assistant that can provide clear, accurate, and
pedagogically sound responses to user queries on these topics.### Source Data passage: #### Initial Data Collection and Normalization

The data is collected from a variety of textbooks covering arithmetic, algebra,
geometry, trigonometry, calculus, algorithms and data structures, design
patterns, and the Python programming language. The textbooks used include:

- Barron's Arithmetic The Easy Way Fourth Edition
- Blitzer Introductory Algebra for College Students Fifth Edition
- McDougal Littell Geometry
- Blitzer Intermediate Algebra for College Students 5th Edition
- Trigonometry Sixth Edition
- Pearson College Algebra Fourth Edition
- Hughes-Hallet Applied Calculus 5th Edition
- CLRS Introduction to Algorithms Third Edition

In addition to the textbooks, the dataset also includes material from the
following online resources:

- C reference
- Cpp reference
- Python Standard Library

These resources provide up-to-date information and examples for the C, C++, and
Python programming languages. The creators of the Cppreference site also provide
archives of their site
for offline use. Code samples synthesized by OpenAI's GPT models, curated by the
dataset creator, are also included in the dataset.

Note: The creator of this dataset owns physical copies of all the textbooks
listed above. The data from these sources are transformed into a dialogue format
using OpenAI's GPT-3.5 and GPT-4 models. The resulting dialogues are then used
as the training data for the phi-1 model. This dataset does not include the full
content of the source textbooks. Instead, it consists of transformations and
syntheses of the original content. Anyone who wants access to the full original
content should purchase or otherwise legally access the textbooks themselves.#### Who are the source language producers?

The original language data was created by a variety of authors and educators,
who wrote the textbooks and other materials used as sources for this dataset.
These include:

- Barron's Arithmetic The Easy Way Fourth Edition - Edward Williams, Katie
  Prindle
- Blitzer Introductory Algebra for College Students Fifth Edition - Robert
  Blitzer
- McDougal Littell Geometry - Ron Larson, Laurie Boswell, Timothy D. Kanold, Lee
  Stiff
- Blitzer Intermediate Algebra for College Students 5th Edition - Robert Blitzer
- Trigonometry Sixth Edition - Charles P. McKeague, Mark D. Turner
- Pearson College Algebra Fourth Edition - Robert F. Blitzer
- Hughes-Hallet Applied Calculus 5th Edition - Deborah Hughes-Hallett, Andrew M.
  Gleason, Patti Frazer Lock, Daniel E. Flath, Sheldon P. Gordon, David O.
  Lomen, David Lovelock, William G. McCallum, Brad G. Osgood, Andrew Pasquale,
  Jeff Tecosky-Feldman, Joseph Thrash, Karen R. Rhea, Thomas W. Tucker
- CLRS Introduction to Algorithms Third Edition - Thomas H. Cormen, Charles E.
  Leiserson, Ronald L. Rivest, Clifford Stein

In addition to these authors, the developers of OpenAI's GPT-3.5 and GPT-4
models also contributed to the creation of the language data, as these models
were used to transform the source material into a dialogue format.### Annotations#### Annotation process

The dataset does not contain any explicit annotations. However, the data is
curated and synthesized using OpenAI's GPT-3.5 and GPT-4 models. The process
involves transforming the source material into a dialogue format suitable for
training the phi-1 model. The dataset creator, an independent learner with a
strong interest in computer science, reviewed and curated the synthesized
dialogues to ensure their quality and relevance. passage: #### Who are the annotators?

The dataset creator, an independent learner who has studied computer science
extensively in a self-directed manner, performed the curation and review of the
synthesized dialogues.### Personal and Sensitive Information

The dataset does not contain any personal or sensitive information. All the data
is derived from publicly available textbooks and online resources. Any names or
other potential identifiers in the source material have been removed or
anonymized.### Social Impact of Dataset

The dataset is intended to support the development of AI models capable of
providing detailed explanations and examples in the context of arithmetic,
algebra, geometry, trigonometry, calculus, algorithms and data structures,
design patterns, and the python programming language. The potential social
impact is significant, as such models could greatly enhance self-directed
learning and provide valuable educational support to students worldwide.

However, it's important to note that the quality and usefulness of the AI models
trained on this dataset will depend on the quality of the data itself. If the
data is inaccurate or biased, the models could propagate these inaccuracies and
biases, potentially leading to misinformation or unfair outcomes.### Discussion of Biases

The dataset is based on a variety of textbooks and online resources, which may
contain their own inherent biases. For example, textbooks often reflect the
perspectives and biases of their authors, which can influence the way
information is presented. These biases could potentially be reflected in the
dataset and in any models trained on it.### Other Known Limitations

At this stage of the dataset creation process, it's difficult to identify all
potential limitations. However, one potential limitation is that the dataset may
not cover all possible topics or perspectives within the fields it addresses.
The dataset creator will continue to monitor and assess the dataset for
limitations as the work progresses.## Additional Information### Dataset Curators

The dataset was curated by an independent learner with a strong interest in
computer science. The curator has studied the subject matter in a self-directed
manner, using a variety of resources including textbooks and online materials.
The curation process also involved the use of OpenAI's GPT-3.5 and GPT-4 models
to synthesize dialogues based on the source material.