# Dataset Card for "ArtifactAI/arxiv_python_research_code"

## Dataset Description

https://huggingface.co/datasets/ArtifactAI/arxiv_deep_learning_python_research_code


### Dataset Summary

ArtifactAI/arxiv_deep_learning_python_research_code contains over 1.49B of  source code files referenced strictly in ArXiv papers. The dataset serves as a curated dataset for Code LLMs.

### How to use it
```python
from datasets import load_dataset

# full dataset (1.49GB of data)
ds = load_dataset("ArtifactAI/arxiv_deep_learning_python_research_code", split="train")

# dataset streaming (will only download the data as needed)
ds = load_dataset("ArtifactAI/arxiv_deep_learning_python_research_code", streaming=True, split="train")
for sample in iter(ds): print(sample["code"])
```

## Dataset Structure
### Data Instances
Each data instance corresponds to one file. The content of the file is in the `code` feature, and other features (`repo`, `file`, etc.) provide some metadata.
### Data Fields
- `repo` (string): code repository name.
- `file` (string): file path in the repository.
- `code` (string): code within the file.
- `file_length`: (integer): number of characters in the file.
- `avg_line_length`: (float): the average line-length of the file.
- `max_line_length`: (integer): the maximum line-length of the file.
- `extension_type`: (string): file extension.

### Data Splits

The dataset has no splits and all data is loaded as train split by default.

## Dataset Creation

### Source Data
#### Initial Data Collection and Normalization
34,099 active GitHub repository names were extracted from [ArXiv](https://arxiv.org/) papers from its inception through July 21st, 2023 totaling 773G of compressed github repositories.

These repositories were then filtered, and the code from each file that mentions ["torch", "jax", "flax", "stax", "haiku", "keras", "fastai", "xgboost", "caffe", "mxnet"] was extracted into 1.4 million files.

#### Who are the source language producers?

The source (code) language producers are users of GitHub that created unique repository

### Personal and Sensitive Information
The released dataset may contain sensitive information such as emails, IP addresses, and API/ssh keys that have previously been published to public repositories on GitHub. 

## Additional Information

### Dataset Curators
Matthew Kenney, Artifact AI, matt@artifactai.com

### Citation Information
```
@misc{arxiv_deep_learning_python_research_code,
    title={arxiv_deep_learning_python_research_code},
    author={Matthew Kenney},
    year={2023}
}
```
region:us
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "repo", "dtype": "string"}, {"name": "file", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "file_length", "dtype": "int64"}, {"name": "avg_line_length", "dtype": "float64"}, {"name": "max_line_length", "dtype": "int64"}, {"name": "extension_type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3590067176.125193, "num_examples": 391496}], "download_size": 1490724325, "dataset_size": 3590067176.125193}}
TAGS
#region-us 

# Dataset Card for "ArtifactAI/arxiv_python_research_code"

## Dataset Description

URL


### Dataset Summary

ArtifactAI/arxiv_deep_learning_python_research_code contains over 1.49B of  source code files referenced strictly in ArXiv papers. The dataset serves as a curated dataset for Code LLMs.

### How to use it


## Dataset Structure
### Data Instances
Each data instance corresponds to one file. The content of the file is in the 'code' feature, and other features ('repo', 'file', etc.) provide some metadata.
### Data Fields
- 'repo' (string): code repository name.
- 'file' (string): file path in the repository.
- 'code' (string): code within the file.
- 'file_length': (integer): number of characters in the file.
- 'avg_line_length': (float): the average line-length of the file.
- 'max_line_length': (integer): the maximum line-length of the file.
- 'extension_type': (string): file extension.

### Data Splits

The dataset has no splits and all data is loaded as train split by default.

## Dataset Creation

### Source Data
#### Initial Data Collection and Normalization
34,099 active GitHub repository names were extracted from ArXiv papers from its inception through July 21st, 2023 totaling 773G of compressed github repositories.

These repositories were then filtered, and the code from each file that mentions ["torch", "jax", "flax", "stax", "haiku", "keras", "fastai", "xgboost", "caffe", "mxnet"] was extracted into 1.4 million files.

#### Who are the source language producers?

The source (code) language producers are users of GitHub that created unique repository

### Personal and Sensitive Information
The released dataset may contain sensitive information such as emails, IP addresses, and API/ssh keys that have previously been published to public repositories on GitHub. 

## Additional Information

### Dataset Curators
Matthew Kenney, Artifact AI, matt@URL



# Dataset Card for "ArtifactAI/arxiv_python_research_code" ## Dataset Description

URL ### Dataset Summary

ArtifactAI/arxiv_deep_learning_python_research_code contains over 1.49B of  source code files referenced strictly in ArXiv papers. The dataset serves as a curated dataset for Code LLMs. ### How to use it ## Dataset Structure ### Data Instances
Each data instance corresponds to one file. The content of the file is in the 'code' feature, and other features ('repo', 'file', etc.) provide some metadata. ### Data Fields
- 'repo' (string): code repository name.
- 'file' (string): file path in the repository.
- 'code' (string): code within the file.
- 'file_length': (integer): number of characters in the file.
- 'avg_line_length': (float): the average line-length of the file.
- 'max_line_length': (integer): the maximum line-length of the file.
- 'extension_type': (string): file extension. ### Data Splits

The dataset has no splits and all data is loaded as train split by default. ## Dataset Creation ### Source Data #### Initial Data Collection and Normalization
34,099 active GitHub repository names were extracted from ArXiv papers from its inception through July 21st, 2023 totaling 773G of compressed github repositories.

These repositories were then filtered, and the code from each file that mentions ["torch", "jax", "flax", "stax", "haiku", "keras", "fastai", "xgboost", "caffe", "mxnet"] was extracted into 1.4 million files. #### Who are the source language producers?

The source (code) language producers are users of GitHub that created unique repository ### Personal and Sensitive Information
The released dataset may contain sensitive information such as emails, IP addresses, and API/ssh keys that have previously been published to public repositories on GitHub. ## Additional Information ### Dataset Curators
Matthew Kenney, Artifact AI, matt@URL
TAGS
#region-us 
 # Dataset Card for "ArtifactAI/arxiv_python_research_code" ## Dataset Description

URL ### Dataset Summary

ArtifactAI/arxiv_deep_learning_python_research_code contains over 1.49B of  source code files referenced strictly in ArXiv papers. The dataset serves as a curated dataset for Code LLMs. ### How to use it ## Dataset Structure ### Data Instances
Each data instance corresponds to one file. The content of the file is in the 'code' feature, and other features ('repo', 'file', etc.) provide some metadata. ### Data Fields
- 'repo' (string): code repository name.
- 'file' (string): file path in the repository.
- 'code' (string): code within the file.
- 'file_length': (integer): number of characters in the file.
- 'avg_line_length': (float): the average line-length of the file.
- 'max_line_length': (integer): the maximum line-length of the file.
- 'extension_type': (string): file extension. ### Data Splits

The dataset has no splits and all data is loaded as train split by default. ## Dataset Creation ### Source Data #### Initial Data Collection and Normalization
34,099 active GitHub repository names were extracted from ArXiv papers from its inception through July 21st, 2023 totaling 773G of compressed github repositories.

These repositories were then filtered, and the code from each file that mentions ["torch", "jax", "flax", "stax", "haiku", "keras", "fastai", "xgboost", "caffe", "mxnet"] was extracted into 1.4 million files. #### Who are the source language producers?

The source (code) language producers are users of GitHub that created unique repository ### Personal and Sensitive Information
The released dataset may contain sensitive information such as emails, IP addresses, and API/ssh keys that have previously been published to public repositories on GitHub. ## Additional Information ### Dataset Curators
Matthew Kenney, Artifact AI, matt@URL
passage: TAGS
#region-us 
# Dataset Card for "ArtifactAI/arxiv_python_research_code"## Dataset Description

URL### Dataset Summary

ArtifactAI/arxiv_deep_learning_python_research_code contains over 1.49B of  source code files referenced strictly in ArXiv papers. The dataset serves as a curated dataset for Code LLMs.### How to use it## Dataset Structure### Data Instances
Each data instance corresponds to one file. The content of the file is in the 'code' feature, and other features ('repo', 'file', etc.) provide some metadata.### Data Fields
- 'repo' (string): code repository name.
- 'file' (string): file path in the repository.
- 'code' (string): code within the file.
- 'file_length': (integer): number of characters in the file.
- 'avg_line_length': (float): the average line-length of the file.
- 'max_line_length': (integer): the maximum line-length of the file.
- 'extension_type': (string): file extension.### Data Splits

The dataset has no splits and all data is loaded as train split by default.## Dataset Creation### Source Data#### Initial Data Collection and Normalization
34,099 active GitHub repository names were extracted from ArXiv papers from its inception through July 21st, 2023 totaling 773G of compressed github repositories.

These repositories were then filtered, and the code from each file that mentions ["torch", "jax", "flax", "stax", "haiku", "keras", "fastai", "xgboost", "caffe", "mxnet"] was extracted into 1.4 million files.#### Who are the source language producers?

The source (code) language producers are users of GitHub that created unique repository