# Dataset Card for "code_x_glue_cc_code_completion_token"

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits-sample-size)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/CodeCompletion-token

### Dataset Summary

CodeXGLUE CodeCompletion-token dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/CodeCompletion-token

Predict next code token given context of previous tokens. Models are evaluated by token level accuracy.
    Code completion is a one of the most widely used features in software development through IDEs. An effective code completion tool could improve software developers' productivity. We provide code completion evaluation tasks in two granularities -- token level and line level. Here we introduce token level code completion. Token level task is analogous to language modeling. Models should have be able to predict the next token in arbitary types.

### Supported Tasks and Leaderboards

- `language-modeling`: The dataset can be used to train a model for completing single code tokens.

### Languages

- Java **programming** language
- Python **programming** language

## Dataset Structure

### Data Instances

#### java

An example of 'test' looks as follows.
```
{
    "code": ["<s>", "package", "org", ".", "vaadin", ".", "teemu", ".", "clara", ".", "demo", ";", "import", "java", ".", "io", ".", "BufferedReader", ";", "import", "java", ".", "io", ".", "ByteArrayInputStream", ";", "import", "java", ".", "io", ".", "IOException", ";", "import", "java", ".", "io", ".", "InputStreamReader", ";", "import", "org", ".", "vaadin", ".", "teemu", ".", "clara", ".", "Clara", ";", "import", "org", ".", "vaadin", ".", "teemu", ".", "clara", ".", "inflater", ".", "LayoutInflaterException", ";", "import", "com", ".", "vaadin", ".", "Application", ";", "import", "com", ".", "vaadin", ".", "terminal", ".", "ThemeResource", ";", "import", "com", ".", "vaadin", ".", "ui", ".", "Button", ";", "import", "com", ".", "vaadin", ".", "ui", ".", "Button", ".", "ClickEvent", ";", "import", "com", ".", "vaadin", ".", "ui", ".", "Component", ";", "import", "com", ".", "vaadin", ".", "ui", ".", "Embedded", ";", "import", "com", ".", "vaadin", ".", "ui", ".", "HorizontalLayout", ";", "import", "com", ".", "vaadin", ".", "ui", ".", "HorizontalSplitPanel", ";", "import", "com", ".", "vaadin", ".", "ui", ".", "TextArea", ";", "import", "com", ".", "vaadin", ".", "ui", ".", "VerticalLayout", ";", "import", "com", ".", "vaadin", ".", "ui", ".", "Window", ";", "import", "com", ".", "vaadin", ".", "ui", ".", "Window", ".", "Notification", ";", "@", "SuppressWarnings", "(", "\"serial\"", ")", "public", "class", "DemoApplication", "extends", "Application", "{", "private", "DemoController", "controller", ";", "private", "TextArea", "xmlArea", ";", "private", "HorizontalSplitPanel", "split", "=", "new", "HorizontalSplitPanel", "(", ")", ";", "private", "Window", "mainWindow", ";", "@", "Override", "public", "void", "init", "(", ")", "{", "setTheme", "(", "\"clara\"", ")", ";", "setMainWindow", "(", "mainWindow", "=", "new", "Window", "(", ")", ")", ";", "controller", "=", "new", "DemoController", "(", "mainWindow", ")", ";", "mainWindow", ".", "setContent", "(", "split", ")", ";", "VerticalLayout", "editor", "=", "new", "VerticalLayout", "(", ")", ";", "editor", ".", "setSpacing", "(", "true", ")", ";", "editor", ".", "setMargin", "(", "false", ",", "false", ",", "false", ",", "true", ")", ";", "editor", ".", "setHeight", "(", "\"100%\"", ")", ";", "editor", ".", "addComponent", "(", "xmlArea", "=", "createXmlArea", "(", ")", ")", ";", "editor", ".", "setExpandRatio", "(", "xmlArea", ",", "1.0f", ")", ";", "editor", ".", "addComponent", "(", "createUpdateButton", "(", ")", ")", ";", "HorizontalLayout", "wrapper", "=", "new", "HorizontalLayout", "(", ")", ";", "wrapper", ".", "setMargin", "(", "true", ")", ";", "wrapper", ".", "setSizeFull", "(", ")", ";", "wrapper", ".", "addComponent", "(", "createLogo", "(", ")", ")", ";", "wrapper", ".", "addComponent", "(", "editor", ")", ";", "wrapper", ".", "setExpandRatio", "(", "editor", ",", "1.0f", ")", ";", "split", ".", "setFirstComponent", "(", "wrapper", ")", ";", "updateLayout", "(", ")", ";", "}", "private", "Component", "createLogo", "(", ")", "{", "Embedded", "logo", "=", "new", "Embedded", "(", "null", ",", "new", "ThemeResource", "(", "\"\"", ")", ")", ";", "logo", ".", "setHeight", "(", "\"90px\"", ")", ";", "logo", ".", "setWidth", "(", "\"90px\"", ")", ";", "return", "logo", ";", "}", "private", "TextArea", "createXmlArea", "(", ")", "{", "TextArea", "area", "=", "new", "TextArea", "(", ")", ";", "area", ".", "setStyleName", "(", "\"xml-area\"", ")", ";", "area", ".", "setSizeFull", "(", ")", ";", "area", ".", "setValue", "(", "readStartingPoint", "(", ")", ")", ";", "return", "area", ";", "}", "private", "Button", "createUpdateButton", "(", ")", "{", "return", "new", "Button", "(", "\"Update\"", ",", "new", "Button", ".", "ClickListener", "(", ")", "{", "public", "void", "buttonClick", "(", "ClickEvent", "event", ")", "{", "updateLayout", "(", ")", ";", "}", "}", ")", ";", "}", "private", "String", "readStartingPoint", "(", ")", "{", "BufferedReader", "reader", "=", "null", ";", "try", "{", "reader", "=", "new", "BufferedReader", "(", "new", "InputStreamReader", "(", "getClass", "(", ")", ".", "getClassLoader", "(", ")", ".", "getResourceAsStream", "(", "\"\"", ")", ")", ")", ";", "StringBuilder", "xml", "=", "new", "StringBuilder", "(", ")", ";", "String", "line", ";", "while", "(", "(", "line", "=", "reader", ".", "readLine", "(", ")", ")", "!=", "null", ")", "{", "xml", ".", "append", "(", "line", ")", ";", "xml", ".", "append", "(", "\"n\"", ")", ";", "}", "return", "xml", ".", "toString", "(", ")", ";", "}", "catch", "(", "IOException", "e", ")", "{", "e", ".", "printStackTrace", "(", ")", ";", "}", "finally", "{", "if", "(", "reader", "!=", "null", ")", "{", "try", "{", "reader", ".", "close", "(", ")", ";", "}", "catch", "(", "IOException", "e", ")", "{", "e", ".", "printStackTrace", "(", ")", ";", "}", "}", "}", "return", "null", ";", "}", "private", "void", "updateLayout", "(", ")", "{", "try", "{", "Component", "c", "=", "Clara", ".", "create", "(", "new", "ByteArrayInputStream", "(", "xmlArea", ".", "getValue", "(", ")", ".", "toString", "(", ")", ".", "getBytes", "(", ")", ")", ",", "controller", ")", ";", "split", ".", "replaceComponent", "(", "split", ".", "getSecondComponent", "(", ")", ",", "c", ")", ";", "}", "catch", "(", "LayoutInflaterException", "e", ")", "{", "mainWindow", ".", "showNotification", "(", "e", ".", "getMessage", "(", ")", ",", "Notification", ".", "TYPE_ERROR_MESSAGE", ")", ";", "}", "}", "}", "</s>"], 
    "id": 0
}
```

#### python

An example of 'train' looks as follows.
```
{
    "code": ["<s>", "from", "bootstrap", "import", "Bootstrap", "<EOL>", "from", "fund", "import", "InstantPaymentNotificationHandler", "<EOL>", "from", "fund", "import", "ThankYouHandler", "<EOL>", "from", "view", "import", "*", "<EOL>", "mapping", "=", "[", "(", "<EOL>", "r'/'", ",", "<EOL>", "Index", "<EOL>", ")", ",", "(", "<EOL>", "r'/ipn'", ",", "<EOL>", "InstantPaymentNotificationHandler", "<EOL>", ")", ",", "(", "<EOL>", "r'/thank-you'", ",", "<EOL>", "ThankYouHandler", "<EOL>", ")", ",", "(", "<EOL>", "r'/about\\/?'", ",", "<EOL>", "About", "<EOL>", ")", ",", "(", "<EOL>", "r'/guide\\/?'", ",", "<EOL>", "Guide", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Download", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Standards", "<EOL>", ")", ",", "(", "<EOL>", "r'/community\\/?'", ",", "<EOL>", "Community", "<EOL>", ")", ",", "(", "<EOL>", "r'/news\\/?'", ",", "<EOL>", "News", "<EOL>", ")", ",", "(", "<EOL>", "r'/support\\/?'", ",", "<EOL>", "Support", "<EOL>", ")", ",", "(", "<EOL>", "r'/contact\\/?'", ",", "<EOL>", "Contact", "<EOL>", ")", ",", "(", "<EOL>", "r'/press\\/?'", ",", "<EOL>", "Press", "<EOL>", ")", ",", "(", "<EOL>", "r'/legal/terms'", ",", "<EOL>", "Terms", "<EOL>", ")", ",", "(", "<EOL>", "r'/library\\/?'", ",", "<EOL>", "Library", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Library", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Library", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Users", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "User", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Design", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Design", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Design", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Design", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Design", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "RedirectSuccess", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "RedirectError", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "RedirectAfterDelete", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Moderate", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Bootstrap", "<EOL>", ")", ",", "(", "<EOL>", "r'/activity'", ",", "<EOL>", "ActivityScreen", "<EOL>", ")", ",", "(", "<EOL>", "r'/txns'", ",", "<EOL>", "TxnList", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Base64Blob", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Base64Blob", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "MessageStrings", "<EOL>", ")", ",", "(", "<EOL>", "r'/.*'", ",", "<EOL>", "NotFound", "<EOL>", ")", "<EOL>", "]", "</s>"], 
    "id": 0, 
    "path": "00/wikihouse/urls.py\n"
}
```

### Data Fields

In the following each data field in go is explained for each config. The data fields are the same among all splits.

#### java

|field name|      type      |    description     |
|----------|----------------|--------------------|
|id        |int32           | Index of the sample|
|code      |Sequence[string]| Code Tokens        |

#### python

|field name|      type      |         description         |
|----------|----------------|-----------------------------|
|id        |int32           | Index of the sample         |
|path      |string          | Original path in the dataset|
|code      |Sequence[string]| Code Tokens                 |

### Data Splits

#### java

|    |train|validation|test|
|----|----:|---------:|---:|
|java|12934|      7189|8268|

#### python

|      |train |test |
|------|-----:|----:|
|python|100000|50000|

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

https://github.com/microsoft, https://github.com/madlag

### Licensing Information

Computational Use of Data Agreement (C-UDA) License.

### Citation Information

```
@article{raychev2016probabilistic,
      title={Probabilistic Model for Code with Decision Trees},
      author={Raychev, Veselin and Bielik, Pavol and Vechev, Martin},
      journal={ACM SIGPLAN Notices},
      pages={731--747},
      year={2016},
      publisher={ACM New York, NY, USA}
    }
    @inproceedings{allamanis2013mining,
      title={Mining Source Code Repositories at Massive Scale using Language Modeling},
      author={Allamanis, Miltiadis and Sutton, Charles},
      booktitle={2013 10th Working Conference on Mining Software Repositories (MSR)},
      pages={207--216},
      year={2013},
      organization={IEEE}
    }
```

The data for "java" configuration comes from:
```
@dataset{rafael_michael_karampatsis_2020_3628665,
  author       = {Rafael - Michael Karampatsis and
                  Hlib Babii and
                  Romain Robbes and
                  Charles Sutton and
                  Andrea Janes},
  title        = {Preprocessed Java Code Corpus},
  month        = jan,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {1.0},
  doi          = {10.5281/zenodo.3628665},
  url          = {https://doi.org/10.5281/zenodo.3628665}
}
```

### Contributions

Thanks to @madlag (and partly also @ncoop57) for adding this dataset.
task_categories:text-generation task_categories:fill-mask task_ids:language-modeling task_ids:masked-language-modeling annotations_creators:found language_creators:found multilinguality:monolingual size_categories:10K<n<100K source_datasets:original language:code license:c-uda region:us
{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["code"], "license": ["c-uda"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "pretty_name": "CodeXGlueCcCodeCompletionToken", "dataset_info": [{"config_name": "java", "features": [{"name": "id", "dtype": "int32"}, {"name": "code", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 128312045, "num_examples": 12934}, {"name": "validation", "num_bytes": 30259166, "num_examples": 7189}, {"name": "test", "num_bytes": 43027948, "num_examples": 8268}], "download_size": 31320339, "dataset_size": 201599159}, {"config_name": "python", "features": [{"name": "id", "dtype": "int32"}, {"name": "path", "dtype": "string"}, {"name": "code", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 684319455, "num_examples": 100000}, {"name": "test", "num_bytes": 333978028, "num_examples": 50000}], "download_size": 210143525, "dataset_size": 1018297483}], "configs": [{"config_name": "java", "data_files": [{"split": "train", "path": "java/train-*"}, {"split": "validation", "path": "java/validation-*"}, {"split": "test", "path": "java/test-*"}]}, {"config_name": "python", "data_files": [{"split": "train", "path": "python/train-*"}, {"split": "test", "path": "python/test-*"}]}]}
TAGS
#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-code #license-c-uda #region-us 

Dataset Card for "code\_x\_glue\_cc\_code\_completion\_token"
=============================================================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information
	+ Contributions


Dataset Description
-------------------


* Homepage: URL


### Dataset Summary


CodeXGLUE CodeCompletion-token dataset, available at URL


Predict next code token given context of previous tokens. Models are evaluated by token level accuracy.
Code completion is a one of the most widely used features in software development through IDEs. An effective code completion tool could improve software developers' productivity. We provide code completion evaluation tasks in two granularities -- token level and line level. Here we introduce token level code completion. Token level task is analogous to language modeling. Models should have be able to predict the next token in arbitary types.


### Supported Tasks and Leaderboards


* 'language-modeling': The dataset can be used to train a model for completing single code tokens.


### Languages


* Java programming language
* Python programming language


Dataset Structure
-----------------


### Data Instances


#### java


An example of 'test' looks as follows.


#### python


An example of 'train' looks as follows.


### Data Fields


In the following each data field in go is explained for each config. The data fields are the same among all splits.


#### java


field name: id, type: int32, description: Index of the sample
field name: code, type: Sequence[string], description: Code Tokens


#### python


field name: id, type: int32, description: Index of the sample
field name: path, type: string, description: Original path in the dataset
field name: code, type: Sequence[string], description: Code Tokens


### Data Splits


#### java



#### python



Dataset Creation
----------------


### Curation Rationale


### Source Data


#### Initial Data Collection and Normalization


#### Who are the source language producers?


### Annotations


#### Annotation process


#### Who are the annotators?


### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


### Discussion of Biases


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


URL URL


### Licensing Information


Computational Use of Data Agreement (C-UDA) License.


The data for "java" configuration comes from:


### Contributions


Thanks to @madlag (and partly also @ncoop57) for adding this dataset.



### Dataset Summary


CodeXGLUE CodeCompletion-token dataset, available at URL


Predict next code token given context of previous tokens. Models are evaluated by token level accuracy.
Code completion is a one of the most widely used features in software development through IDEs. An effective code completion tool could improve software developers' productivity. We provide code completion evaluation tasks in two granularities -- token level and line level. Here we introduce token level code completion. Token level task is analogous to language modeling. Models should have be able to predict the next token in arbitary types. ### Supported Tasks and Leaderboards


* 'language-modeling': The dataset can be used to train a model for completing single code tokens. ### Languages


* Java programming language
* Python programming language


Dataset Structure
----------------- ### Data Instances #### java


An example of 'test' looks as follows. #### python


An example of 'train' looks as follows. ### Data Fields


In the following each data field in go is explained for each config. The data fields are the same among all splits. #### java


field name: id, type: int32, description: Index of the sample
field name: code, type: Sequence[string], description: Code Tokens #### python


field name: id, type: int32, description: Index of the sample
field name: path, type: string, description: Original path in the dataset
field name: code, type: Sequence[string], description: Code Tokens ### Data Splits #### java #### python



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators


URL URL ### Licensing Information


Computational Use of Data Agreement (C-UDA) License.


The data for "java" configuration comes from: ### Contributions


Thanks to @madlag (and partly also @ncoop57) for adding this dataset.
TAGS
#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-code #license-c-uda #region-us 
 ### Dataset Summary


CodeXGLUE CodeCompletion-token dataset, available at URL


Predict next code token given context of previous tokens. Models are evaluated by token level accuracy.
Code completion is a one of the most widely used features in software development through IDEs. An effective code completion tool could improve software developers' productivity. We provide code completion evaluation tasks in two granularities -- token level and line level. Here we introduce token level code completion. Token level task is analogous to language modeling. Models should have be able to predict the next token in arbitary types. ### Supported Tasks and Leaderboards


* 'language-modeling': The dataset can be used to train a model for completing single code tokens. ### Languages


* Java programming language
* Python programming language


Dataset Structure
----------------- ### Data Instances #### java


An example of 'test' looks as follows. #### python


An example of 'train' looks as follows. ### Data Fields


In the following each data field in go is explained for each config. The data fields are the same among all splits. #### java


field name: id, type: int32, description: Index of the sample
field name: code, type: Sequence[string], description: Code Tokens #### python


field name: id, type: int32, description: Index of the sample
field name: path, type: string, description: Original path in the dataset
field name: code, type: Sequence[string], description: Code Tokens ### Data Splits #### java #### python



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators


URL URL ### Licensing Information


Computational Use of Data Agreement (C-UDA) License.


The data for "java" configuration comes from: ### Contributions


Thanks to @madlag (and partly also @ncoop57) for adding this dataset.
passage: TAGS
#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-code #license-c-uda #region-us 
### Dataset Summary


CodeXGLUE CodeCompletion-token dataset, available at URL


Predict next code token given context of previous tokens. Models are evaluated by token level accuracy.
Code completion is a one of the most widely used features in software development through IDEs. An effective code completion tool could improve software developers' productivity. We provide code completion evaluation tasks in two granularities -- token level and line level. Here we introduce token level code completion. Token level task is analogous to language modeling. Models should have be able to predict the next token in arbitary types.### Supported Tasks and Leaderboards


* 'language-modeling': The dataset can be used to train a model for completing single code tokens.### Languages


* Java programming language
* Python programming language


Dataset Structure
-----------------### Data Instances#### java


An example of 'test' looks as follows.#### python


An example of 'train' looks as follows.### Data Fields


In the following each data field in go is explained for each config. The data fields are the same among all splits.#### java


field name: id, type: int32, description: Index of the sample
field name: code, type: Sequence[string], description: Code Tokens#### python


field name: id, type: int32, description: Index of the sample
field name: path, type: string, description: Original path in the dataset
field name: code, type: Sequence[string], description: Code Tokens### Data Splits#### java#### python



Dataset Creation
----------------### Curation Rationale