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hexsha string | repo string | path string | license sequence | language string | identifier string | original_docstring string | docstring string | docstring_tokens sequence | code string | code_tokens sequence | short_docstring string | short_docstring_tokens sequence | comment sequence | parameters list | docstring_params dict |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
bf86d18d90c390905405a84287a89ad2f0b7c1bb | iSoron/Prescient | prescient/gosm/derivative_patterns/graph_utilities.py | [
"BSD-3-Clause"
] | Python | WeightedGraph |
This class represents a weighted graph for the purposes
of determining clusters via the Markov Clustering Algorithm.
To initialize an object of this class, pass in a dictionary
which maps pairs (tuples) of vertices to the corresponding weight.
Stores internally both an adjacency list and an adjace... | This class represents a weighted graph for the purposes
of determining clusters via the Markov Clustering Algorithm.
To initialize an object of this class, pass in a dictionary
which maps pairs (tuples) of vertices to the corresponding weight.
Stores internally both an adjacency list and an adjacency matrix
This is fi... | [
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"""
This class represents a weighted graph for the purposes
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44d82532b0e3acb3453df191ceef6f20d3f8da3d | plusus/plus-tuto | tuto-file-handling/libtuto/zone.py | [
"CC-BY-3.0"
] | Python | Zone |
Zone with defined boundaries
| Zone with defined boundaries | [
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"""
Zone with defined boundaries
"""
def topLeft(self):
"""
:rtype: (int, int)
"""
raise NotImplementedError()
def bottomRight(self):
"""
:rtype: (int, int)
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raise NotImplementedError()
def center(self):
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44f4e6ceee8ef657f109e2b9814db4070531e985 | OthmanEmpire/project_xcape | xcape/common/object.py | [
"MIT"
] | Python | GameObject |
The base class for all other classes.
| The base class for all other classes. | [
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"""
The base class for all other classes.
"""
MENU_EVENT = pg.USEREVENT + 1
SCENE_EVENT = pg.USEREVENT + 2
CUTSCENE_EVENT = pg.USEREVENT + 3
CATEGORIES_MENU = [
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]
CA... | [
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"\"\"\"\n Rende... | [] | {
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} |
780191505bf40f3e172386d4238c7acea61b8ff1 | gustavo-bordin/scrapy | scrapy/core/http2/stream.py | [
"BSD-3-Clause"
] | Python | Stream | Represents a single HTTP/2 Stream.
Stream is a bidirectional flow of bytes within an established connection,
which may carry one or more messages. Handles the transfer of HTTP Headers
and Data frames.
Role of this class is to
1. Combine all the data frames
| Represents a single HTTP/2 Stream.
Stream is a bidirectional flow of bytes within an established connection,
which may carry one or more messages. Handles the transfer of HTTP Headers
and Data frames.
Role of this class is to
1. Combine all the data frames | [
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"""Represents a single HTTP/2 Stream.
Stream is a bidirectional flow of bytes within an established connection,
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Role of this class is to
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783035b4b9b3f5d8991fd6a577b630ff9a4e1914 | PickBas/meta-social | chat/forms.py | [
"MIT"
] | Python | Meta |
Meta class. Getting fields.
| Meta class. Getting fields. | [
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"""
Meta class. Getting fields.
"""
model = Chat
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783035b4b9b3f5d8991fd6a577b630ff9a4e1914 | PickBas/meta-social | chat/forms.py | [
"MIT"
] | Python | Meta |
Meta class. Getting fields.
| Meta class. Getting fields. | [
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"""
Meta class. Getting fields.
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model = MessageImages
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786957ff51f0aade241bd7bd3541659d809c48dd | pwo/irrd | irrd/scopefilter/validators.py | [
"BSD-2-Clause"
] | Python | ScopeFilterValidator |
The scope filter validator validates whether prefixes, ASNs or RPSL
objects fall within the configured scope filter.
| The scope filter validator validates whether prefixes, ASNs or RPSL
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"""
The scope filter validator validates whether prefixes, ASNs or RPSL
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"""
def __init__(self):
self.load_filters()
def load_filters(self):
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"\"\"\"\n Validate a prefix and/or A... | [] | {
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} |
789fcdd940662c0ac1e593a1b04be96bee4e4bce | Romansko/MessageU | server/database.py | [
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""" Represents a client entry """
def __init__(self, cid, cname, public_key, last_seen):
self.ID = bytes.fromhex(cid) # Unique client ID, 16 bytes.
self.Name = cname # Client's name, null terminated ascii string, 255 bytes.
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"returns": [],
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789fcdd940662c0ac1e593a1b04be96bee4e4bce | Romansko/MessageU | server/database.py | [
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""" Represents a message entry """
def __init__(self, to_client, from_client, mtype, content):
self.ID = 0 # Message ID, 4 bytes.
self.ToClient = to_client # Receiver's unique ID, 16 bytes.
self.FromClient = from_client # Sender's unique ID, 16 bytes.
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78af814f45a2e5db390cc508ffe44e7889cbd00d | ur1ove/rl_algorithms | algorithms/her.py | [
"MIT"
] | Python | HER | HER (final strategy).
Attributes:
desired_states (np.ndarray): desired states
reward_func (Callable): returns reward from state, action, next_state
| HER (final strategy). | [
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"""HER (final strategy).
Attributes:
desired_states (np.ndarray): desired states
reward_func (Callable): returns reward from state, action, next_state
"""
def __init__(self, demo_path: str, reward_func: Callable = default_reward_func):
"""Initialization.
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78b41cbc059a33010d5dfcf14fdde10feb7c0be0 | mwanakijiji/lbti_altair_fizeau | modules/host_removal.py | [
"MIT"
] | Python | HostRemoval |
PCA-decompose a saturated host star PSF and remove it
| PCA-decompose a saturated host star PSF and remove it | [
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'''
PCA-decompose a saturated host star PSF and remove it
'''
def __init__(self,
n_PCA,
outdir,
abs_PCA_name,
config_data = config):
'''
INPUTS:
n_PCA: number of principal components to use
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78b961a6673ec1e12f8d95c33ef081f75561a87c | AIS-Bonn/sl-cutscenes | sl_cutscenes/object_models.py | [
"MIT"
] | Python | MeshLoader |
Class to load the meshes for the objects in a scene.
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"""
Class to load the meshes for the objects in a scene.
"""
def __init__(self):
"""Module initializer"""
self.base_dir = CONSTANTS.MESH_BASE_DIR
self.text_dir = CONSTANTS.TEXT_BASE_DIR
self.reset()
def reset(self):
self.loaded_meshes = []
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78b961a6673ec1e12f8d95c33ef081f75561a87c | AIS-Bonn/sl-cutscenes | sl_cutscenes/object_models.py | [
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Class to load the objects in a scene
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"""
Class to load the objects in a scene
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def __init__(self):
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78b961a6673ec1e12f8d95c33ef081f75561a87c | AIS-Bonn/sl-cutscenes | sl_cutscenes/object_models.py | [
"MIT"
] | Python | DecoratorLoader |
Class to add random decorative objects to the scene, which do not participate of the scene dynamics.
It is based on creating an occupancy matrix of the scene, finding empty locations and placing stuff there
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"""
Class to add random decorative objects to the scene, which do not participate of the scene dynamics.
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def __init__(self, scene):
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"\"\"\"\n Class to add random decorative objects to the scene, which do not participate of the scene dynamics.\n It is based on creating an occupancy matrix of the scene, finding empty locations and placing stuff there\n \"\"\"",
"\"\"\" Object initializer \"\"\"",
"\"\"\" Loading an object and adding ... | [] | {
"returns": [],
"raises": [],
"params": [],
"outlier_params": [],
"others": []
} |
78c6d327eeab5125863f64f7fe72c2cd35fb66b0 | p-koskey/news-sources | app/models.py | [
"MIT"
] | Python | Source |
Source class to define source objects
| Source class to define source objects | [
"Source",
"class",
"to",
"define",
"source",
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'''
Source class to define source objects
'''
def __init__(self,id,name,category):
self.id = id
self.name = name
self.category = category | [
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78c6d327eeab5125863f64f7fe72c2cd35fb66b0 | p-koskey/news-sources | app/models.py | [
"MIT"
] | Python | Article |
Article class to define article objects
| Article class to define article objects | [
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'''
Article class to define article objects
'''
def __init__(self, name, author, title, description, link, image, publishDate):
self.name = name
self.author = author
self.title = title
self.description = description
self.link = link
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78c6d327eeab5125863f64f7fe72c2cd35fb66b0 | p-koskey/news-sources | app/models.py | [
"MIT"
] | Python | Top |
Top headlines class to define headlines objects
| Top headlines class to define headlines objects | [
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"headlines",
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'''
Top headlines class to define headlines objects
'''
def __init__(self, source, author, title, description, link, image):
self.source = source
self.author = author
self.title = title
self.description = description
self.link = link
self.ima... | [
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} |
78cef279b1537217c1a5435db8a4394af5e49d17 | lrivallain/openfaas-fn | veba-to-argo-fn/handler/function/handler.py | [
"MIT"
] | Python | ArgoWorflow | The ArgoWorflow provide a way to start an argo WF based on an existing template.
| The ArgoWorflow provide a way to start an argo WF based on an existing template. | [
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"""The ArgoWorflow provide a way to start an argo WF based on an existing template.
"""
def __init__(self):
"""Initialize the ArgoWorflow
"""
logger.info("Reading configuration files")
logger.info(f"Argo config file > {ARGO_CONFIG}")
try:
... | [
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15ab59507209a9b7264e3945a7277176ff94124e | sbernasek/flyeye | flyeye/dynamics/visualization.py | [
"MIT"
] | Python | TimeseriesPlot |
Object describes a 1D timeseries.
Attributes:
x (np.ndarray) - independent variable
y (np.ndarray) - dependent variable
ax (matplotlib.axes.AxesSubplot)
| Object describes a 1D timeseries.
Attributes.
x (np.ndarray) - independent variable
y (np.ndarray) - dependent variable
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"""
Object describes a 1D timeseries.
Attributes:
x (np.ndarray) - independent variable
y (np.ndarray) - dependent variable
ax (matplotlib.axes.AxesSubplot)
"""
def __init__(self, x, y, ax=None):
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Instantiate a 1D timeseries.
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} |
ec6a7aa96ee85f520989ee524c59400757ee3b89 | fourstix/Sparkfun_CircuitPython_QwiicJoystick | sparkfun_qwiicjoystick.py | [
"MIT"
] | Python | Sparkfun_QwiicJoystick | CircuitPython class for the Sparkfun QwiicJoystick
Usage:
# import the CircuitPython board and busio libraries
import board
import busio
# Create bus object using the board's I2C port
i2c = busio.I2C(board.SCL, board.SDA)
joystick = QwiicJoystick(i2c) # default address is 0x20
# us... | CircuitPython class for the Sparkfun QwiicJoystick
Usage.
import the CircuitPython board and busio libraries
import board
import busio
Create bus object using the board's I2C port
i2c = busio.I2C(board.SCL, board.SDA)
joystick = QwiicJoystick(i2c) # default address is 0x20
use QwiicJoystick(i2c, address) for a di... | [
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... | class Sparkfun_QwiicJoystick:
"""CircuitPython class for the Sparkfun QwiicJoystick
Usage:
# import the CircuitPython board and busio libraries
import board
import busio
# Create bus object using the board's I2C port
i2c = busio.I2C(board.SCL, board.SDA)
joystick = QwiicJoystick(i2c)... | [
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} |
ecaac8c49f868073f9f6b89fcb22c74fdb9c6a04 | benhid/d4s | d4s/storage.py | [
"MIT"
] | Python | Item |
Class representing store hub files.
| Class representing store hub files. | [
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] | class Item:
"""
Class representing store hub files.
"""
def __init__(self, id: str, base_url: str):
self.id = id
self.base_url = base_url
@cached_property
def public_url(self):
""" Get public url from item in workspace.
"""
url = f'{self.base_url}/worksp... | [
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"\"\"\"\n Class representing store hub files.\n \"\"\"",
"\"\"\" Get public url from item in workspace.\n \"\"\"",
"# for some reason, the response returns an url with surrounding quote marks"
] | [] | {
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} |
ecb3d6f73859938d78658fa282749298aab75d9d | darthryking/VMFMergeTool | gui.py | [
"BSD-3-Clause"
] | Python | VMFCache | An expandable-size cache for VMFs. This lets us skip the load process
for VMFs that we've already loaded before, which is helpful for VMFs that
take a long time to parse.
| An expandable-size cache for VMFs. This lets us skip the load process
for VMFs that we've already loaded before, which is helpful for VMFs that
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""" An expandable-size cache for VMFs. This lets us skip the load process
for VMFs that we've already loaded before, which is helpful for VMFs that
take a long time to parse.
"""
def __init__(self):
self.maxSize = 1
self.data = {}
self.unusedPaths = ... | [
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} |
ecc09f1629ed770a0be9fae5c249608721a3c6c2 | mithem/helix-cli | statehandler.py | [
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] | class StateHandler:
"""utilities commonly used when working with states"""
def getDateString(date):
"""returns iso-date-string of specified date"""
return str(f"{date.year}-{date.month}-{date.day}")
def getAppropriateState(title):
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ecd45ebb5de12537c1bdbc107787ed72f93490f1 | lnros/real-estate-web-scraping | config.py | [
"MIT"
] | Python | DBConfig |
Holds the DB parameters for the web scraping.
| Holds the DB parameters for the web scraping. | [
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"."
] | class DBConfig:
"""
Holds the DB parameters for the web scraping.
"""
HOST = "localhost"
USER = "root"
PASSWORD = "password" # not real password, change after pulling this file
DATABASE = "brbeky1hybvf32t4ufxz"
INSERT_CITY_QUERY = "INSERT IGNORE INTO cities(city_name) values (%s)"
I... | [
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} |
ecd45ebb5de12537c1bdbc107787ed72f93490f1 | lnros/real-estate-web-scraping | config.py | [
"MIT"
] | Python | Configuration |
Holds the user parameters for the web scraping.
| Holds the user parameters for the web scraping. | [
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"user",
"parameters",
"for",
"the",
"web",
"scraping",
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] | class Configuration:
"""
Holds the user parameters for the web scraping.
"""
# class attr
args = None
# PARAMETERS KWARGS KEYS
VERBOSE_KEY = 'verbose'
LIMIT_KEY = 'limit'
PRINT_KEY = 'to_print'
SAVE_KEY = 'save'
DB_KEY = 'to_database'
FETCH_KEY = 'fetch_info'
LISTIN... | [
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"\"\"\"\n Creates the command line argument... | [] | {
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"outlier_params": [],
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} |
Dataset Summary
The Vault dataset is a comprehensive, large-scale, multilingual parallel dataset that features high-quality code-text pairs derived from The Stack, the largest permissively-licensed source code dataset.
We provide The Vault which contains code snippets from 10 popular programming languages such as Java, JavaScript, Python, Ruby, Rust, Golang, C#, C++, C, and PHP. This dataset provides multiple code-snippet levels, metadata, and 11 docstring styles for enhanced usability and versatility.
Supported Tasks
The Vault can be used for pretraining LLMs or downstream code-text interaction tasks. A number of tasks related to code understanding and geneartion can be constructed using The Vault such as code summarization, text-to-code generation and code search.
Languages
The natural language text (docstring) is in English.
10 programming languages are supported in The Vault: Python, Java, JavaScript, PHP, C, C#, C++, Go, Ruby, Rust
Note: C and Go are not contained in this repo due to the nonexistence of traditional classes in these languages.
Dataset Structure
Data Instances
{
"hexsha": "78b961a6673ec1e12f8d95c33ef081f75561a87c",
"repo": "AIS-Bonn/sl-cutscenes",
"path": "sl_cutscenes/object_models.py",
"license": [
"MIT"
],
"language": "Python",
"identifier": "MeshLoader",
"original_docstring": "\n Class to load the meshes for the objects in a scene.\n ",
"docstring": "Class to load the meshes for the objects in a scene.",
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"code": "class MeshLoader:\n \"\"\"\n Class to load the meshes for the objects in a scene.\n \"\"\"\n\n def __init__(self):\n \"\"\"Module initializer\"\"\"\n self.base_dir = CONSTANTS.MESH_BASE_DIR\n self.text_dir = CONSTANTS.TEXT_BASE_DIR\n self.reset()\n\n def reset(self):\n self.loaded_meshes = []\n\n def get_meshes(self):\n \"\"\" \"\"\"\n extract_singular = lambda x: x[0] if len(x) == 1 else x\n return [extract_singular(item) for item in self.loaded_meshes]\n\n def load_meshes(self, obj_info: List[object_info.ObjectInfo], **kwargs):\n \"\"\"\n Loads the meshes whose information is given in parameter 'obj_info.\n Each call of this method APPENDS a list to the loaded_meshes attribute.\n :param obj_info: The object information of the meshes to be loaded.\n :param kwargs: additional mesh modifiers such as scale, specified with a leading 'mod_'\n \"\"\"\n paths = []\n for obj in obj_info:\n path = self.text_dir if obj.name.endswith(\"_floor\") or obj.name.endswith(\"_wall\") else self.base_dir\n paths.append((path / obj.mesh_fp).resolve())\n scales = [obj.scale for obj in obj_info]\n class_ids = [obj.class_id for obj in obj_info]\n mod_scales = kwargs.get(\"mod_scale\", [1.0] * len(scales))\n scales = [s * ms for (s, ms) in zip(scales, mod_scales)]\n flags = [mesh_flags(obj) for obj in obj_info]\n meshes = sl.Mesh.load_threaded(filenames=paths, flags=flags)\n\n # Setup class IDs\n for _, (mesh, scale, class_id) in enumerate(zip(meshes, scales, class_ids)):\n pt = torch.eye(4)\n pt[:3, :3] *= scale\n mesh.pretransform = pt\n mesh.class_index = class_id\n\n info_mesh_tuples = list(zip(obj_info, meshes))\n self.loaded_meshes.append(info_mesh_tuples)",
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"# Setup class IDs"
],
"parameters": [],
"docstring_params": {
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}
Data Fields
Data fields for function level:
- hexsha (string): the unique git hash of file
- repo (string): the owner/repo
- path (string): the full path to the original file
- license (list): licenses in the repo
- language (string): the programming language
- identifier (string): the function or method name
- original_string (string): original version of function/class node
- original_docstring (string): the raw string before tokenization or parsing
- code (string): the part of the original that is code
- code_tokens (list): tokenized version of
code - short_docstring (string): short, brief summarization (first line of the docstring)
- short_docstring_tokens (list): tokenized version of `short_docstring
- docstring (string): the top-level comment or docstring (docstring version without param’s doc, return, exception fields, etc)
- docstring_tokens (list): tokenized version of docstring
- comment (list): list of comments (line) inside the function/class
- parameters (list): List of parameters and its type (type can be None)
- docstring_params (dict): Dictionary of the parsed information from docstring
See here for more details and examples.
Data Splits
In this repo, the class level data is not split, and contained in only train set.
Dataset Statistics
| Language | Number of samples |
|---|---|
| Python | 422,187 |
| Java | 4,872,485 |
| JavaScript | 291,479 |
| PHP | 1,173,916 |
| C# | 1,437,800 |
| C++ | 174,370 |
| Ruby | 353,859 |
| Rust | 93,311 |
| C | - |
| Go | - |
| TOTAL | 9,121,300 |
Usage
You can load The Vault dataset using datasets library: pip install datasets
from datasets import load_dataset
# Load full class level dataset
dataset = load_dataset("Fsoft-AIC/the-vault-class")
# specific language (e.g. Python)
dataset = load_dataset("Fsoft-AIC/the-vault-class", languages=['Python'])
# dataset streaming
data = load_dataset("Fsoft-AIC/the-vault-class", streaming= True)
for sample in iter(data['train']):
print(sample)
A back up dataset can be downloaded in azure storage. See Download The Vault from Azure blob storage.
Additional information
Licensing Information
MIT License
Citation Information
@article{manh2023vault,
title={The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation},
author={Manh, Dung Nguyen and Hai, Nam Le and Dau, Anh TV and Nguyen, Anh Minh and Nghiem, Khanh and Guo, Jin and Bui, Nghi DQ},
journal={arXiv preprint arXiv:2305.06156},
year={2023}
}
Contributions
This dataset is developed by FSOFT AI4Code team.
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