GPT-X2
Collection
Collection of all things GPT-X2 • 3 items • Updated • 1
code stringlengths 10 2.58M | original_code stringlengths 3 3.18M | original_language stringclasses 1
value | source stringclasses 7
values |
|---|---|---|---|
function record_vote self obj user vote
begin
set ctype = call get_for_model obj
try
begin
set v = get self user=user content_type=ctype object_id=call _get_pk_val
if vote == false
begin
delete
end
end
except ObjectDoesNotExist
begin
if vote == false
begin
return
end
call create user=user content_type=ctype object_id=c... | def record_vote(self, obj, user, vote):
ctype = ContentType.objects.get_for_model(obj)
try:
v = self.get(user=user, content_type=ctype, object_id=obj._get_pk_val())
if vote == False:
v.delete()
except models.ObjectDoesNotExist:
if vote == False:
return
self.create(user=user, content_type=ctype... | Python | nomic_cornstack_python_v1 |
comment %%
import math
from math import sqrt
import warnings
import sympy as smp
import sys
from itertools import count
import random
from subprocess import check_call
import os
from PIL import Image
from random import randint
import unittest
append path directory name path absolute path path __file__ + sep + string ..... | #%%
import math
from math import sqrt
import warnings
import sympy as smp
import sys
from itertools import count
import random
from subprocess import check_call
import os
from PIL import Image
from random import randint
import unittest
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + (os.path.sep + "..") * ... | Python | zaydzuhri_stack_edu_python |
import numpy as np
import matplotlib.pyplot as plt
import keras as k
function plot_functions x y func1
begin
set model1 = call load_model func1
set predict1 = predict model1 x
plot x predict1 label=string model
plot x y label=string target
legend
show
end function
function plot_loss model1
begin
set history1 = load np ... | import numpy as np
import matplotlib.pyplot as plt
import keras as k
def plot_functions(x,y,func1):
model1 = k.models.load_model(func1)
predict1 = model1.predict(x)
plt.plot(x, predict1, label = "model")
plt.plot(x, y,label = "target")
plt.legend()
plt.show()
def plot_loss(model1):
history1... | Python | zaydzuhri_stack_edu_python |
comment !/usr/bin/env python
from time import time
from math import sqrt
function is_natural_number n
begin
return n % 1.0 == 0 and n > 0
end function
function solve_quadratic_equation a b c
begin
set root1 = - 1.0 * b + square root b ^ 2 - 4.0 * a * c / 2.0 * a
set root2 = - 1.0 * b - square root b ^ 2 - 4.0 * a * c /... | #!/usr/bin/env python
from time import time
from math import sqrt
def is_natural_number(n):
return n % 1.0 == 0 and n > 0
def solve_quadratic_equation(a, b, c):
root1 = (-1.0 * b + sqrt(b**2 - 4.0 * a * c)) / (2.0 * a)
root2 = (-1.0 * b - sqrt(b**2 - 4.0 * a * c)) / (2.0 * a)
return root1, root2
de... | Python | zaydzuhri_stack_edu_python |
comment coding: utf-8
import os , time
import numpy as np
import librosa
import soundfile as sf
import model
comment ===========================================
comment 初始化模型
comment ===========================================
class Mymodel extends object
begin
string docstring for Mymodel
function __init__ self networ... | # coding: utf-8
import os, time
import numpy as np
import librosa
import soundfile as sf
import model
# ===========================================
# 初始化模型
# ===========================================
class Mymodel(object):
"""docstring for Mymodel"""
def __init__(self, network):
super(Mym... | Python | zaydzuhri_stack_edu_python |
function find_workers self worker_name=none provider_type=none
begin
with table_access_condition
begin
set conn = call _get_connection
set c = call cursor
execute c string SELECT * from workers WHERE (?1 IS NULL OR worker_name = ?1) AND (?2 IS NULL OR provider_type = ?2) tuple worker_name provider_type
set rows = call ... | def find_workers(
self, worker_name: Optional[str] = None, provider_type: Optional[str] = None
) -> List[Worker]:
with self.table_access_condition:
conn = self._get_connection()
c = conn.cursor()
c.execute(
"""
SELECT * from workers... | Python | nomic_cornstack_python_v1 |
function arguments self
begin
set barsplits = call _not_in_subspans_split_spans string | at slice 1 : :
set arguments = list
set spans = _spans
set lststr = _lststr
set typeindex = string ta + string _index
if typeindex not in spans
begin
set spans at typeindex = list
end
set aspans = spans at typeindex
if barsplit... | def arguments(self):
barsplits = self._not_in_subspans_split_spans('|')[1:]
arguments = []
spans = self._spans
lststr = self._lststr
typeindex = 'ta' + str(self._index)
if typeindex not in spans:
spans[typeindex] = []
aspans = spans[typeindex]
... | Python | nomic_cornstack_python_v1 |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import metrics , preprocessing
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import confusion_matrix
function plot_confusion_matrix cm target_names title=string Confusion matrix cmap=none normalize=true
begin
s... | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import metrics,preprocessing
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import confusion_matrix
def plot_confusion_matrix(cm,
target_names,
title='Confus... | Python | zaydzuhri_stack_edu_python |
function request self button
begin
put button block=false
end function | def request(self, button):
self.requests.put(button, block=False) | Python | nomic_cornstack_python_v1 |
function get_calibration self name shape chunks=none
begin
string Get the calibration array.
set data_items = find all string .//calibrationVector
set tuple data low_res_coords = call read_xml_array data_items name
return call interpolate_xml_array data low_res_coords shape chunks=chunks
end function | def get_calibration(self, name, shape, chunks=None):
"""Get the calibration array."""
data_items = self.root.findall(".//calibrationVector")
data, low_res_coords = self.read_xml_array(data_items, name)
return self.interpolate_xml_array(data, low_res_coords, shape, chunks=chunks) | Python | jtatman_500k |
function copy self
begin
return call Interval start end
end function | def copy(self):
return Interval(self.start, self.end) | Python | nomic_cornstack_python_v1 |
function to_grayscale self
begin
comment Get current image
set tuple curr_img _ = undoStack at - 1
comment Convert to grayscale
set new_img = as type call to_grayscale curr_img uint8
set new_img_path = call _internal_save call fromarray new_img
append undoStack tuple new_img new_img_path
call display_image new_img_path... | def to_grayscale(self):
curr_img, _ = self.undoStack[-1] # Get current image
new_img = to_grayscale(curr_img).astype(np.uint8) # Convert to grayscale
new_img_path = self._internal_save(Image.fromarray(new_img))
self.undoStack.append((new_img, new_img_path))
self.display_image(new... | Python | nomic_cornstack_python_v1 |
comment Componente que se dedica a comprender un lenguaje natural ##
comment -----------------------------------------------------------##
comment Convierte textos de un lenguaje natural en intents ##
comment Importamos metodos de la libreria de utilidades
from libraries.lib_utils import count_words , multi_split , mer... | ###############################################################
## Componente que se dedica a comprender un lenguaje natural ##
##-----------------------------------------------------------##
## Convierte textos de un lenguaje natural en intents ##
###############################################################
... | Python | zaydzuhri_stack_edu_python |
function test_sub_i_less_0
begin
set a : list at int = list 1 2 3 4
set i : int = - 1
set j : int = 3
assert sub a i j == list 1 2 3
end function | def test_sub_i_less_0() -> None:
a: list[int] = [1, 2, 3, 4]
i: int = -1
j: int = 3
assert sub(a, i, j) == [1, 2, 3] | Python | nomic_cornstack_python_v1 |
function is_hidden self path
begin
return false
end function | def is_hidden(self, path):
return False | Python | nomic_cornstack_python_v1 |
comment !/usr/bin/python
import sys
function solve A B K
begin
comment Catalina owns any number less than K.
comment For her to win, random numbers must be such that no
comment bit is 1 for bits in positions greater than len(bin(K)).
comment 3 ^ shared length of A,B + 2 ^ overflow of max(A,B).
comment of course, discou... | #!/usr/bin/python
import sys
def solve(A, B, K):
# Catalina owns any number less than K.
# For her to win, random numbers must be such that no
# bit is 1 for bits in positions greater than len(bin(K)).
# 3 ^ shared length of A,B + 2 ^ overflow of max(A,B).
# of course, discounting the number of 1's there a... | Python | zaydzuhri_stack_edu_python |
comment !/usr/bin/env python
import sys
import os
import datetime
from contextlib import contextmanager
from sqlalchemy import BigInteger , Text , Integer , DateTime , DECIMAL , BOOLEAN , ForeignKey , Column
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.schema import MetaData
from sqlalchemy.o... | #!/usr/bin/env python
import sys
import os
import datetime
from contextlib import contextmanager
from sqlalchemy import (
BigInteger, Text, Integer, DateTime, DECIMAL, BOOLEAN,
ForeignKey,
Column
)
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.schema import MetaData
from sqlalch... | Python | zaydzuhri_stack_edu_python |
from flask import Flask , render_template , request , send_from_directory
import matplotlib.pyplot as plt
import requests
import numpy as np
set app = call Flask __name__
set config at string upload_folder = string ./
decorator call route string /
function home
begin
return call render_template string home.htm
end func... | from flask import Flask, render_template, request, send_from_directory
import matplotlib.pyplot as plt
import requests
import numpy as np
app=Flask(__name__)
app.config['upload_folder']='./'
@app.route('/')
def home():
return render_template('home.htm')
@app.route('/grafik', methods=['POST','GET'])
def grafik():... | Python | zaydzuhri_stack_edu_python |
from abc import ABCMeta
class AudioTranscriber
begin
string Abstract class for VideoPipeline.audio_transcriber
set __metaclass__ = ABCMeta
function transcribe self audio
begin
string Transcribe an audio clip into text. Args: audio: The audio file loaded as bytes. You might do this by loading the file using open(file, "... | from abc import ABCMeta
class AudioTranscriber:
"""
Abstract class for VideoPipeline.audio_transcriber
"""
__metaclass__ = ABCMeta
def transcribe(self, audio):
"""
Transcribe an audio clip into text.
Args:
audio: The audio file loaded as bytes. You might do thi... | Python | zaydzuhri_stack_edu_python |
comment !/usr/bin/env python
comment coding: utf-8
comment In[1]:
comment Código para minimizar as linhas de código
from IPython.display import HTML
call HTML string <script> code_show=true; function code_toggle() { if (code_show){ $('div.input').hide(); } else { $('div.input').show(); } code_show = !code_show } $( doc... | #!/usr/bin/env python
# coding: utf-8
# In[1]:
# Código para minimizar as linhas de código
from IPython.display import HTML
HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide();
} else {
$('div.input').show();
}
code_show = !code_show
}
$( doc... | Python | zaydzuhri_stack_edu_python |
string ' You are given an integer array nums and you have to return a new counts array. The counts array has the property where counts[i] is the number of smaller elements to the right of nums[i]. Example: Input: [5,2,6,1] Output: [2,1,1,0] Explanation: To the right of 5 there are 2 smaller elements (2 and 1). To the r... | ''''
You are given an integer array nums and you have to return a new counts array. The counts array has the property where counts[i] is the number of smaller elements to the right of nums[i].
Example:
Input: [5,2,6,1]
Output: [2,1,1,0]
Explanation:
To the right of 5 there are 2 smaller elements (2 and 1).
To the r... | Python | zaydzuhri_stack_edu_python |
comment - encapsulation: hide delegate from client
comment client -> wrapper -> delegate
comment decouple client and delegate: client need not to change accordingly when delegate changes
comment - replace inheritance with delegation
comment when a class (client) needs to use another class (delegate) but wants more cont... | # - encapsulation: hide delegate from client
# client -> wrapper -> delegate
# decouple client and delegate: client need not to change accordingly when delegate changes
# - replace inheritance with delegation
# when a class (client) needs to use another class (delegate) but wants more control over its interface
#... | Python | zaydzuhri_stack_edu_python |
string Created on Thu Sep 4 14:52:32 2020 @author: Mukilan
string Using different Alogorithmn to find the best algorithmn for regression Problem statement. I used algorithmn like Linear, Random Forest, Gradient Boost Regression. And I visualize the data using seaborn.
import pandas as pd
import numpy as np
import seabo... | """
Created on Thu Sep 4 14:52:32 2020
@author: Mukilan
"""
""" Using different Alogorithmn to find the best algorithmn for regression Problem statement.
I used algorithmn like Linear, Random Forest, Gradient Boost Regression. And I visualize the data using seaborn."""
import pandas as pd
import numpy as np
... | Python | zaydzuhri_stack_edu_python |
function filenames self
begin
return call filenames
end function | def filenames(self):
return self.latest()[0].filenames() | Python | nomic_cornstack_python_v1 |
function init_map city
begin
global map
try
begin
set map = call load_graph city + string _map
end
except FileNotFoundError
begin
print string downloading...
set map = call download_graph city
call save_graph map city + string _map
print string downloaded!
end
end function | def init_map(city):
global map
try:
map = guide.load_graph(city + "_map")
except FileNotFoundError:
print("downloading...")
map = guide.download_graph(city)
guide.save_graph(map, city + "_map")
print("downloaded!") | Python | nomic_cornstack_python_v1 |
function readboards filename
begin
set f = open filename string r
set n = integer strip read line f
set boards = list
for i in range n
begin
yield list comprehension strip read line f for b in range 4
read line f
end
end function
function row b a
begin
for r in b
begin
if call win r a
begin
return true
end
end
return ... | def readboards(filename):
f = open(filename, 'r')
n = int(f.readline().strip())
boards = []
for i in range(n):
yield [f.readline().strip() for b in range(4)]
f.readline()
def row(b, a):
for r in b:
if win(r,a):
return True
return False
def win(... | Python | zaydzuhri_stack_edu_python |
function overlap_conflict out *inputs
begin
from import _bh
for i in inputs
begin
if not call isscalar i
begin
if call may_share_memory out i and not call same_view out i
begin
return true
end
end
end
return false
end function | def overlap_conflict(out, *inputs):
from . import _bh
for i in inputs:
if not np.isscalar(i):
if np.may_share_memory(out, i) and not _bh.same_view(out, i):
return True
return False | Python | nomic_cornstack_python_v1 |
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NPset
A normalized semi-sythetic Python dataset for training small language models on code logic without the overhead of raw code syntax.
Why
Small language models trained on natural language corpora develop latent representations of logical constructs -- iteration, conditionals, data flow, function composition -- yet struggle to apply this reasoning to source code, where syntactic overhead (delimiters, indentation conventions, language-specific idioms) occupies a disproportionate share of the token budget, requires a vocabulary of code-specific tokens rarely encountered during pretraining, and introduces a surface-form distribution shift relative to the model's prior knowledge. NPset addresses this by normalizing Python source through an AST-based converter that strips syntactic noise while preserving the full logical structure of each program, producing a pseudocode representation composed entirely of natural language tokens that aligns more directly with the semantic representations already present in small models, allowing them to reason about what code does rather than expending capacity learning what it looks like.
Format
Parquet, shuffled. Each row:
| Field | Type | Description |
|---|---|---|
code |
string | Normalized pseudocode |
original_code |
string | Original Python source |
original_language |
string | Always Python |
source |
string | Origin dataset identifier |
Sources
| Source | Dataset | Rows |
|---|---|---|
nomic_cornstack_python_v1 |
nomic-ai/cornstack-python-v1 | 3,498,845 |
zaydzuhri_stack_edu_python |
zaydzuhri/stack-edu-python (license_type=no_license) |
3,543,752 |
jtatman_500k |
jtatman/python-code-dataset-500k | 32,590 |
iamtarun_python_18k_alpaca |
iamtarun/python_code_instructions_18k_alpaca | 17,496 |
flytech_python_25k |
flytech/python-codes-25k | 42,968 |
dbands_pythonMath |
dbands/pythonMath | 5,726 |
greatdarklord_python_dataset |
greatdarklord/python_dataset | 18,452 |
| Total | 7,159,829 |
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