MMTEB: Massive Multilingual Text Embedding Benchmark
Paper • 2502.13595 • Published • 47
Datasets:
id stringlengths 9 9 | text stringlengths 312 2.4k | title stringclasses 1
value |
|---|---|---|
000000000 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
V, x = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
V.data += x
#print(V)
with open('result/result_{}.pkl'.format(args.test_ca... | |
000000001 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
sa = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = (sa.count_nonzero()==0)
#print(result)
with open('result/result_{}... | |
000000002 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import stats
x, mu, stddev = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = stats.lognorm(s=stddev, scale=np.exp(mu)).... | |
000000003 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.ndimage
x, shape = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = scipy.ndimage.zoom(x, zoom=(shape[0]/x.shape[0], ... | |
000000004 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import stats
import random
import numpy as np
def poisson_simul(rate, T):
time = random.expovariate(rate)
times = [0]
while (times[-1] < T):
... | |
000000005 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
import numpy as np
sa, sb = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = sa.multiply(sb)
#print(result)
with open('r... | |
000000006 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.optimize
import numpy as np
a, x_true, y, x0, x_lower_bounds = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def residual_ans(x, a, y):
s =... | |
000000007 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.integrate
c, low, high = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def f(c=5, low=0, high=1):
result = scipy.integrate.quadrature(lam... | |
000000008 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import pandas as pd
import io
import numpy as np
from scipy import stats
df = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
indices = [('1415777_at Pnlipr... | |
000000009 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
a = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
kurtosis_result = (sum((a - np.mean(a)) ** 4)/len(a)) / np.std(a)**4
#print(kurtosis... | |
000000010 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy
x, y = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = np.polyfit(np.log(x), y, 1)[::-1]
#print(result)
with open... | |
000000011 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy as sp
from scipy import integrate,stats
def bekkers(x, a, m, d):
p = a*np.exp((-1*(x**(1/3) - m)**2)/(2*d**2))*x**(-2/3)
return(p... | |
000000012 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import pandas as pd
import io
from scipy import stats
df = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = pd.DataFrame(data=stats.zscore(df, axis ... | |
000000013 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.spatial.distance
example_array = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def f(example_array):
import itertools
... | |
000000014 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.optimize as optimize
from math import *
initial_guess = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def g(params):
import numpy as np
... | |
000000015 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.optimize as sciopt
import numpy as np
import pandas as pd
a = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
weights = (a.values / a.values.sum... | |
000000016 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.optimize import fsolve
def eqn(x, a, b):
return x + 2*a - b**2
xdata, bdata = pickle.load(open(f"input/input{args.test_case}.pkl", "rb... | |
000000017 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import stats
import random
import numpy as np
def poisson_simul(rate, T):
time = random.expovariate(rate)
times = [0]
while (times[-1] < T):
... | |
000000018 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.sparse import csr_matrix
arr = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
M = csr_matrix(arr)
result = M.A.diagonal(0)
#p... | |
000000019 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import stats
import random
import numpy as np
def poisson_simul(rate, T):
time = random.expovariate(rate)
times = [0]
while (times[-1] < T):
... | |
000000020 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy
import scipy.optimize
import numpy as np
def test_func(x):
return (x[0])**2+(x[1])**2
def test_grad(x):
return [2*x[0],2*x[1]]
starting_point, dire... | |
000000021 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.stats
z_scores, mu, sigma = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
temp = np.array(z_scores)
p_values = scipy.stats... | |
000000022 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.sparse import lil_matrix
from scipy import sparse
M = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
rows, cols = M.nonzero(... | |
000000023 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
c1, c2 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
Feature = sparse.vstack((c1, c2))
#print(Feature)
with open('result/resu... | |
000000024 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.spatial import distance
shape = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
xs, ys = np.indices(shape)
xs = xs.reshape(shap... | |
000000025 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.spatial.distance
example_array = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
import itertools
n = example_array.max()+1
in... | |
000000026 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import pandas as pd
import numpy as np
import scipy.stats as stats
df = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
import itertools as IT
for col1, col2... | |
000000027 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
import numpy as np
sA, sB = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def f(sA, sB):
result = sA.multiply(sB)
return ... | |
000000028 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.ndimage
square = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def filter_isolated_cells(array, struct):
filtered_array... | |
000000029 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
c1, c2 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
Feature = sparse.hstack((c1, c2)).tocsr()
#print(Feature)
with open('res... | |
000000030 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy
x, y = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = np.polyfit(np.log(x), y, 1)
#print(result)
with open('resu... | |
000000031 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.sparse import csr_matrix
col = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
Max, Min = col.max(), col.min()
#print(Max)
#p... | |
000000032 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
sa = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = (sa.count_nonzero()==0)
#print(result)
with open('result/result_{}... | |
000000033 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy.optimize import curve_fit
import numpy as np
z, Ua, tau, degree = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def fourier(x, *a):
ret = a[... | |
000000034 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.optimize
x, y, p0 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = scipy.optimize.curve_fit(lambda t,a,b, c: a*np.e... | |
000000035 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy
import numpy as np
a = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
a = 1-np.sign(a)
#print(a)
with open('result/result_{}.pkl'.format(ar... | |
000000036 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.spatial
import numpy as np
centroids, data, k = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def find_k_closest(centroids, data, k=1, distan... | |
000000037 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.ndimage
a = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
b = scipy.ndimage.median_filter(a, size=(3, 3), origin=(0, 1))
... | |
000000038 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
sa, sb = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = sparse.hstack((sa, sb)).tocsr()
#print(result)
with open('resu... | |
000000039 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
c1, c2 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
Feature = sparse.hstack((c1, c2)).tocsr()
#print(Feature)
with open('res... | |
000000040 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.stats
N, p = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
n = np.arange(N + 1, dtype=np.int64)
dist = scipy.stats.binom(p=... | |
000000041 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.stats as ss
x1, x2 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
s, c_v, s_l = ss.anderson_ksamp([x1,x2])
result = c_v[2] ... | |
000000042 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.spatial
import scipy.optimize
np.random.seed(100)
points1, N, points2 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
C = s... | |
000000043 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.optimize as sciopt
fp = lambda p, x: p[0]*x[0]+p[1]*x[1]
e = lambda p, x, y: ((fp(p,x)-y)**2).sum()
pmin, pmax, x, y = pickle.load(op... | |
000000044 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import signal
arr, n = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
res = signal.argrelextrema(arr, np.less_equal, order=n, a... | |
000000045 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.spatial
centroids, data = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def find_k_closest(centroids, data, k=1, distance_n... | |
000000046 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.optimize
import numpy as np
a, y, x0 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def residual_ans(x, a, y):
s = ((y - a.dot(x**2))**2)... | |
000000047 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.spatial
points, extraPoints = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
vor = scipy.spatial.Voronoi(points)
kdtree = scipy.spatial.cKDTree(... | |
000000048 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.spatial
import numpy as np
centroids, data = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def find_k_closest(centroids, data, k=1, distance... | |
000000049 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.interpolate
s, t = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
x, y = np.ogrid[-1:1:10j,-2:0:10j]
z = (x + y)*np.exp(-6.0 ... | |
000000050 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.spatial
points, extraPoints = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
vor = scipy.spatial.Voronoi(points)
kdtree = scipy.spatial.cKDTree(... | |
000000051 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.integrate
import numpy as np
N0, time_span = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def dN1_dt (t, N1):
return -100 * N1 + np.sin(t)... | |
000000052 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.interpolate
x, array, x_new = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
new_array = scipy.interpolate.interp1d(x, array,... | |
000000053 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy
import numpy as np
a = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
a = np.sign(a)
#print(a)
with open('result/result_{}.pkl'.format(args... | |
000000054 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.interpolate
points, V, request = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = scipy.interpolate.griddata(points,... | |
000000055 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.stats
z_scores = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
temp = np.array(z_scores)
p_values = scipy.stats.norm.cdf(te... | |
000000056 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.integrate
import numpy as np
N0, time_span = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def dN1_dt (t, N1):
return -100 * N1 + np.sin(t... | |
000000057 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.sparse as sparse
vectors, max_vector_size = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = sparse.lil_matrix((len(... | |
000000058 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.spatial import distance
shape = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def f(shape = (6, 6)):
xs, ys = np.indices(... | |
000000059 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
import numpy as np
matrix = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = sparse.spdiags(matrix, (1, 0, -1), 5, 5).T.A
... | |
000000060 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.optimize import minimize
def function(x):
return -1*(18*x[0]+16*x[1]+12*x[2]+11*x[3])
I = pickle.load(open(f"input/input{args.test_cas... | |
000000061 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import pandas as pd
import io
from scipy import integrate
df = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
df.Time = pd.to_datetime(df.Time, format='%Y-%m... | |
000000062 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import stats
pre_course_scores, during_course_scores = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
p_value = stats.ranksums... | |
000000063 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
V, x, y = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
V = V.copy()
V.data += x
V.eliminate_zeros()
V.data += y
V.eliminate_zeros... | |
000000064 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.sparse import lil_matrix
def f(sA):
rows, cols = sA.nonzero()
sA[cols, rows] = sA[rows, cols]
return sA
sA = pickle.load(open... | |
000000065 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import stats
def f(pre_course_scores, during_course_scores):
p_value = stats.ranksums(pre_course_scores, during_course_scores).pvalue
... | |
000000066 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
sa, sb = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = sparse.vstack((sa, sb)).tocsr()
#print(result)
with open('resu... | |
000000067 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.ndimage
square = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def filter_isolated_cells(array, struct):
filtered_array... | |
000000068 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.stats as ss
x1, x2, x3, x4 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
statistic, critical_values, significance_level = ... | |
000000069 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import pandas as pd
import io
from scipy import stats
df = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = pd.DataFrame(data=stats.zscore(df, axis =... | |
000000070 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.integrate
import math
import numpy as np
x, u, o2 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def NDfx(x):
return((1/math.sqrt((2*math.... | |
000000071 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.interpolate
s, t = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def f(s, t):
x, y = np.ogrid[-1:1:10j,-2:0:10j]
z =... | |
000000072 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.stats
a = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
kurtosis_result = scipy.stats.kurtosis(a)
#print(kurtosis_result)... | |
000000073 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy as sp
from scipy import integrate,stats
def bekkers(x, a, m, d):
p = a*np.exp((-1*(x**(1/3) - m)**2)/(2*d**2))*x**(-2/3)
return(p... | |
000000074 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import ndimage
def f(img):
threshold = 0.75
blobs = img > threshold
labels, result = ndimage.label(blobs)
return result
i... | |
000000075 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import signal
arr, n = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = signal.argrelextrema(arr, np.less_equal, order=n... | |
000000076 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.sparse import csr_matrix
col = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
n = col.shape[0]
val = col.data
for i in range... | |
000000077 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.spatial import distance
shape = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
xs, ys = np.indices(shape)
xs = xs.reshape(shap... | |
000000078 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import sparse
V, x = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
V._update(zip(V.keys(), np.array(list(V.values())) + x))
#... | |
000000079 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.spatial
import scipy.optimize
points1, N, points2 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
C = scipy.spatial.distanc... | |
000000080 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.optimize import fsolve
def eqn(x, a, b):
return x + 2*a - b**2
xdata, adata = pickle.load(open(f"input/input{args.test_case}.pkl", "rb... | |
000000081 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
import numpy as np
import math
sa = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
sa = sparse.csr_matrix(sa.toarray() / np.sqrt(np.... | |
000000082 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import stats
mu, stddev = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
expected_value = np.exp(mu + stddev ** 2 / 2)
median =... | |
000000083 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import ndimage
img = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
threshold = 0.75
blobs = img < threshold
labels, result =... | |
000000084 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import ndimage
img = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
threshold = 0.75
blobs = img > threshold
labels, nlabels ... | |
000000085 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.interpolate
points, V, request = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = scipy.interpolate.griddata(points,... | |
000000086 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import stats
import numpy as np
x, y, alpha = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
s, p = stats.ks_2samp(x, y)
result = (p <= alpha)
... | |
000000087 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
import numpy as np
a, b = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
b = sparse.csr_matrix(a)
b.setdiag(0)
b.eliminate_zeros()
... | |
000000088 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import misc
from scipy.ndimage import rotate
import numpy as np
data_orig, x0, y0, angle = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def rot... | |
000000089 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import stats
import numpy as np
np.random.seed(42)
x, y = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
statistic, p_value = stats.ks_2samp(x, y)... | |
000000090 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import pandas as pd
import io
from scipy import stats
df = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
indices = [('1415777_at Pnliprp1', 'data'), ('141... | |
000000091 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.interpolate
x, y, eval = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = scipy.interpolate.griddata(x, y, eval)
#print(result)
with... | |
000000092 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import stats
import pandas as pd
import numpy as np
LETTERS = list('ABCDEFGHIJKLMNOPQRSTUVWXYZ')
df = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"... | |
000000093 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.integrate
import math
import numpy as np
x, u, o2 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def NDfx(x):
return((1/math.sqrt((2*math.... | |
000000094 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.spatial.distance
example_array = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
import itertools
n = example_array.max()+1
in... | |
000000095 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import interpolate
import numpy as np
x, y, x_val = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = np.zeros((5, 100))
for i in range(5):
... | |
000000096 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.integrate
c, low, high = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = scipy.integrate.quadrature(lambda x: 2*c*x, low, high)[0]
#p... | |
000000097 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.sparse import csr_matrix
col = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
mean = col.mean()
N = col.shape[0]
sqr = col.c... | |
000000098 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.integrate
import numpy as np
N0, time_span = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def dN1_dt(t, N1):
input = 1-np.cos(t) if 0<t<2*... | |
000000099 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.stats
p_values = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
z_scores = scipy.stats.norm.ppf(p_values)
#print(z_scores)
... |
End of preview. Expand in Data Studio
A code retrieval task based on 1,000 data science programming problems from DS-1000. Each query is a natural language description of a data science task (e.g., 'Create a scatter plot of column A vs column B with matplotlib'), and the corpus contains Python code implementations using libraries like pandas, numpy, matplotlib, scikit-learn, and scipy. The task is to retrieve the correct code snippet that solves the described problem. Queries are problem descriptions while the corpus contains Python function implementations focused on data science workflows.
| Task category | t2t |
| Domains | Programming |
| Reference | https://huggingface.co/datasets/embedding-benchmark/DS1000 |
Source datasets:
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("DS1000Retrieval")
evaluator = mteb.MTEB([task])
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
To learn more about how to run models on mteb task check out the GitHub repository.
Citation
If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional processing as a part of the MMTEB Contribution.
@article{lai2022ds,
author = {Lai, Yuhang and Li, Chengxi and Wang, Yiming and Zhang, Tianyi and Zhong, Ruiqi and Zettlemoyer, Luke and Yih, Wen-tau and Fried, Daniel and Wang, Sida and Yu, Tao},
journal = {arXiv preprint arXiv:2211.11501},
title = {DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation},
year = {2022},
}
@article{enevoldsen2025mmtebmassivemultilingualtext,
title={MMTEB: Massive Multilingual Text Embedding Benchmark},
author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
publisher = {arXiv},
journal={arXiv preprint arXiv:2502.13595},
year={2025},
url={https://arxiv.org/abs/2502.13595},
doi = {10.48550/arXiv.2502.13595},
}
@article{muennighoff2022mteb,
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Loïc and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
url = {https://arxiv.org/abs/2210.07316},
doi = {10.48550/ARXIV.2210.07316},
}
Dataset Statistics
Dataset Statistics
The following code contains the descriptive statistics from the task. These can also be obtained using:
import mteb
task = mteb.get_task("DS1000Retrieval")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 3996,
"number_of_characters": 3114529,
"documents_text_statistics": {
"total_text_length": 1365771,
"min_text_length": 311,
"average_text_length": 683.5690690690691,
"max_text_length": 2398,
"unique_texts": 997
},
"documents_image_statistics": null,
"queries_text_statistics": {
"total_text_length": 1748758,
"min_text_length": 105,
"average_text_length": 875.2542542542543,
"max_text_length": 2948,
"unique_texts": 1837
},
"queries_image_statistics": null,
"relevant_docs_statistics": {
"num_relevant_docs": 1998,
"min_relevant_docs_per_query": 1,
"average_relevant_docs_per_query": 1.0,
"max_relevant_docs_per_query": 1,
"unique_relevant_docs": 1998
},
"top_ranked_statistics": null
}
}
This dataset card was automatically generated using MTEB
- Downloads last month
- 16