向量化¶
简述¶
向量化过程是将item转换为向量的过程，其前置步骤为语法解析、成分分解、令牌化，本部分将先后介绍如何获得数据集、如何使用本地的预训练模型、如何直接调用远程提供的预训练模型。
获得数据集¶
概述¶
此部分通过调用 OpenLUNA.json 获得。
I2V¶
概述¶
使用自己提供的任一预训练模型（给出模型存放路径即可）将给定的题目文本转成向量。
优点：可以使用自己的模型，另可调整训练参数，灵活性强。
D2V¶
导入类¶
[ ]:
from EduNLP.I2V import D2V
输入¶
类型：str
内容：题目文本 （text）
[ ]:
items = [
r"1如图几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\SIFChoice$$\FigureID{1}$",
r"2如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\SIFChoice$$\FigureID{1}$"
]
输出¶
[ ]:
model_path = "../../data/d2v/test_d2v_256.bin"
i2v = D2V("pure_text","d2v",filepath=model_path, pretrained_t2v = False)
item_vectors, token_vectors = i2v(items)
print(item_vectors, token_vectors)
print(len(item_vectors), item_vectors[0].shape) # For d2v, token_vector is None
[array([ 0.10634826, -0.10273553, -0.04942605, 0.15323296, 0.25780037,
-0.06630539, -0.02834828, 0.0065832 , -0.07495898, 0.06529831,
0.07989643, 0.14697047, -0.06513839, -0.23898382, 0.04266138,
0.15211952, 0.17704317, 0.02768603, -0.1854407 , 0.21742067,
0.00984387, 0.06774732, -0.02058181, -0.13266353, -0.20851782,
0.00558438, 0.00460878, -0.05139542, -0.06485009, -0.04847892,
0.09366203, 0.13947366, -0.07552954, 0.05977268, -0.09442269,
0.12721013, 0.13273577, -0.02567345, 0.09778284, -0.08030444,
-0.05697263, -0.02538129, -0.04187621, -0.03123644, 0.11242954,
-0.00650479, 0.06117244, 0.06449873, 0.05163544, 0.01810673,
0.03808153, 0.03746097, 0.1275719 , 0.07381074, -0.14620851,
0.05757713, 0.02838781, -0.12927914, 0.07510903, 0.08147711,
-0.06523656, -0.01486957, 0.02304654, 0.04361376, 0.21421443,
-0.02614699, -0.03873423, 0.21355043, -0.09329057, -0.09335907,
0.08157952, 0.00625934, -0.05051626, -0.07981606, -0.03226125,
0.08958097, -0.04725425, -0.06789905, 0.06730124, 0.06633109,
-0.07370041, -0.13176298, -0.01786646, 0.11614659, -0.04729063,
0.06670643, 0.12553535, -0.1768527 , -0.09982615, -0.07682986,
0.06889272, -0.04036296, 0.03164692, -0.1446157 , 0.03707608,
0.13580611, -0.07748953, -0.02646814, -0.01086655, -0.09684405,
-0.15440914, -0.1067182 , 0.0349819 , 0.10395218, -0.00877615,
0.05640977, -0.01675119, 0.03014355, 0.12512377, 0.01414524,
0.0211464 , -0.06591589, 0.08297148, 0.07012929, -0.10675234,
-0.04061191, 0.01302199, 0.11621079, 0.00885058, -0.0304758 ,
-0.01553952, -0.12865704, 0.17576885, -0.02950172, -0.01326492,
0.01504841, 0.09476334, -0.03984889, 0.00763261, -0.01630022,
-0.03672133, 0.01365909, 0.05986133, -0.10593513, -0.08282372,
-0.08374579, -0.04833878, 0.0489895 , 0.00875692, -0.03651298,
-0.01757672, -0.01623743, 0.23217827, 0.15424854, -0.26954046,
-0.04381514, 0.03540273, 0.06735364, 0.09081568, 0.07033931,
-0.14287043, -0.1028204 , -0.04575491, -0.01768357, -0.08948888,
0.07118583, 0.1542743 , 0.09116074, -0.08839961, 0.01807798,
0.12579061, -0.03509037, 0.08215491, -0.06514787, 0.00351805,
0.04778312, 0.04185999, 0.08844823, 0.10574003, -0.01214823,
0.04865406, -0.03167628, 0.02753052, 0.05206006, -0.15243334,
-0.02005264, -0.00607761, -0.01364831, 0.10955886, -0.0010281 ,
0.11600163, -0.04597978, 0.0601487 , -0.05470827, 0.11942582,
0.00830158, -0.13237426, -0.15353964, -0.07234719, 0.04098862,
0.15481706, 0.01778992, 0.14323051, 0.09922674, 0.12223933,
0.0526598 , 0.0408572 , -0.13533838, 0.02140845, -0.1030432 ,
-0.17960373, -0.0495283 , 0.14249511, 0.0465369 , -0.04416716,
0.11465154, -0.2066736 , -0.02712584, -0.06553549, 0.0557267 ,
0.04696037, -0.02916378, 0.07578196, 0.00608585, 0.00944843,
0.02514744, 0.16452865, 0.0508883 , -0.15239422, -0.01861594,
-0.1283559 , 0.06792712, 0.0826408 , -0.05190438, 0.03456785,
-0.00661102, 0.00788855, 0.02678101, -0.02560065, -0.00632013,
-0.193216 , -0.1322564 , -0.03174976, -0.04030088, 0.01397508,
0.07423516, 0.04445267, 0.0541687 , 0.083343 , -0.15150657,
0.00937343, 0.05487543, 0.02184621, -0.00872194, -0.00329627,
-0.05453907, 0.05444683, -0.02034983, -0.0742873 , -0.01699653,
-0.16100745, 0.10637001, -0.12436336, -0.03511102, -0.20044689,
-0.02411474], dtype=float32), array([ 1.36041582e-01, -1.08781166e-01, -5.83929941e-02, 1.91058487e-01,
3.24488580e-01, -6.02278523e-02, -4.59153317e-02, -2.24303032e-04,
-8.76015052e-02, 8.96644667e-02, 1.04697935e-01, 1.83520690e-01,
-7.28873909e-02, -3.05169255e-01, 4.17109728e-02, 1.82436183e-01,
2.26057917e-01, 2.32112277e-02, -2.30914488e-01, 2.76603520e-01,
-4.85374872e-03, 7.58373216e-02, -2.29087640e-02, -1.79299608e-01,
-2.54585207e-01, 1.64706651e-02, 7.45610800e-03, -7.51212165e-02,
-7.42579326e-02, -4.80351038e-02, 1.25985965e-01, 1.73480287e-01,
-1.01882420e-01, 6.75791055e-02, -1.05399802e-01, 1.51736602e-01,
1.63938925e-01, -3.84903289e-02, 1.10449508e-01, -9.40715224e-02,
-6.31079897e-02, -3.84142622e-02, -4.99494635e-02, -4.23108302e-02,
1.55812502e-01, -1.16190566e-02, 5.13239168e-02, 6.35165498e-02,
6.25813156e-02, 2.21146382e-02, 6.36933595e-02, 5.06636649e-02,
1.47942111e-01, 9.66929942e-02, -1.76337466e-01, 7.13716373e-02,
2.70809997e-02, -1.57342017e-01, 9.11386758e-02, 9.41570252e-02,
-9.42364857e-02, -5.13231056e-03, 1.36982789e-02, 5.77186793e-02,
2.64365107e-01, -3.76182236e-02, -4.40398045e-02, 2.51175314e-01,
-1.05481505e-01, -1.20290719e-01, 9.69798341e-02, 1.16603952e-02,
-7.56024718e-02, -1.08631417e-01, -4.93911207e-02, 1.18807659e-01,
-5.85292391e-02, -8.24716613e-02, 7.88261220e-02, 8.72761682e-02,
-1.01744331e-01, -1.44680306e-01, -2.09336001e-02, 1.33469954e-01,
-3.82428057e-02, 7.42774233e-02, 1.48893908e-01, -2.09778398e-01,
-1.35315523e-01, -8.64634812e-02, 9.07569081e-02, -5.20299003e-02,
5.39300404e-02, -1.71408862e-01, 3.55036259e-02, 1.70207739e-01,
-8.76016989e-02, -2.80412864e-02, -2.70368508e-03, -1.22690618e-01,
-1.63871482e-01, -1.22708760e-01, 3.35654393e-02, 1.26829579e-01,
-1.43117364e-02, 7.96694681e-02, -3.10931951e-02, 2.37075817e-02,
1.55911028e-01, 1.69575382e-02, 1.54271908e-02, -9.58202332e-02,
1.09241918e-01, 8.82035494e-02, -1.39677778e-01, -5.01841754e-02,
2.18386631e-02, 1.57107875e-01, 7.93855730e-03, -3.16538438e-02,
-1.25909718e-02, -1.62555054e-01, 2.32526332e-01, -3.71498615e-02,
-2.24065650e-02, 2.61516422e-02, 1.20939091e-01, -6.72491416e-02,
-4.27399318e-05, -3.77265252e-02, -4.58711684e-02, 2.53423229e-02,
8.39713439e-02, -1.19929694e-01, -1.07749395e-01, -1.10882133e-01,
-4.97324318e-02, 6.16546497e-02, -1.15693109e-02, -3.91881578e-02,
-2.29061693e-02, -2.30852738e-02, 3.04617375e-01, 2.08306596e-01,
-3.39470148e-01, -5.02127111e-02, 5.37177026e-02, 9.11831036e-02,
1.14661761e-01, 9.45202038e-02, -1.62573129e-01, -1.38315439e-01,
-6.47830069e-02, -2.83303317e-02, -1.22525014e-01, 9.37087536e-02,
1.96664244e-01, 1.07929356e-01, -1.03713363e-01, 1.60198733e-02,
1.63076490e-01, -5.17086089e-02, 1.10869825e-01, -7.68428966e-02,
8.37910455e-03, 5.56400865e-02, 6.31182268e-02, 1.21523716e-01,
1.36712998e-01, -3.43602896e-02, 5.95005825e-02, -3.38348858e-02,
3.81066576e-02, 6.04561493e-02, -1.77799106e-01, -3.31094116e-02,
-1.12194773e-02, -3.21154818e-02, 1.08467303e-01, -4.27856669e-03,
1.51000738e-01, -6.47066757e-02, 6.33488074e-02, -6.28948510e-02,
1.29441455e-01, 4.04741522e-03, -1.72044575e-01, -1.91321328e-01,
-8.56087804e-02, 4.62383777e-02, 2.01764986e-01, 3.43908928e-02,
1.77972093e-01, 1.27216563e-01, 1.52360231e-01, 5.92516772e-02,
4.44654636e-02, -1.71815068e-01, 5.64131141e-02, -1.30362645e-01,
-2.15643197e-01, -6.48207739e-02, 1.80758730e-01, 6.18097410e-02,
-6.83161020e-02, 1.69224367e-01, -2.53407449e-01, -4.33427393e-02,
-8.90076980e-02, 7.33624548e-02, 5.79761080e-02, -3.76918130e-02,
8.55930522e-02, 2.99188751e-03, 1.56438854e-02, 2.43693665e-02,
2.17681393e-01, 6.79032505e-02, -1.83962315e-01, -1.40863275e-02,
-1.53916925e-01, 8.76621082e-02, 9.75017548e-02, -6.90853447e-02,
3.22550870e-02, -1.22331316e-02, 8.18267651e-03, 3.01490221e-02,
-1.40115758e-02, 1.17691020e-02, -2.29610443e-01, -1.88834459e-01,
-4.37003709e-02, -3.97654139e-02, 2.05663759e-02, 9.48096588e-02,
4.75249775e-02, 6.99462742e-02, 9.24514905e-02, -1.96493998e-01,
3.27189267e-02, 4.80476059e-02, 3.26564163e-02, -1.90033496e-03,
3.73094343e-03, -5.62442988e-02, 5.95186092e-02, -2.72717867e-02,
-8.34571719e-02, -2.22789161e-02, -1.91354036e-01, 1.27723783e-01,
-1.69917479e-01, -3.11680529e-02, -2.62214512e-01, -2.82380339e-02],
dtype=float32)] None
2 (256,)
[ ]:
from EduData import get_data
get_data("open-luna", "../../data/")
/usr/lib/python3/dist-packages/requests/__init__.py:89: RequestsDependencyWarning: urllib3 (1.26.6) or chardet (3.0.4) doesn't match a supported version!
warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
downloader, INFO http://base.ustc.edu.cn/data/OpenLUNA/OpenLUNA.json is saved as ../../data/OpenLUNA.json
Downloading ../../data/OpenLUNA.json 100.00%: 269KB | 269KB
'../../data/OpenLUNA.json'
W2V¶
[ ]:
from EduNLP.I2V import W2V
model_path = "../../data/w2v/general_literal_300/general_literal_300.kv"
i2v = W2V("pure_text","w2v",filepath=model_path, pretrained_t2v = False)
try:
item_vectors, token_vectors = i2v(items)
print(item_vectors) # 每个句子vector 为np.ndarray
print(token_vectors) # 每个单词vector list
print(len(item_vectors), item_vectors[0].shape,len(token_vectors), len(token_vectors[0]),len(token_vectors[0][0]))
except Exception as e:
print(e)
[array([-1.34266680e-03, 5.19845746e-02, -1.98070258e-02, -4.17470075e-02,
4.92814295e-02, -1.70883536e-01, -2.16597781e-01, -3.12069029e-01,
8.96430463e-02, -1.31331667e-01, 9.16494895e-03, -3.22572999e-02,
3.07940125e-01, 1.92060292e-01, 1.31043345e-02, 6.10962026e-02,
2.21019030e-01, -3.53541046e-01, 1.34150490e-01, 1.14867561e-01,
1.17448963e-01, 2.27990672e-01, -1.65213019e-01, 2.78246611e-01,
-4.36594114e-02, -1.37816787e-01, -1.07707813e-01, -1.80805102e-01,
1.20028563e-01, -1.14409983e-01, 6.19181581e-02, -1.79836392e-01,
7.68677965e-02, 2.41688967e-01, 6.20721914e-02, -7.59824514e-02,
1.79465964e-01, 1.69306010e-01, -1.99512452e-01, -9.75036696e-02,
1.02485821e-01, -1.59723386e-01, -1.67252243e-01, 1.52240042e-02,
-5.98842278e-03, 6.47612512e-02, 8.48228261e-02, 2.67874986e-01,
-1.73656959e-02, -4.40101810e-02, 9.11948457e-02, 1.40905827e-01,
6.33735815e-03, 2.03221604e-01, -1.97303146e-01, 1.32987842e-01,
-1.80283263e-01, 3.64040211e-02, 2.49624569e-02, 7.49479085e-02,
-2.05568615e-02, -4.02397066e-02, -1.08619891e-01, -1.04757406e-01,
-8.36341307e-02, 6.61163032e-02, -1.11632387e-03, 3.96131463e-02,
-3.51454802e-02, 9.09155831e-02, 1.87938929e-01, -2.40521863e-01,
-5.97307160e-02, 1.74426511e-01, 1.56350788e-02, -4.20243442e-02,
-1.90285146e-02, -3.85696471e-01, -1.01543151e-01, -1.42145246e-01,
2.33298853e-01, 1.85939763e-02, -2.68633634e-01, -3.60178575e-02,
3.64447385e-02, -1.70443758e-01, 1.03326524e-02, -1.47003353e-01,
-9.38110873e-02, 1.63190335e-01, 1.20674491e-01, -2.36976147e-02,
-6.52602538e-02, 1.29773334e-01, -4.23593611e-01, 2.43276700e-01,
-8.00978579e-03, -9.39133018e-03, 1.17623486e-01, 1.16482794e-01,
-9.27330479e-02, 6.17316999e-02, 1.51295820e-02, 2.25901395e-01,
7.31975585e-02, 2.29724105e-02, 9.95925292e-02, -1.10697523e-01,
2.28960160e-02, 8.65939483e-02, 1.16645083e-01, -7.00058565e-02,
1.13389529e-01, -5.30471019e-02, 1.43660516e-01, 1.61379650e-02,
6.77419230e-02, 8.09707418e-02, 2.09957212e-01, -6.64654151e-02,
-1.81450248e-01, 2.21659631e-01, -4.53737518e-03, 4.69567403e-02,
8.59350115e-02, 1.17934339e-01, -1.98988736e-01, -4.13361974e-02,
1.26167178e-01, 3.84825058e-02, 1.64396539e-01, -1.63344927e-02,
9.12889242e-02, -1.13650873e-01, -1.37156844e-02, 2.06742659e-02,
-9.15742964e-02, 7.41296187e-02, 2.50813574e-01, -1.35987863e-01,
-1.11708120e-01, -1.52451068e-01, 1.08608082e-01, 4.99855466e-02,
1.68440521e-01, -2.47063249e-01, -2.21773341e-01, 4.81536575e-02,
-7.66365305e-02, 2.55189091e-01, -5.60788438e-03, -2.69066542e-02,
2.07698755e-02, 1.36008840e-02, 1.33086294e-01, -3.80828045e-02,
-7.03251585e-02, -6.18199483e-02, 9.03518647e-02, -1.89310908e-01,
-5.30523732e-02, -2.04426926e-02, -2.27697566e-01, 7.68405125e-02,
1.28568143e-01, 1.07449636e-01, -1.98028013e-01, -2.67155319e-01,
-5.17064631e-02, -1.62200809e-01, 1.87425911e-01, 4.74511087e-02,
-4.24213745e-02, -2.71449953e-01, -2.83543557e-01, -2.36278087e-01,
-4.38764729e-02, 1.67618364e-01, -2.51966029e-01, -2.73265123e-01,
-1.68406263e-01, -3.58684808e-01, 2.44145632e-01, -2.55741596e-01,
-2.28520826e-01, 2.39279866e-01, -1.68833986e-01, 1.66422993e-01,
-3.53969544e-01, -1.10907286e-01, -6.29489049e-02, -4.55605611e-02,
1.46765754e-01, 1.95176788e-02, -3.80197394e-04, 3.36615089e-03,
-1.42359287e-01, -1.06109239e-01, -3.36164385e-02, 3.16832401e-02,
1.09924652e-01, 2.10711379e-02, 1.58359021e-01, 1.71957895e-01,
4.08717275e-01, -4.28679548e-02, -6.48310632e-02, 1.27063962e-02,
5.73479272e-02, 1.40002951e-01, -3.66613895e-01, 8.07148069e-02,
2.11823225e-01, -1.10516161e-01, -2.01001287e-01, 3.22122797e-02,
5.47345541e-02, 2.30176803e-02, -9.94866490e-02, -4.44128877e-03,
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2 (300,) 2 55 300
get_pretrained_i2v¶
概述¶
使用 EduNLP 项目组给定的预训练模型将给定的题目文本转成向量。
优点：简单方便。
缺点：只能使用项目中给定的模型，局限性较大。
导入功能块¶
[ ]:
from EduNLP import get_pretrained_i2v
输入¶
类型：str
内容：题目文本 （text）
[ ]:
item = {
"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构成，三个半圆的直径分别为直角三角形$ABC$的斜边$BC$, 直角边$AB$, $AC$.$\bigtriangleup ABC$的三边所围成的区域记为$I$,黑色部分记为$II$, 其余部分记为$III$.在整个图形中随机取一点，此点取自$I,II,III$的概率分别记为$p_1,p_2,p_3$,则$\SIFChoice$$\FigureID{1}$"
}
模型选择与使用¶
根据题目所属学科选择预训练模型：
预训练模型名称 |
模型训练数据的所属学科 |
|---|---|
d2v_all_256 |
全学科 |
d2v_sci_256 |
理科 |
d2v_eng_256 |
英语 |
d2v_lit_256 |
文科 |
[ ]:
i2v = get_pretrained_i2v("d2v_sci_256")
EduNLP, INFO Use pretrained t2v model d2v_sci_256
downloader, INFO http://base.ustc.edu.cn/data/model_zoo/EduNLP/d2v/general_science_256.zip is saved as /home/lvrui/.EduNLP/model/general_science_256.zip
downloader, INFO file existed, skipped
注意： 默认的 EduNLP 项目存储地址为根目录(
~/.EduNLP)，模型存储地址为项目存储地址下的model文件夹。您可以通过修改下面的环境变量来修改模型存储地址：EduNLP 项目存储地址：
EDUNLPPATH = xx/xx/xx模型存储地址：
EDUNLPMODELPATH = xx/xx/xx
[ ]:
print(i2v(item))
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