In [1]:
from nlpia.data.loaders import get_data
word_vectors = get_data('word2vec')
INFO:nlpia.constants:Starting logger in nlpia.constants...
INFO:nlpia.loaders:No BIGDATA index found in c:\users\mcama\appdata\local\programs\python\python36\lib\site-packages\nlpia\data\bigdata_info.csv so copy c:\users\mcama\appdata\local\programs\python\python36\lib\site-packages\nlpia\data\bigdata_info.latest.csv to c:\users\mcama\appdata\local\programs\python\python36\lib\site-packages\nlpia\data\bigdata_info.csv if you want to "freeze" it.
INFO:nlpia.futil:Reading CSV with `read_csv(*('c:\\users\\mcama\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\nlpia\\data\\mavis-batey-greetings.csv',), **{'low_memory': False})`...
INFO:nlpia.futil:Reading CSV with `read_csv(*('c:\\users\\mcama\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\nlpia\\data\\sms-spam.csv',), **{'low_memory': False})`...
INFO:nlpia.loaders:Downloading word2vec
INFO:nlpia.loaders:expanded+normalized file path: c:\users\mcama\appdata\local\programs\python\python36\lib\site-packages\nlpia\bigdata\googlenews-vectors-negative300.bin.gz
INFO:nlpia.loaders:requesting URL: https://www.dropbox.com/s/965dir4dje0hfi4/GoogleNews-vectors-negative300.bin.gz?dl=1
INFO:nlpia.loaders:remote_size: 1647046227
INFO:pugnlp.futil:Unable to stat path 'c:\users\mcama\appdata\local\programs\python\python36\lib\site-packages\nlpia\bigdata\googlenews-vectors-negative300.bin.gz'
INFO:nlpia.loaders:local_size: None
INFO:nlpia.loaders:data path created: pre-existing: c:\users\mcama\appdata\local\programs\python\python36\lib\site-packages\nlpia\bigdata
INFO:nlpia.loaders:downloading to: c:\users\mcama\appdata\local\programs\python\python36\lib\site-packages\nlpia\bigdata\googlenews-vectors-negative300.bin.gz
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 402111/402111 [00:58<00:00, 6886.07it/s]
INFO:nlpia.loaders:local file stat {'name': 'googlenews-vectors-negative300.bin.gz', 'path': 'c:\\users\\mcama\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\nlpia\\bigdata\\googlenews-vectors-negative300.bin.gz', 'dir': 'c:\\users\\mcama\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\nlpia\\bigdata', 'type': 'file', 'size': 1647046227, 'accessed': datetime.datetime(2020, 1, 3, 14, 40, 19, 626452), 'modified': datetime.datetime(2020, 1, 3, 14, 40, 19, 626452), 'changed_any': datetime.datetime(2020, 1, 3, 14, 39, 21, 228602), 'mode': 33206}
INFO:gensim.models.utils_any2vec:loading projection weights from c:\users\mcama\appdata\local\programs\python\python36\lib\site-packages\nlpia\bigdata\googlenews-vectors-negative300.bin.gz
INFO:gensim.models.utils_any2vec:loaded (3000000, 300) matrix from c:\users\mcama\appdata\local\programs\python\python36\lib\site-packages\nlpia\bigdata\googlenews-vectors-negative300.bin.gz
In [97]:
word_vectors.most_similar(positive=['Tesla','car'])
Out[97]:
[('Tesla_Roadster', 0.6967231035232544),
('cars', 0.6825358867645264),
('electric_Tesla_Roadster', 0.6615927219390869),
('Roadster', 0.655667781829834),
('BMW', 0.6320173144340515),
('Prius', 0.6309181451797485),
('microcar', 0.6287855505943298),
('Tesla_Motors', 0.6204673647880554),
('RAV4_EV', 0.6161788105964661),
('Camaro', 0.6161160469055176)]
In [106]:
word_vectors.most_similar(positive=['Tesla','car'], negative=['electric'])
Out[106]:
[('BMW', 0.5349339842796326),
('Mazda_Miata', 0.5336187481880188),
('Camaro', 0.5163763761520386),
('Corvette', 0.5156921148300171),
('Porsche', 0.5118168592453003),
('cars', 0.5070662498474121),
('Lamborghini', 0.5064753293991089),
('Ford_Mustang', 0.4998040795326233),
('Mini_Cooper', 0.4986817240715027),
('Mercedes', 0.4939258098602295)]
In [6]:
word_vectors['phone']
Out[6]:
array([-0.01446533, -0.12792969, -0.11572266, -0.22167969, -0.07373047,
-0.05981445, -0.10009766, -0.06884766, 0.14941406, 0.10107422,
-0.03076172, -0.03271484, -0.03125 , -0.10791016, 0.12158203,
0.16015625, 0.19335938, 0.0065918 , -0.15429688, 0.03710938,
0.22753906, 0.1953125 , 0.08300781, 0.03686523, -0.02148438,
0.01483154, -0.21289062, 0.16015625, 0.29101562, -0.03149414,
-0.05883789, 0.04418945, -0.11767578, -0.12597656, 0.08447266,
-0.10791016, -0.11279297, 0.17871094, 0.04467773, 0.17675781,
-0.17089844, -0.02160645, -0.00061417, -0.17480469, -0.04760742,
0.06835938, -0.0546875 , 0.04467773, -0.19628906, -0.18554688,
-0.10839844, -0.06030273, 0.11474609, 0.08544922, 0.05859375,
0.23925781, -0.07080078, 0.11816406, -0.11132812, 0.08300781,
-0.04394531, 0.00970459, -0.1484375 , 0.265625 , -0.13769531,
0.23535156, -0.19824219, 0.31445312, 0.02734375, 0.16894531,
0.20898438, -0.0480957 , 0.16015625, -0.00147247, -0.13085938,
-0.01312256, 0.07763672, 0.22851562, 0.13867188, -0.2578125 ,
0.06176758, 0.03955078, 0.13867188, 0.08154297, 0.00210571,
-0.05297852, 0.03222656, 0.02148438, -0.21582031, 0.30664062,
-0.05761719, 0.02722168, -0.28320312, -0.4140625 , 0.01745605,
0.04101562, 0.00352478, 0.11279297, 0.046875 , -0.09960938,
-0.18945312, -0.24707031, 0.10058594, 0.35546875, 0.15625 ,
0.02319336, 0.125 , -0.12402344, 0.13378906, 0.17578125,
0.06494141, -0.10644531, 0.00714111, -0.40234375, 0.01989746,
0.11376953, -0.10644531, -0.19921875, 0.29492188, 0.15527344,
-0.13574219, 0.16601562, -0.18457031, 0.28125 , 0.16992188,
-0.04345703, -0.203125 , 0.02380371, 0.00268555, 0.125 ,
-0.14550781, -0.24121094, -0.125 , -0.12304688, -0.01251221,
-0.203125 , 0.11132812, 0.04858398, -0.02246094, -0.19238281,
0.00221252, -0.13378906, -0.11035156, 0.1796875 , 0.14648438,
0.11914062, -0.05419922, -0.3046875 , -0.14257812, -0.0019455 ,
0.296875 , 0.34179688, -0.06396484, -0.00958252, 0.15527344,
-0.06494141, -0.12792969, 0.04736328, 0.00686646, 0.07910156,
-0.31445312, 0.15039062, -0.00558472, -0.00854492, -0.19726562,
0.0456543 , -0.0859375 , -0.3125 , -0.04931641, -0.17480469,
0.23632812, 0.0189209 , -0.01470947, -0.23632812, -0.48828125,
-0.09667969, 0.26953125, 0.2265625 , -0.33007812, 0.01855469,
-0.15332031, -0.13769531, -0.24316406, 0.12109375, 0.43945312,
0.05078125, -0.01574707, 0.14550781, -0.27148438, 0.05249023,
-0.12060547, -0.10742188, -0.22070312, 0.11132812, 0.00765991,
0.234375 , -0.2734375 , 0.00512695, -0.01708984, 0.02258301,
-0.01031494, 0.19433594, 0.07226562, 0.02185059, 0.00915527,
0.15722656, -0.01733398, -0.01928711, -0.08837891, 0.01269531,
0.04125977, -0.05615234, -0.08105469, -0.40625 , 0.04882812,
0.15136719, -0.06030273, -0.10791016, -0.3125 , 0.00247192,
0.08007812, 0.203125 , -0.08789062, 0.06640625, 0.03417969,
0.20898438, -0.29101562, 0.20703125, -0.23730469, -0.05517578,
0.05737305, -0.13769531, -0.34960938, -0.20214844, 0.13671875,
-0.28710938, 0.00592041, -0.21289062, -0.25585938, 0.01397705,
0.3203125 , -0.01123047, -0.08544922, -0.16210938, 0.22558594,
0.05126953, 0.21386719, -0.00552368, 0.05932617, -0.06396484,
-0.04003906, 0.21191406, 0.12255859, -0.02954102, 0.18554688,
0.07421875, 0.20605469, -0.40429688, -0.05761719, -0.09521484,
-0.00830078, -0.14257812, -0.22265625, -0.22363281, -0.16601562,
0.29492188, -0.01190186, -0.11132812, -0.08642578, -0.19140625,
0.01818848, 0.17675781, 0.04077148, -0.2734375 , -0.00708008,
-0.09472656, 0.32421875, 0.05322266, 0.046875 , 0.11376953,
0.15722656, 0.06201172, 0.07275391, -0.09179688, -0.09521484,
-0.10839844, 0.07470703, 0.10742188, -0.02856445, 0.16015625,
-0.07910156, 0.15722656, -0.06152344, -0.17480469, 0.08007812,
-0.13671875, -0.18359375, -0.05200195, -0.00585938, -0.15625 ],
dtype=float32)
In [104]:
word_vectors.wv.vocab
c:\users\mcama\appdata\local\programs\python\python36\lib\site-packages\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).
"""Entry point for launching an IPython kernel.
Out[104]:
{'</s>': <gensim.models.keyedvectors.Vocab at 0x22744f5cf28>,
'in': <gensim.models.keyedvectors.Vocab at 0x22744f5c668>,
'for': <gensim.models.keyedvectors.Vocab at 0x22744f5ca20>,
'that': <gensim.models.keyedvectors.Vocab at 0x22744f5c710>,
'is': <gensim.models.keyedvectors.Vocab at 0x22744f5cc50>,
'on': <gensim.models.keyedvectors.Vocab at 0x22744f5cc88>,
'##': <gensim.models.keyedvectors.Vocab at 0x22744f5cbe0>,
'The': <gensim.models.keyedvectors.Vocab at 0x22744f5c5c0>,
'with': <gensim.models.keyedvectors.Vocab at 0x22744f5ca90>,
'said': <gensim.models.keyedvectors.Vocab at 0x22744f5c4e0>,
'was': <gensim.models.keyedvectors.Vocab at 0x22744f5cb70>,
'the': <gensim.models.keyedvectors.Vocab at 0x227445895c0>,
'at': <gensim.models.keyedvectors.Vocab at 0x22744b00320>,
'not': <gensim.models.keyedvectors.Vocab at 0x22744b007f0>,
'as': <gensim.models.keyedvectors.Vocab at 0x22744b00ef0>,
'it': <gensim.models.keyedvectors.Vocab at 0x22744b00b38>,
'be': <gensim.models.keyedvectors.Vocab at 0x22744b00c50>,
'from': <gensim.models.keyedvectors.Vocab at 0x22744b00b00>,
'by': <gensim.models.keyedvectors.Vocab at 0x22744b00978>,
'are': <gensim.models.keyedvectors.Vocab at 0x22744b00710>,
'I': <gensim.models.keyedvectors.Vocab at 0x22744b00c18>,
'have': <gensim.models.keyedvectors.Vocab at 0x22744b00f60>,
'he': <gensim.models.keyedvectors.Vocab at 0x22744b00ac8>,
'will': <gensim.models.keyedvectors.Vocab at 0x22744b00e48>,
'has': <gensim.models.keyedvectors.Vocab at 0x22744b00860>,
'####': <gensim.models.keyedvectors.Vocab at 0x22741df02b0>,
'his': <gensim.models.keyedvectors.Vocab at 0x22744b136a0>,
'an': <gensim.models.keyedvectors.Vocab at 0x22744b130f0>,
'this': <gensim.models.keyedvectors.Vocab at 0x22744b13128>,
'or': <gensim.models.keyedvectors.Vocab at 0x22744b13198>,
'their': <gensim.models.keyedvectors.Vocab at 0x22744b13048>,
'who': <gensim.models.keyedvectors.Vocab at 0x22744b13208>,
'they': <gensim.models.keyedvectors.Vocab at 0x22744b132b0>,
'but': <gensim.models.keyedvectors.Vocab at 0x22744b13358>,
'$': <gensim.models.keyedvectors.Vocab at 0x22744b132e8>,
'had': <gensim.models.keyedvectors.Vocab at 0x22744b13400>,
'year': <gensim.models.keyedvectors.Vocab at 0x22744b13390>,
'were': <gensim.models.keyedvectors.Vocab at 0x22744b13710>,
'we': <gensim.models.keyedvectors.Vocab at 0x22744b13748>,
'more': <gensim.models.keyedvectors.Vocab at 0x22744b13668>,
'###': <gensim.models.keyedvectors.Vocab at 0x22744b137f0>,
'up': <gensim.models.keyedvectors.Vocab at 0x22744b13860>,
'been': <gensim.models.keyedvectors.Vocab at 0x22744b138d0>,
'you': <gensim.models.keyedvectors.Vocab at 0x22744b13940>,
'its': <gensim.models.keyedvectors.Vocab at 0x22744b139b0>,
'one': <gensim.models.keyedvectors.Vocab at 0x22744b13a20>,
'about': <gensim.models.keyedvectors.Vocab at 0x22744b13a90>,
'would': <gensim.models.keyedvectors.Vocab at 0x22744b13b00>,
'which': <gensim.models.keyedvectors.Vocab at 0x22744b13b70>,
'out': <gensim.models.keyedvectors.Vocab at 0x22744b13be0>,
'can': <gensim.models.keyedvectors.Vocab at 0x22744b13c50>,
'It': <gensim.models.keyedvectors.Vocab at 0x22744b13cc0>,
'all': <gensim.models.keyedvectors.Vocab at 0x22744b13d30>,
'also': <gensim.models.keyedvectors.Vocab at 0x22744b13da0>,
'two': <gensim.models.keyedvectors.Vocab at 0x22744b13e10>,
'after': <gensim.models.keyedvectors.Vocab at 0x22744b13e80>,
'first': <gensim.models.keyedvectors.Vocab at 0x22744b13ef0>,
'He': <gensim.models.keyedvectors.Vocab at 0x22744b13f60>,
'do': <gensim.models.keyedvectors.Vocab at 0x22744b13fd0>,
'time': <gensim.models.keyedvectors.Vocab at 0x22744b27080>,
'than': <gensim.models.keyedvectors.Vocab at 0x22744b270f0>,
'when': <gensim.models.keyedvectors.Vocab at 0x22744b27160>,
'We': <gensim.models.keyedvectors.Vocab at 0x22744b271d0>,
'over': <gensim.models.keyedvectors.Vocab at 0x22744b27240>,
'last': <gensim.models.keyedvectors.Vocab at 0x22744b272b0>,
'new': <gensim.models.keyedvectors.Vocab at 0x22744b27320>,
'other': <gensim.models.keyedvectors.Vocab at 0x22744b27390>,
'her': <gensim.models.keyedvectors.Vocab at 0x22744b27400>,
'people': <gensim.models.keyedvectors.Vocab at 0x22744b27470>,
'into': <gensim.models.keyedvectors.Vocab at 0x22744b274e0>,
'In': <gensim.models.keyedvectors.Vocab at 0x22744b27550>,
'our': <gensim.models.keyedvectors.Vocab at 0x22744b275c0>,
'there': <gensim.models.keyedvectors.Vocab at 0x22744b27630>,
'A': <gensim.models.keyedvectors.Vocab at 0x22744b27668>,
'she': <gensim.models.keyedvectors.Vocab at 0x22744b276d8>,
'could': <gensim.models.keyedvectors.Vocab at 0x22744b27748>,
'just': <gensim.models.keyedvectors.Vocab at 0x22744b277b8>,
'years': <gensim.models.keyedvectors.Vocab at 0x22744b27828>,
'some': <gensim.models.keyedvectors.Vocab at 0x22744b27898>,
'U.S.': <gensim.models.keyedvectors.Vocab at 0x22744b27908>,
'three': <gensim.models.keyedvectors.Vocab at 0x22744b27978>,
'million': <gensim.models.keyedvectors.Vocab at 0x22744b279e8>,
'them': <gensim.models.keyedvectors.Vocab at 0x22744b27a58>,
'what': <gensim.models.keyedvectors.Vocab at 0x22744b27ac8>,
'But': <gensim.models.keyedvectors.Vocab at 0x22744b27b38>,
'so': <gensim.models.keyedvectors.Vocab at 0x22744b27ba8>,
'no': <gensim.models.keyedvectors.Vocab at 0x22744b27c18>,
'like': <gensim.models.keyedvectors.Vocab at 0x22744b27c88>,
'if': <gensim.models.keyedvectors.Vocab at 0x22744b27cf8>,
'only': <gensim.models.keyedvectors.Vocab at 0x22744b27d68>,
'percent': <gensim.models.keyedvectors.Vocab at 0x22744b27dd8>,
'get': <gensim.models.keyedvectors.Vocab at 0x22744b27e48>,
'did': <gensim.models.keyedvectors.Vocab at 0x22744b27eb8>,
'him': <gensim.models.keyedvectors.Vocab at 0x22744b27f28>,
'game': <gensim.models.keyedvectors.Vocab at 0x22744b27f98>,
'back': <gensim.models.keyedvectors.Vocab at 0x22744b29048>,
'because': <gensim.models.keyedvectors.Vocab at 0x22744b290b8>,
'now': <gensim.models.keyedvectors.Vocab at 0x22744b29128>,
'#.#': <gensim.models.keyedvectors.Vocab at 0x22744b29198>,
'before': <gensim.models.keyedvectors.Vocab at 0x22744b29208>,
'company': <gensim.models.keyedvectors.Vocab at 0x22744b29278>,
'any': <gensim.models.keyedvectors.Vocab at 0x22744b292e8>,
'team': <gensim.models.keyedvectors.Vocab at 0x22744b29358>,
'against': <gensim.models.keyedvectors.Vocab at 0x22744b293c8>,
'off': <gensim.models.keyedvectors.Vocab at 0x22744b29438>,
'This': <gensim.models.keyedvectors.Vocab at 0x22744b294a8>,
'most': <gensim.models.keyedvectors.Vocab at 0x22744b29518>,
'made': <gensim.models.keyedvectors.Vocab at 0x22744b29588>,
'through': <gensim.models.keyedvectors.Vocab at 0x22744b295f8>,
'make': <gensim.models.keyedvectors.Vocab at 0x22744b29668>,
'second': <gensim.models.keyedvectors.Vocab at 0x22744b296d8>,
'state': <gensim.models.keyedvectors.Vocab at 0x22744b29748>,
'well': <gensim.models.keyedvectors.Vocab at 0x22744b297b8>,
'day': <gensim.models.keyedvectors.Vocab at 0x22744b29828>,
'season': <gensim.models.keyedvectors.Vocab at 0x22744b29898>,
'says': <gensim.models.keyedvectors.Vocab at 0x22744b29908>,
'week': <gensim.models.keyedvectors.Vocab at 0x22744b29978>,
'where': <gensim.models.keyedvectors.Vocab at 0x22744b299e8>,
'while': <gensim.models.keyedvectors.Vocab at 0x22744b29a58>,
'down': <gensim.models.keyedvectors.Vocab at 0x22744b29ac8>,
'being': <gensim.models.keyedvectors.Vocab at 0x22744b29b38>,
'government': <gensim.models.keyedvectors.Vocab at 0x22744b29b70>,
'your': <gensim.models.keyedvectors.Vocab at 0x227449bc048>,
'#-#': <gensim.models.keyedvectors.Vocab at 0x22728d5b1d0>,
'home': <gensim.models.keyedvectors.Vocab at 0x22728d5b0b8>,
'going': <gensim.models.keyedvectors.Vocab at 0x22744b00be0>,
'my': <gensim.models.keyedvectors.Vocab at 0x22744b00518>,
'good': <gensim.models.keyedvectors.Vocab at 0x22744b00550>,
'They': <gensim.models.keyedvectors.Vocab at 0x22744b006a0>,
"'re": <gensim.models.keyedvectors.Vocab at 0x22744b006d8>,
'should': <gensim.models.keyedvectors.Vocab at 0x22744b00160>,
'many': <gensim.models.keyedvectors.Vocab at 0x22744b00d68>,
'way': <gensim.models.keyedvectors.Vocab at 0x22744b005f8>,
'those': <gensim.models.keyedvectors.Vocab at 0x22744b00828>,
'four': <gensim.models.keyedvectors.Vocab at 0x22744b00748>,
'during': <gensim.models.keyedvectors.Vocab at 0x22744b00f28>,
'such': <gensim.models.keyedvectors.Vocab at 0x22744b001d0>,
'may': <gensim.models.keyedvectors.Vocab at 0x22744b00390>,
'very': <gensim.models.keyedvectors.Vocab at 0x22744b002e8>,
'how': <gensim.models.keyedvectors.Vocab at 0x22744b004e0>,
'since': <gensim.models.keyedvectors.Vocab at 0x22744b00cf8>,
'work': <gensim.models.keyedvectors.Vocab at 0x22744b00208>,
'take': <gensim.models.keyedvectors.Vocab at 0x22744b00080>,
'including': <gensim.models.keyedvectors.Vocab at 0x22744b000f0>,
'high': <gensim.models.keyedvectors.Vocab at 0x22744b00278>,
'then': <gensim.models.keyedvectors.Vocab at 0x22744f5cb38>,
'%': <gensim.models.keyedvectors.Vocab at 0x22744f5cb00>,
'next': <gensim.models.keyedvectors.Vocab at 0x22744f5ce10>,
'#,###': <gensim.models.keyedvectors.Vocab at 0x22744f5c4a8>,
'By': <gensim.models.keyedvectors.Vocab at 0x22744f5c630>,
'much': <gensim.models.keyedvectors.Vocab at 0x22744f5c9e8>,
'still': <gensim.models.keyedvectors.Vocab at 0x22744f5cf98>,
'go': <gensim.models.keyedvectors.Vocab at 0x22744b29be0>,
'think': <gensim.models.keyedvectors.Vocab at 0x22744b29c50>,
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'includes': <gensim.models.keyedvectors.Vocab at 0x22744b4c668>,
'post': <gensim.models.keyedvectors.Vocab at 0x22744b4c6d8>,
'Canada': <gensim.models.keyedvectors.Vocab at 0x22744b4c748>,
'probably': <gensim.models.keyedvectors.Vocab at 0x22744b4c780>,
'related': <gensim.models.keyedvectors.Vocab at 0x22744b4c7f0>,
'training': <gensim.models.keyedvectors.Vocab at 0x22744b4c828>,
'allowed': <gensim.models.keyedvectors.Vocab at 0x22744b4c898>,
'class': <gensim.models.keyedvectors.Vocab at 0x22744b4c908>,
'bit': <gensim.models.keyedvectors.Vocab at 0x22744b4c978>,
'video': <gensim.models.keyedvectors.Vocab at 0x22744b4c9e8>,
'Michael': <gensim.models.keyedvectors.Vocab at 0x22744b4ca58>,
'An': <gensim.models.keyedvectors.Vocab at 0x22744b4cac8>,
'sent': <gensim.models.keyedvectors.Vocab at 0x22744b4cb38>,
'education': <gensim.models.keyedvectors.Vocab at 0x22744b4cb70>,
'states': <gensim.models.keyedvectors.Vocab at 0x22744b4cbe0>,
'straight': <gensim.models.keyedvectors.Vocab at 0x22744b4cc18>,
'love': <gensim.models.keyedvectors.Vocab at 0x22744b4cc88>,
'beat': <gensim.models.keyedvectors.Vocab at 0x22744b4ccf8>,
'hold': <gensim.models.keyedvectors.Vocab at 0x22744b4cd68>,
'turn': <gensim.models.keyedvectors.Vocab at 0x22744b4cdd8>,
'finished': <gensim.models.keyedvectors.Vocab at 0x22744b4ce10>,
'network': <gensim.models.keyedvectors.Vocab at 0x22744b4ce80>,
'Smith': <gensim.models.keyedvectors.Vocab at 0x22744b4cef0>,
'buy': <gensim.models.keyedvectors.Vocab at 0x22744b4cf60>,
'foreign': <gensim.models.keyedvectors.Vocab at 0x22744b4cfd0>,
'especially': <gensim.models.keyedvectors.Vocab at 0x22744b4f048>,
'groups': <gensim.models.keyedvectors.Vocab at 0x22744b4f0b8>,
'wants': <gensim.models.keyedvectors.Vocab at 0x22744b4f128>,
'title': <gensim.models.keyedvectors.Vocab at 0x22744b4f198>,
'included': <gensim.models.keyedvectors.Vocab at 0x22744b4f1d0>,
'turned': <gensim.models.keyedvectors.Vocab at 0x22744b4f240>,
'bank': <gensim.models.keyedvectors.Vocab at 0x22744b4f2b0>,
'Florida': <gensim.models.keyedvectors.Vocab at 0x22744b4f320>,
'efforts': <gensim.models.keyedvectors.Vocab at 0x22744b4f390>,
'personal': <gensim.models.keyedvectors.Vocab at 0x22744b4f3c8>,
'businesses': <gensim.models.keyedvectors.Vocab at 0x22744b4f400>,
'August': <gensim.models.keyedvectors.Vocab at 0x22744b4f470>,
'California': <gensim.models.keyedvectors.Vocab at 0x22744b4f4a8>,
'situation': <gensim.models.keyedvectors.Vocab at 0x22744b4f4e0>,
'district': <gensim.models.keyedvectors.Vocab at 0x22744b4f518>,
'allow': <gensim.models.keyedvectors.Vocab at 0x22744b4f588>,
'helped': <gensim.models.keyedvectors.Vocab at 0x22744b4f5f8>,
'body': <gensim.models.keyedvectors.Vocab at 0x22744b4f668>,
'nothing': <gensim.models.keyedvectors.Vocab at 0x22744b4f6d8>,
'soon': <gensim.models.keyedvectors.Vocab at 0x22744b4f748>,
'safety': <gensim.models.keyedvectors.Vocab at 0x22744b4f7b8>,
'officer': <gensim.models.keyedvectors.Vocab at 0x22744b4f828>,
'cents': <gensim.models.keyedvectors.Vocab at 0x22744b4f898>,
'Europe': <gensim.models.keyedvectors.Vocab at 0x22744b4f908>,
'St.': <gensim.models.keyedvectors.Vocab at 0x22744b4f978>,
'additional': <gensim.models.keyedvectors.Vocab at 0x22744b4f9b0>,
'spokesman': <gensim.models.keyedvectors.Vocab at 0x22744b4f9e8>,
'February': <gensim.models.keyedvectors.Vocab at 0x22744b4fa20>,
'wife': <gensim.models.keyedvectors.Vocab at 0x22744b4fa90>,
'showed': <gensim.models.keyedvectors.Vocab at 0x22744b4fb00>,
'leave': <gensim.models.keyedvectors.Vocab at 0x22744b4fb70>,
'investors': <gensim.models.keyedvectors.Vocab at 0x22744b4fba8>,
'parents': <gensim.models.keyedvectors.Vocab at 0x22744b4fc18>,
'medical': <gensim.models.keyedvectors.Vocab at 0x22744b4fc88>,
'spending': <gensim.models.keyedvectors.Vocab at 0x22744b4fcc0>,
'non': <gensim.models.keyedvectors.Vocab at 0x22744b4fd30>,
'London': <gensim.models.keyedvectors.Vocab at 0x22744b4fda0>,
'Council': <gensim.models.keyedvectors.Vocab at 0x22744b4fe10>,
'matter': <gensim.models.keyedvectors.Vocab at 0x22744b4fe80>,
'spent': <gensim.models.keyedvectors.Vocab at 0x22744b4fef0>,
'child': <gensim.models.keyedvectors.Vocab at 0x22744b4ff60>,
'World': <gensim.models.keyedvectors.Vocab at 0x22744b4ffd0>,
'effort': <gensim.models.keyedvectors.Vocab at 0x22744b53080>,
'opening': <gensim.models.keyedvectors.Vocab at 0x22744b530f0>,
'either': <gensim.models.keyedvectors.Vocab at 0x22744b53160>,
'range': <gensim.models.keyedvectors.Vocab at 0x22744b531d0>,
'question': <gensim.models.keyedvectors.Vocab at 0x22744b53208>,
'European': <gensim.models.keyedvectors.Vocab at 0x22744b53240>,
'goals': <gensim.models.keyedvectors.Vocab at 0x22744b532b0>,
'administration': <gensim.models.keyedvectors.Vocab at 0x22744b532e8>,
'friends': <gensim.models.keyedvectors.Vocab at 0x22744b53358>,
'himself': <gensim.models.keyedvectors.Vocab at 0x22744b533c8>,
'shows': <gensim.models.keyedvectors.Vocab at 0x22744b53438>,
'difficult': <gensim.models.keyedvectors.Vocab at 0x22744b53470>,
'kids': <gensim.models.keyedvectors.Vocab at 0x22744b534e0>,
'paid': <gensim.models.keyedvectors.Vocab at 0x22744b53550>,
'create': <gensim.models.keyedvectors.Vocab at 0x22744b535c0>,
'cash': <gensim.models.keyedvectors.Vocab at 0x22744b53630>,
'age': <gensim.models.keyedvectors.Vocab at 0x22744b536a0>,
'league': <gensim.models.keyedvectors.Vocab at 0x22744b53710>,
'form': <gensim.models.keyedvectors.Vocab at 0x22744b53780>,
'impact': <gensim.models.keyedvectors.Vocab at 0x22744b537f0>,
'drive': <gensim.models.keyedvectors.Vocab at 0x22744b53860>,
'someone': <gensim.models.keyedvectors.Vocab at 0x22744b538d0>,
'became': <gensim.models.keyedvectors.Vocab at 0x22744b53940>,
'stay': <gensim.models.keyedvectors.Vocab at 0x22744b539b0>,
'fight': <gensim.models.keyedvectors.Vocab at 0x22744b53a20>,
'significant': <gensim.models.keyedvectors.Vocab at 0x22744b53a58>,
'firm': <gensim.models.keyedvectors.Vocab at 0x22744b53ac8>,
'Senate': <gensim.models.keyedvectors.Vocab at 0x22744b53b38>,
'hospital': <gensim.models.keyedvectors.Vocab at 0x22744b53b70>,
'charged': <gensim.models.keyedvectors.Vocab at 0x22744b53be0>,
'operating': <gensim.models.keyedvectors.Vocab at 0x22744b53c18>,
'main': <gensim.models.keyedvectors.Vocab at 0x22744b53c88>,
'book': <gensim.models.keyedvectors.Vocab at 0x22744b53cf8>,
'success': <gensim.models.keyedvectors.Vocab at 0x22744b53d68>,
'son': <gensim.models.keyedvectors.Vocab at 0x22744b53dd8>,
'trading': <gensim.models.keyedvectors.Vocab at 0x22744b53e48>,
'###-####': <gensim.models.keyedvectors.Vocab at 0x22744b53e80>,
'focus': <gensim.models.keyedvectors.Vocab at 0x22744b53ef0>,
'room': <gensim.models.keyedvectors.Vocab at 0x22744b53f60>,
'continued': <gensim.models.keyedvectors.Vocab at 0x22744b53f98>,
'Congress': <gensim.models.keyedvectors.Vocab at 0x22744b53fd0>,
'everything': <gensim.models.keyedvectors.Vocab at 0x22744b55048>,
'Park': <gensim.models.keyedvectors.Vocab at 0x22744b550b8>,
'agency': <gensim.models.keyedvectors.Vocab at 0x22744b55128>,
'brought': <gensim.models.keyedvectors.Vocab at 0x22744b55198>,
'talk': <gensim.models.keyedvectors.Vocab at 0x22744b55208>,
'break': <gensim.models.keyedvectors.Vocab at 0x22744b55278>,
'air': <gensim.models.keyedvectors.Vocab at 0x22744b552e8>,
'software': <gensim.models.keyedvectors.Vocab at 0x22744b55320>,
'decided': <gensim.models.keyedvectors.Vocab at 0x22744b55390>,
'Do': <gensim.models.keyedvectors.Vocab at 0x22744b55400>,
'ready': <gensim.models.keyedvectors.Vocab at 0x22744b55470>,
'arrested': <gensim.models.keyedvectors.Vocab at 0x22744b554a8>,
'track': <gensim.models.keyedvectors.Vocab at 0x22744b55518>,
'provides': <gensim.models.keyedvectors.Vocab at 0x22744b55550>,
'mother': <gensim.models.keyedvectors.Vocab at 0x22744b555c0>,
'base': <gensim.models.keyedvectors.Vocab at 0x22744b55630>,
'trial': <gensim.models.keyedvectors.Vocab at 0x22744b556a0>,
'phone': <gensim.models.keyedvectors.Vocab at 0x22744b55710>,
'My': <gensim.models.keyedvectors.Vocab at 0x22744b55780>,
'build': <gensim.models.keyedvectors.Vocab at 0x22744b557f0>,
'conditions': <gensim.models.keyedvectors.Vocab at 0x22744b55828>,
'rest': <gensim.models.keyedvectors.Vocab at 0x22744b55898>,
'Johnson': <gensim.models.keyedvectors.Vocab at 0x22744b55908>,
'terms': <gensim.models.keyedvectors.Vocab at 0x22744b55978>,
'expect': <gensim.models.keyedvectors.Vocab at 0x22744b559e8>,
'England': <gensim.models.keyedvectors.Vocab at 0x22744b55a58>,
'Israel': <gensim.models.keyedvectors.Vocab at 0x22744b55ac8>,
'despite': <gensim.models.keyedvectors.Vocab at 0x22744b55b38>,
'closed': <gensim.models.keyedvectors.Vocab at 0x22744b55ba8>,
'starting': <gensim.models.keyedvectors.Vocab at 0x22744b55be0>,
'provided': <gensim.models.keyedvectors.Vocab at 0x22744b55c18>,
'pressure': <gensim.models.keyedvectors.Vocab at 0x22744b55c50>,
'lives': <gensim.models.keyedvectors.Vocab at 0x22744b55cc0>,
'step': <gensim.models.keyedvectors.Vocab at 0x22744b55d30>,
'remain': <gensim.models.keyedvectors.Vocab at 0x22744b55da0>,
'similar': <gensim.models.keyedvectors.Vocab at 0x22744b55e10>,
'charge': <gensim.models.keyedvectors.Vocab at 0x22744b55e80>,
'date': <gensim.models.keyedvectors.Vocab at 0x22744b55ef0>,
'whole': <gensim.models.keyedvectors.Vocab at 0x22744b55f60>,
'land': <gensim.models.keyedvectors.Vocab at 0x22744b55fd0>,
'growing': <gensim.models.keyedvectors.Vocab at 0x22744b57080>,
'James': <gensim.models.keyedvectors.Vocab at 0x22744b570f0>,
'Internet': <gensim.models.keyedvectors.Vocab at 0x22744b57128>,
'projects': <gensim.models.keyedvectors.Vocab at 0x22744b57160>,
'British': <gensim.models.keyedvectors.Vocab at 0x22744b571d0>,
'cases': <gensim.models.keyedvectors.Vocab at 0x22744b57240>,
'ground': <gensim.models.keyedvectors.Vocab at 0x22744b572b0>,
'legal': <gensim.models.keyedvectors.Vocab at 0x22744b57320>,
'International': <gensim.models.keyedvectors.Vocab at 0x22744b57358>,
'agreed': <gensim.models.keyedvectors.Vocab at 0x22744b573c8>,
'tell': <gensim.models.keyedvectors.Vocab at 0x22744b57438>,
'test': <gensim.models.keyedvectors.Vocab at 0x22744b574a8>,
'everyone': <gensim.models.keyedvectors.Vocab at 0x22744b574e0>,
'pretty': <gensim.models.keyedvectors.Vocab at 0x22744b57550>,
'authorities': <gensim.models.keyedvectors.Vocab at 0x22744b57588>,
'Two': <gensim.models.keyedvectors.Vocab at 0x22744b575f8>,
'above': <gensim.models.keyedvectors.Vocab at 0x22744b57668>,
'moved': <gensim.models.keyedvectors.Vocab at 0x22744b576d8>,
'profit': <gensim.models.keyedvectors.Vocab at 0x22744b57748>,
'throughout': <gensim.models.keyedvectors.Vocab at 0x22744b57780>,
'inside': <gensim.models.keyedvectors.Vocab at 0x22744b577f0>,
'ability': <gensim.models.keyedvectors.Vocab at 0x22744b57860>,
'overall': <gensim.models.keyedvectors.Vocab at 0x22744b578d0>,
'pass': <gensim.models.keyedvectors.Vocab at 0x22744b57940>,
'officers': <gensim.models.keyedvectors.Vocab at 0x22744b57978>,
'rather': <gensim.models.keyedvectors.Vocab at 0x22744b579e8>,
'Australia': <gensim.models.keyedvectors.Vocab at 0x22744b57a20>,
'actually': <gensim.models.keyedvectors.Vocab at 0x22744b57a58>,
'county': <gensim.models.keyedvectors.Vocab at 0x22744b57ac8>,
'amount': <gensim.models.keyedvectors.Vocab at 0x22744b57b38>,
'scheduled': <gensim.models.keyedvectors.Vocab at 0x22744b57b70>,
'themselves': <gensim.models.keyedvectors.Vocab at 0x22744b57ba8>,
'organization': <gensim.models.keyedvectors.Vocab at 0x22744b57be0>,
'giving': <gensim.models.keyedvectors.Vocab at 0x22744b57c50>,
'credit': <gensim.models.keyedvectors.Vocab at 0x22744b57cc0>,
'father': <gensim.models.keyedvectors.Vocab at 0x22744b57d30>,
'drug': <gensim.models.keyedvectors.Vocab at 0x22744b57da0>,
'investigation': <gensim.models.keyedvectors.Vocab at 0x22744b57dd8>,
'families': <gensim.models.keyedvectors.Vocab at 0x22744b57e10>,
'Republican': <gensim.models.keyedvectors.Vocab at 0x22744b57e48>,
'funds': <gensim.models.keyedvectors.Vocab at 0x22744b57eb8>,
'patients': <gensim.models.keyedvectors.Vocab at 0x22744b57ef0>,
'takes': <gensim.models.keyedvectors.Vocab at 0x22744b57f60>,
'systems': <gensim.models.keyedvectors.Vocab at 0x22744b57fd0>,
'Japan': <gensim.models.keyedvectors.Vocab at 0x22744b5b080>,
'complete': <gensim.models.keyedvectors.Vocab at 0x22744b5b0b8>,
'sold': <gensim.models.keyedvectors.Vocab at 0x22744b5b128>,
'practice': <gensim.models.keyedvectors.Vocab at 0x22744b5b160>,
'calls': <gensim.models.keyedvectors.Vocab at 0x22744b5b1d0>,
'•': <gensim.models.keyedvectors.Vocab at 0x22744b5b208>,
'UK': <gensim.models.keyedvectors.Vocab at 0x22744b5b278>,
'force': <gensim.models.keyedvectors.Vocab at 0x22744b5b2e8>,
'student': <gensim.models.keyedvectors.Vocab at 0x22744b5b358>,
'idea': <gensim.models.keyedvectors.Vocab at 0x22744b5b3c8>,
'reached': <gensim.models.keyedvectors.Vocab at 0x22744b5b438>,
'reason': <gensim.models.keyedvectors.Vocab at 0x22744b5b4a8>,
'levels': <gensim.models.keyedvectors.Vocab at 0x22744b5b518>,
'space': <gensim.models.keyedvectors.Vocab at 0x22744b5b588>,
'competition': <gensim.models.keyedvectors.Vocab at 0x22744b5b5c0>,
'forces': <gensim.models.keyedvectors.Vocab at 0x22744b5b630>,
'sector': <gensim.models.keyedvectors.Vocab at 0x22744b5b6a0>,
'Last': <gensim.models.keyedvectors.Vocab at 0x22744b5b710>,
'tried': <gensim.models.keyedvectors.Vocab at 0x22744b5b780>,
'common': <gensim.models.keyedvectors.Vocab at 0x22744b5b7f0>,
'homes': <gensim.models.keyedvectors.Vocab at 0x22744b5b860>,
'stage': <gensim.models.keyedvectors.Vocab at 0x22744b5b8d0>,
'department': <gensim.models.keyedvectors.Vocab at 0x22744b5b908>,
'named': <gensim.models.keyedvectors.Vocab at 0x22744b5b978>,
'earnings': <gensim.models.keyedvectors.Vocab at 0x22744b5b9b0>,
'offers': <gensim.models.keyedvectors.Vocab at 0x22744b5ba20>,
'star': <gensim.models.keyedvectors.Vocab at 0x22744b5ba90>,
'certain': <gensim.models.keyedvectors.Vocab at 0x22744b5bb00>,
'double': <gensim.models.keyedvectors.Vocab at 0x22744b5bb70>,
'longer': <gensim.models.keyedvectors.Vocab at 0x22744b5bbe0>,
'followed': <gensim.models.keyedvectors.Vocab at 0x22744b5bc18>,
'cause': <gensim.models.keyedvectors.Vocab at 0x22744b5bc88>,
'Association': <gensim.models.keyedvectors.Vocab at 0x22744b5bcc0>,
'signed': <gensim.models.keyedvectors.Vocab at 0x22744b5bd30>,
'committee': <gensim.models.keyedvectors.Vocab at 0x22744b5bd68>,
'hour': <gensim.models.keyedvectors.Vocab at 0x22744b5bdd8>,
'college': <gensim.models.keyedvectors.Vocab at 0x22744b5be48>,
'Pakistan': <gensim.models.keyedvectors.Vocab at 0x22744b5be80>,
'users': <gensim.models.keyedvectors.Vocab at 0x22744b5bef0>,
'Iran': <gensim.models.keyedvectors.Vocab at 0x22744b5bf60>,
'sign': <gensim.models.keyedvectors.Vocab at 0x22744b5bfd0>,
'living': <gensim.models.keyedvectors.Vocab at 0x22744b5d080>,
'failed': <gensim.models.keyedvectors.Vocab at 0x22744b5d0f0>,
'reach': <gensim.models.keyedvectors.Vocab at 0x22744b5d160>,
'quickly': <gensim.models.keyedvectors.Vocab at 0x22744b5d1d0>,
'receive': <gensim.models.keyedvectors.Vocab at 0x22744b5d240>,
'debt': <gensim.models.keyedvectors.Vocab at 0x22744b5d2b0>,
'sale': <gensim.models.keyedvectors.Vocab at 0x22744b5d320>,
'Board': <gensim.models.keyedvectors.Vocab at 0x22744b5d390>,
'Americans': <gensim.models.keyedvectors.Vocab at 0x22744b5d3c8>,
'Road': <gensim.models.keyedvectors.Vocab at 0x22744b5d438>,
'Brown': <gensim.models.keyedvectors.Vocab at 0x22744b5d4a8>,
'insurance': <gensim.models.keyedvectors.Vocab at 0x22744b5d4e0>,
'##:##': <gensim.models.keyedvectors.Vocab at 0x22744b5d550>,
'anyone': <gensim.models.keyedvectors.Vocab at 0x22744b5d5c0>,
'tournament': <gensim.models.keyedvectors.Vocab at 0x22744b5d5f8>,
'More': <gensim.models.keyedvectors.Vocab at 0x22744b5d668>,
'gas': <gensim.models.keyedvectors.Vocab at 0x22744b5d6d8>,
'talks': <gensim.models.keyedvectors.Vocab at 0x22744b5d748>,
'serious': <gensim.models.keyedvectors.Vocab at 0x22744b5d7b8>,
'required': <gensim.models.keyedvectors.Vocab at 0x22744b5d7f0>,
'sell': <gensim.models.keyedvectors.Vocab at 0x22744b5d860>,
'construction': <gensim.models.keyedvectors.Vocab at 0x22744b5d898>,
'evidence': <gensim.models.keyedvectors.Vocab at 0x22744b5d8d0>,
'remains': <gensim.models.keyedvectors.Vocab at 0x22744b5d940>,
'black': <gensim.models.keyedvectors.Vocab at 0x22744b5d9b0>,
'below': <gensim.models.keyedvectors.Vocab at 0x22744b5da20>,
'improve': <gensim.models.keyedvectors.Vocab at 0x22744b5da90>,
'crisis': <gensim.models.keyedvectors.Vocab at 0x22744b5db00>,
'address': <gensim.models.keyedvectors.Vocab at 0x22744b5db70>,
'questions': <gensim.models.keyedvectors.Vocab at 0x22744b5dba8>,
'easy': <gensim.models.keyedvectors.Vocab at 0x22744b5dc18>,
'begin': <gensim.models.keyedvectors.Vocab at 0x22744b5dc88>,
'view': <gensim.models.keyedvectors.Vocab at 0x22744b5dcf8>,
'School': <gensim.models.keyedvectors.Vocab at 0x22744b5dd68>,
'heard': <gensim.models.keyedvectors.Vocab at 0x22744b5ddd8>,
'executive': <gensim.models.keyedvectors.Vocab at 0x22744b5de10>,
'raised': <gensim.models.keyedvectors.Vocab at 0x22744b5de80>,
...}
In [7]:
from gensim.models.word2vec import Word2Vec
num_features = 300
min_word_count = 3
num_workers = 2
window_size = 6
subsampling = 1e-3
In [10]:
token_list = [
['to', 'provide', 'early', 'intervention/early', 'childhood', 'special',
'education', 'services', 'to', 'eligible', 'children', 'and', 'their',
'families'],
['essential', 'job', 'functions'],
['participate', 'as', 'a', 'transdisciplinary', 'team', 'member', 'to',
'complete', 'educational', 'assessments', 'for']
]
In [12]:
model = Word2Vec(token_list, workers=num_workers, size=num_features,
min_count=min_word_count, window=window_size, sample=subsampling)
model.init_sims(replace=True)
# model_name = "custom_word2vec_v1"
# model.save(model_name)
INFO:gensim.models.word2vec:collecting all words and their counts
INFO:gensim.models.word2vec:PROGRESS: at sentence #0, processed 0 words, keeping 0 word types
INFO:gensim.models.word2vec:collected 26 word types from a corpus of 28 raw words and 3 sentences
INFO:gensim.models.word2vec:Loading a fresh vocabulary
INFO:gensim.models.word2vec:effective_min_count=3 retains 1 unique words (3% of original 26, drops 25)
INFO:gensim.models.word2vec:effective_min_count=3 leaves 3 word corpus (10% of original 28, drops 25)
INFO:gensim.models.word2vec:deleting the raw counts dictionary of 26 items
INFO:gensim.models.word2vec:sample=0.001 downsamples 1 most-common words
INFO:gensim.models.word2vec:downsampling leaves estimated 0 word corpus (3.3% of prior 3)
INFO:gensim.models.base_any2vec:estimated required memory for 1 words and 300 dimensions: 2900 bytes
INFO:gensim.models.word2vec:resetting layer weights
INFO:gensim.models.base_any2vec:training model with 2 workers on 1 vocabulary and 300 features, using sg=0 hs=0 sample=0.001 negative=5 window=6
INFO:gensim.models.base_any2vec:worker thread finished; awaiting finish of 1 more threads
INFO:gensim.models.base_any2vec:worker thread finished; awaiting finish of 0 more threads
INFO:gensim.models.base_any2vec:EPOCH - 1 : training on 28 raw words (0 effective words) took 0.0s, 0 effective words/s
INFO:gensim.models.base_any2vec:worker thread finished; awaiting finish of 1 more threads
INFO:gensim.models.base_any2vec:worker thread finished; awaiting finish of 0 more threads
INFO:gensim.models.base_any2vec:EPOCH - 2 : training on 28 raw words (0 effective words) took 0.0s, 0 effective words/s
INFO:gensim.models.base_any2vec:worker thread finished; awaiting finish of 1 more threads
INFO:gensim.models.base_any2vec:worker thread finished; awaiting finish of 0 more threads
INFO:gensim.models.base_any2vec:EPOCH - 3 : training on 28 raw words (1 effective words) took 0.0s, 706 effective words/s
INFO:gensim.models.base_any2vec:worker thread finished; awaiting finish of 1 more threads
INFO:gensim.models.base_any2vec:worker thread finished; awaiting finish of 0 more threads
INFO:gensim.models.base_any2vec:EPOCH - 4 : training on 28 raw words (0 effective words) took 0.0s, 0 effective words/s
INFO:gensim.models.base_any2vec:worker thread finished; awaiting finish of 1 more threads
INFO:gensim.models.base_any2vec:worker thread finished; awaiting finish of 0 more threads
INFO:gensim.models.base_any2vec:EPOCH - 5 : training on 28 raw words (0 effective words) took 0.0s, 0 effective words/s
INFO:gensim.models.base_any2vec:training on a 140 raw words (1 effective words) took 0.0s, 49 effective words/s
WARNING:gensim.models.base_any2vec:under 10 jobs per worker: consider setting a smaller `batch_words' for smoother alpha decay
INFO:gensim.models.keyedvectors:precomputing L2-norms of word weight vectors
In [35]:
model.wv.vocab
Out[35]:
{'to': <gensim.models.keyedvectors.Vocab at 0x22740522a58>}
In [ ]:
Content source: mcamack/Jupyter-Notebooks
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