INTUIA/Programa final/spacy/tests/training/__pycache__/test_pretraining.cpython-312.pyc

Ë
?û gž0ãó€—dd
lm
Z

ddlZddlZddlZddlmZmZ
ddl	m
Z

ddlmZ
ddl
mZmZ
ddlmZ
ddlmZmZ
dd	lmZ
dd
lmZ
ddlmZ
ddlmZ
dd
lmZ
ddl
mZ
dZ dZ!dZ"iddi
dddddœgZ#dddddœdddddœgZ$d„Z%ejLjOd e#«ejLjOd!d"«d#„««Z(ejLjOd e$«d$„«Z)ejLjOd e$«d%„«Z*ejLjOd&e!e g«d'„«Z+d(„Z,d)„Z-d*„Z.d+„Z/d,„Z0d-„Z1y).é)
ÚPathN)ÚConfigÚget_current_ops)
Úutil)
ÚEnglish)ÚDEFAULT_CONFIG_PATHÚDEFAULT_CONFIG_PRETRAIN_PATH)
Úcreate_pretrain_vectors)ÚDocÚDocBin)
Úinit_nlp)
Útrain)
Úpretrain)
ÚVectors)
ÚVocabé)
Úmake_tempdiraE
[nlp]
lang = "en"
pipeline = ["tok2vec", "tagger"]

[components]

[components.tok2vec]
factory = "tok2vec"

[components.tok2vec.model]
@architectures = "spacy.HashEmbedCNN.v1"
pretrained_vectors = null
width = 342
depth = 4
window_size = 1
embed_size = 2000
maxout_pieces = 3
subword_features = true

[components.tagger]
factory = "tagger"

[components.tagger.model]
@architectures = "spacy.Tagger.v2"

[components.tagger.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.width}

[pretraining]
max_epochs = 5

[training]
max_epochs = 5
až

[nlp]
lang = "en"
pipeline = ["tagger"]

[components]

[components.tagger]
factory = "tagger"

[components.tagger.model]
@architectures = "spacy.Tagger.v2"

[components.tagger.model.tok2vec]
@architectures = "spacy.HashEmbedCNN.v1"
pretrained_vectors = null
width = 342
depth = 4
window_size = 1
embed_size = 2000
maxout_pieces = 3
subword_features = true

[pretraining]
max_epochs = 5

[training]
max_epochs = 5
aº
[nlp]
lang = "en"
pipeline = ["tok2vec", "tagger"]

[components]

[components.tok2vec]
factory = "tok2vec"

[components.tok2vec.model]
@architectures = "spacy.HashEmbedCNN.v1"
pretrained_vectors = null
width = 342
depth = 4
window_size = 1
embed_size = 2000
maxout_pieces = 3
subword_features = true

[components.tagger]
factory = "tagger"

[components.tagger.model]
@architectures = "spacy.Tagger.v2"

[components.tagger.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.width}

[pretraining]
max_epochs = 5

[pretraining.objective]
@architectures = spacy.PretrainVectors.v1
maxout_pieces = 3
hidden_size = 300
loss = cosine

[training]
max_epochs = 5
ú@architectureszspacy.PretrainCharacters.v1éé*)rÚ
maxout_piecesÚhidden_sizeÚn_characterszspacy.PretrainVectors.v1éé,
Úcosine)rrrÚlosséÈÚL2cóø—t
«jt«
}tj|d
d¬«}
|
j
}tjt«
}|j|«
}d|dddvsJ‚
y)	z7Test that pretraining defaults to a character objectiveTF©Ú	auto_fillÚvalidateÚPretrainCharactersÚpretrainingÚ	objectiverN)	rÚfrom_strÚpretrain_string_internalrÚload_model_from_configÚconfigÚload_configr	Úmerge)r*ÚnlpÚfilledÚpretrain_configs    úfC:\Users\garci\AppData\Roaming\Python\Python312\site-packages\spacy/tests/training/test_pretraining.pyÚtest_pretraining_defaultr1Ÿsq€ä
‹X×
Ñ
Ô7Ó
8€FÜ
×
%Ñ
% f¸ÀuÔ
M€CØ
�Z‰Z€FÜ×&Ñ&Ô'CÓD€OØ
×
"Ñ
" 6Ó
*€FØ 6¨-Ñ#8¸Ñ#EÐFVÑ#WÑWÐWÑWór&Ú	skip_last)TFcóÔ—t
«jt«
}||d
d<tj|dd¬«}|j
}tjt«
}|j|«
}t«5}t|«
}||dd<|j«}|d
dd	k(sJ‚
t|||
¬
«
t|dz«
j«sJ‚
t|dz«
j«sJ‚
t|d
z«
j«rJ‚
|
rt|dz«
j«r J‚
t|dz«
j«sJ‚
ddd«
y#1s
w
Y

yxYw
)z8Test that pretraining works with the character objectiver%r&TFr!ÚpathsÚraw_textÚ	componentÚtok2vec)
r3ú
model0.binú
model4.binú
model5.binúmodel-last.binN©rr'Úpretrain_string_listenerrr)r*r+r	r,rÚwrite_sample_jsonlÚinterpolaterrÚexists)r&r3r*r-r.r/Útmp_dirÚ	file_paths        r0Ú#test_pretraining_tok2vec_charactersrD©sP
€ô‹X×
Ñ
Ô7Ó
8€FØ)2€Fˆ=ј+Ñ&Ü
×
%Ñ
% f¸ÀuÔ
M€CØ
�Z‰Z€FÜ×&Ñ&Ô'CÓD€OØ
×
"Ñ
" 6Ó
*€FÜ	Œ˜7Ü& wÓ/ˆ	Ø&/ˆˆw‰˜
Ñ#Ø×#Ñ#Ó%ˆØ�mÑ$ [Ñ1°YÒ>Ð>Ð>Ü�˜¨IÕ6Ü�G˜lÑ*Ó+×2Ñ2Ô4Ð4Ð4Ü�G˜lÑ*Ó+×2Ñ2Ô4Ð4Ð4ܘ ,Ñ.Ó/×6Ñ6Ô8Ð8Ð8ÙܘGÐ&6Ñ6Ó7×>Ñ>Ô@Ð@Ð@ä˜Ð"2Ñ2Ó3×:Ñ:Ô<Ð<Ð<÷
�‰ús
Á>CEÅE'c
óö
—t
«jt«
}
||
d
d<tj|
dd¬«}|j
}tjt«
}|j|«
}t«5}t|«
}||dd<|j«}|dd	�J‚
tjt«
5
t||«
d
d
d
«
d
d
d
«
y
#1s
w
Y

ŒxYw
#1s
w
Y

y
xYw
)z]Test that pretraining doesn't works with the vectors objective if there are no static vectorsr%r&TFr!r5r6Ú
initializeÚvectorsN)rr'r>rr)r*r+r	r,rr?r@ÚpytestÚraisesÚ
ValueErrorr)r&r*r-r.r/rBrCs       r0Ú%test_pretraining_tok2vec_vectors_failrKÂsÚ€ô‹X×
Ñ
Ô7Ó
8€FØ)2€Fˆ=ј+Ñ&Ü
×
%Ñ
% f¸ÀuÔ
M€CØ
�Z‰Z€FÜ×&Ñ&Ô'CÓD€OØ
×
"Ñ
" 6Ó
*€FÜ	Œ˜7Ü& wÓ/ˆ	Ø&/ˆˆw‰˜
Ñ#Ø×#Ñ#Ó%ˆØ�lÑ# IÑ.Ð6Ð6Ð6Ü
�]‰]œ:Õ
&Ü�V˜WÔ%÷'÷
ˆ÷
'Ð
&ú÷
ˆús%Á>AC/Ã
C#ÃC/Ã#C,	Ã(C/Ã/C8c
ó¬
—t
«jt«
}
||
d
d<tj|
dd¬«}|j
}tjt«
}|j|«
}t«5}t|«
}||dd<t|«
}||dd	<|j«}t||«
d
d
d
«
y
#1s
w
Y

y
xYw
)zQTest that pretraining works with the vectors objective and static vectors definedr%r&TFr!r5r6rFrGN)rr'r>rr)r*r+r	r,rr?Úwrite_vectors_modelr@r)r&r*r-r.r/rBrCÚnlp_paths        r0Ú test_pretraining_tok2vec_vectorsrOÔs¾€ô‹X×
Ñ
Ô7Ó
8€FØ)2€Fˆ=ј+Ñ&Ü
×
%Ñ
% f¸ÀuÔ
M€CØ
�Z‰Z€FÜ×&Ñ&Ô'CÓD€OØ
×
"Ñ
" 6Ó
*€FÜ	Œ˜7Ü& wÓ/ˆ	Ø&/ˆˆw‰˜
Ñ#Ü& wÓ/ˆØ*2ˆˆ|јYÑ'Ø×#Ñ#Ó%ˆÜ�˜Ô!÷

�‰ús
Á>AC
Ã
Cr*c
óˆ—t
«jt«
}tj|d
d¬«}
|
j
}tjt«
}|j|«
}t«5}t|«
}||dd<d|dd<d	|dd
<|j«}t||«
t|dz«
j«sJ‚
t|dz«
j«sJ‚
t|d
z«
j«sJ‚
t|dz«
j«rJ‚
	ddd«
y#1s
w
Y

yxYw
)z?Test pretraining of the tagger's tok2vec layer (via a listener)TFr!r5r6Útaggerr%r7r8Úlayerr9r:r<r;Nr=©r*r-r.r/rBrCs      r0Útest_pretraining_tagger_tok2vecrTæs(
€ô‹X×
Ñ
Ô7Ó
8€FÜ
×
%Ñ
% f¸ÀuÔ
M€CØ
�Z‰Z€FÜ×&Ñ&Ô'CÓD€OØ
×