def build_model(nr_class, width, depth, conv_depth, **kwargs):
with Model.define_operators({"|": concatenate, ">>": chain, "**": clone}):
embed = (HashEmbed(width, 5000, column=1)
| StaticVectors("spacy_pretrained_vectors", width, column=5)
| HashEmbed(width // 2, 750, column=2)
| HashEmbed(width // 2, 750, column=3)
| HashEmbed(width // 2, 750, column=4)) >> LN(Maxout(width))
sent2vec = (flatten_add_lengths >> with_getitem(
0,
embed >> Residual(ExtractWindow(nW=1) >> LN(Maxout(width)))**
conv_depth,
) >> ParametricAttention(width) >> Pooling(sum_pool) >> Residual(
LN(Maxout(width)))**depth)
model = (
foreach(sent2vec, drop_factor=2.0) >> flatten_add_lengths
# This block would allow the model to learn some cross-sentence
# features. It's not useful on this problem. It might make more
# sense to use a BiLSTM here, following Liang et al (2016).
# >> with_getitem(0,
# Residual(ExtractWindow(nW=1) >> LN(Maxout(width))) ** conv_depth
# )
>> ParametricAttention(width, hard=False) >> Pooling(sum_pool) >>
Residual(LN(Maxout(width)))**depth >> Softmax(nr_class))
model.lsuv = False
return model
def build_model(nr_class, width, depth, conv_depth, **kwargs):
with Model.define_operators({'|': concatenate, '>>': chain, '**': clone}):
embed = ((HashEmbed(width, 5000, column=1)
| StaticVectors('spacy_pretrained_vectors', width, column=5)
| HashEmbed(width // 2, 750, column=2)
| HashEmbed(width // 2, 750, column=3)
| HashEmbed(width // 2, 750, column=4)) >> LN(Maxout(width)))
sent2vec = (flatten_add_lengths >> with_getitem(
0, embed >> Residual(ExtractWindow(nW=1) >> LN(Maxout(width)))**
conv_depth) >> ParametricAttention(width) >> Pooling(sum_pool) >>
Residual(LN(Maxout(width)))**depth)
model = (foreach(sent2vec, drop_factor=2.0) >> flatten_add_lengths >>
ParametricAttention(width, hard=False) >> Pooling(sum_pool) >>
Residual(LN(Maxout(width)))**depth >> Softmax(nr_class))
model.lsuv = False
return model
def build_model(nr_class, width, depth, conv_depth, **kwargs):
with Model.define_operators({'|': concatenate, '>>': chain, '**': clone}):
embed = (
(HashEmbed(width, 5000, column=1)
| StaticVectors('spacy_pretrained_vectors', width, column=5)
| HashEmbed(width//2, 750, column=2)
| HashEmbed(width//2, 750, column=3)
| HashEmbed(width//2, 750, column=4))
>> LN(Maxout(width))
)
sent2vec = (
flatten_add_lengths
>> with_getitem(0,
embed
>> Residual(ExtractWindow(nW=1) >> LN(Maxout(width))) ** conv_depth
)
>> ParametricAttention(width)
>> Pooling(sum_pool)
>> Residual(LN(Maxout(width))) ** depth
)
model = (
foreach(sent2vec, drop_factor=2.0)
>> flatten_add_lengths
# This block would allow the model to learn some cross-sentence
# features. It's not useful on this problem. It might make more
# sense to use a BiLSTM here, following Liang et al (2016).
#>> with_getitem(0,
# Residual(ExtractWindow(nW=1) >> LN(Maxout(width))) ** conv_depth
#)
>> ParametricAttention(width, hard=False)
>> Pooling(sum_pool)
>> Residual(LN(Maxout(width))) ** depth
>> Softmax(nr_class)
)
model.lsuv = False
return model
def build_model(nr_class, width, depth, conv_depth, vectors_name, **kwargs):
with Model.define_operators({"|": concatenate, ">>": chain, "**": clone}):
embed = (HashEmbed(width, 5000, column=1)
| StaticVectors(vectors_name, width, column=5)
| HashEmbed(width // 2, 750, column=2)
| HashEmbed(width // 2, 750, column=3)
| HashEmbed(width // 2, 750, column=4)) >> LN(Maxout(width))
sent2vec = (with_flatten(embed) >> Residual(
prepare_self_attention(Affine(width * 3, width), nM=width, nH=4) >>
MultiHeadedAttention() >> with_flatten(
Maxout(width, width, pieces=3))) >> flatten_add_lengths >>
ParametricAttention(width, hard=False) >>
Pooling(mean_pool) >> Residual(LN(Maxout(width))))
model = (foreach(sent2vec, drop_factor=2.0) >> Residual(
prepare_self_attention(Affine(width * 3, width), nM=width, nH=4) >>
MultiHeadedAttention() >> with_flatten(LN(Affine(width, width))))
>> flatten_add_lengths >> ParametricAttention(
width, hard=False) >> Pooling(mean_pool) >> Residual(
LN(Maxout(width)))**2 >> Softmax(nr_class))
model.lsuv = False
return model