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Update onto-json default

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This commit is contained in:
Matthew Honnibal 2020-06-24 00:00:59 +02:00
parent 306a591e1f
commit 5120113f39
1 changed files with 7 additions and 16 deletions
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23
examples/experiments/onto-joint/defaults.cfg
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@ -5,7 +5,7 @@
# data is passed in sentence-by-sentence via some prior preprocessing. # data is passed in sentence-by-sentence via some prior preprocessing.
gold_preproc = false gold_preproc = false
# Limitations on training document length or number of examples. # Limitations on training document length or number of examples.
max_length = 0 max_length = 5000
limit = 0 limit = 0
# Data augmentation # Data augmentation
orth_variant_level = 0.0 orth_variant_level = 0.0
@ -14,7 +14,7 @@ dropout = 0.1
patience = 1600 patience = 1600
max_epochs = 0 max_epochs = 0
max_steps = 20000 max_steps = 20000
eval_frequency = 400 eval_frequency = 50
# Other settings # Other settings
seed = 0 seed = 0
accumulate_gradient = 1 accumulate_gradient = 1
@ -57,8 +57,6 @@ vectors = null
[nlp.pipeline.tok2vec] [nlp.pipeline.tok2vec]
factory = "tok2vec" factory = "tok2vec"
[nlp.pipeline.senter]
factory = "senter"
[nlp.pipeline.ner] [nlp.pipeline.ner]
factory = "ner" factory = "ner"
@ -73,17 +71,10 @@ factory = "tagger"
[nlp.pipeline.parser] [nlp.pipeline.parser]
factory = "parser" factory = "parser"
learn_tokens = false learn_tokens = false
min_action_freq = 1 min_action_freq = 30
beam_width = 1 beam_width = 1
beam_update_prob = 1.0 beam_update_prob = 1.0
[nlp.pipeline.senter.model]
@architectures = "spacy.Tagger.v1"
[nlp.pipeline.senter.model.tok2vec]
@architectures = "spacy.Tok2VecTensors.v1"
width = ${nlp.pipeline.tok2vec.model:width}
[nlp.pipeline.tagger.model] [nlp.pipeline.tagger.model]
@architectures = "spacy.Tagger.v1" @architectures = "spacy.Tagger.v1"
@ -116,10 +107,10 @@ width = ${nlp.pipeline.tok2vec.model:width}
[nlp.pipeline.tok2vec.model] [nlp.pipeline.tok2vec.model]
@architectures = "spacy.HashEmbedCNN.v1" @architectures = "spacy.HashEmbedCNN.v1"
pretrained_vectors = ${nlp:vectors} pretrained_vectors = ${nlp:vectors}
width = 256 width = 128
depth = 6 depth = 4
window_size = 1 window_size = 1
embed_size = 10000 embed_size = 7000
maxout_pieces = 3 maxout_pieces = 3
subword_features = true subword_features = true
dropout = null dropout = ${training:dropout}