mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-13 13:17:06 +03:00
7f5715a081
* setting KB in the EL constructor, similar to how the model is passed on * removing wikipedia example files - moved to projects * throw an error when nlp.update is called with 2 positional arguments * rewriting the config logic in create pipe to accomodate for other objects (e.g. KB) in the config * update config files with new parameters * avoid training pipeline components that don't have a model (like sentencizer) * various small fixes + UX improvements * small fixes * set thinc to 8.0.0a9 everywhere * remove outdated comment
68 lines
1.3 KiB
INI
68 lines
1.3 KiB
INI
[training]
|
|
patience = 10000
|
|
eval_frequency = 200
|
|
dropout = 0.2
|
|
init_tok2vec = null
|
|
vectors = null
|
|
max_epochs = 100
|
|
orth_variant_level = 0.0
|
|
gold_preproc = true
|
|
max_length = 0
|
|
use_gpu = 0
|
|
scores = ["tags_acc", "uas", "las"]
|
|
score_weights = {"las": 0.8, "tags_acc": 0.2}
|
|
limit = 0
|
|
seed = 0
|
|
accumulate_gradient = 2
|
|
|
|
[training.batch_size]
|
|
@schedules = "compounding.v1"
|
|
start = 100
|
|
stop = 1000
|
|
compound = 1.001
|
|
|
|
[optimizer]
|
|
@optimizers = "Adam.v1"
|
|
learn_rate = 0.001
|
|
beta1 = 0.9
|
|
beta2 = 0.999
|
|
|
|
[nlp]
|
|
lang = "en"
|
|
vectors = ${training:vectors}
|
|
|
|
[nlp.pipeline.tok2vec]
|
|
factory = "tok2vec"
|
|
|
|
[nlp.pipeline.tagger]
|
|
factory = "tagger"
|
|
|
|
[nlp.pipeline.parser]
|
|
factory = "parser"
|
|
|
|
[nlp.pipeline.tagger.model]
|
|
@architectures = "spacy.Tagger.v1"
|
|
|
|
[nlp.pipeline.tagger.model.tok2vec]
|
|
@architectures = "spacy.Tok2VecTensors.v1"
|
|
width = ${nlp.pipeline.tok2vec.model:width}
|
|
|
|
[nlp.pipeline.parser.model]
|
|
@architectures = "spacy.TransitionBasedParser.v1"
|
|
nr_feature_tokens = 8
|
|
hidden_width = 64
|
|
maxout_pieces = 3
|
|
|
|
[nlp.pipeline.parser.model.tok2vec]
|
|
@architectures = "spacy.Tok2VecTensors.v1"
|
|
width = ${nlp.pipeline.tok2vec.model:width}
|
|
|
|
[nlp.pipeline.tok2vec.model]
|
|
@architectures = "spacy.HashEmbedBiLSTM.v1"
|
|
pretrained_vectors = ${nlp:vectors}
|
|
width = 96
|
|
depth = 4
|
|
embed_size = 2000
|
|
subword_features = true
|
|
maxout_pieces = 3
|