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spaCy/examples/experiments/tok2vec-ner/charembed_tok2vec.cfg

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Tok2Vec: extract-embed-encode (#5102) * avoid changing original config * fix elif structure, batch with just int crashes otherwise * tok2vec example with doc2feats, encode and embed architectures * further clean up MultiHashEmbed * further generalize Tok2Vec to work with extract-embed-encode parts * avoid initializing the charembed layer with Docs (for now ?) * small fixes for bilstm config (still does not run) * rename to core layer * move new configs * walk model to set nI instead of using core ref * fix senter overfitting test to be more similar to the training data (avoid flakey behaviour)
2020-03-08 15:23:18 +03:00
[training]
use_gpu = -1
limit = 0
dropout = 0.2
patience = 10000
eval_frequency = 200
scores = ["ents_f"]
score_weights = {"ents_f": 1}
orth_variant_level = 0.0
gold_preproc = true
max_length = 0
batch_size = 25
Various fixes to NEL functionality, Example class etc (#5460) * 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(like sentencizer) * various small fixes + UX improvements * small fixes * set thinc to 8.0.0a9 everywhere * remove outdated comment
2020-05-20 12:41:12 +03:00
seed = 0
accumulate_gradient = 2
add discard_oversize parameter, move optimizer to training subsection
2020-06-03 11:04:16 +03:00
discard_oversize = false
Tok2Vec: extract-embed-encode (#5102) * avoid changing original config * fix elif structure, batch with just int crashes otherwise * tok2vec example with doc2feats, encode and embed architectures * further clean up MultiHashEmbed * further generalize Tok2Vec to work with extract-embed-encode parts * avoid initializing the charembed layer with Docs (for now ?) * small fixes for bilstm config (still does not run) * rename to core layer * move new configs * walk model to set nI instead of using core ref * fix senter overfitting test to be more similar to the training data (avoid flakey behaviour)
2020-03-08 15:23:18 +03:00
add discard_oversize parameter, move optimizer to training subsection
2020-06-03 11:04:16 +03:00
[training.optimizer]
Tok2Vec: extract-embed-encode (#5102) * avoid changing original config * fix elif structure, batch with just int crashes otherwise * tok2vec example with doc2feats, encode and embed architectures * further clean up MultiHashEmbed * further generalize Tok2Vec to work with extract-embed-encode parts * avoid initializing the charembed layer with Docs (for now ?) * small fixes for bilstm config (still does not run) * rename to core layer * move new configs * walk model to set nI instead of using core ref * fix senter overfitting test to be more similar to the training data (avoid flakey behaviour)
2020-03-08 15:23:18 +03:00
@optimizers = "Adam.v1"
learn_rate = 0.001
beta1 = 0.9
beta2 = 0.999
[nlp]
lang = "en"
vectors = null
[nlp.pipeline.tok2vec]
factory = "tok2vec"
[nlp.pipeline.tok2vec.model]
@architectures = "spacy.Tok2Vec.v1"
[nlp.pipeline.tok2vec.model.extract]
@architectures = "spacy.CharacterEmbed.v1"
width = 96
nM = 64
nC = 8
rows = 2000
columns = ["ID", "NORM", "PREFIX", "SUFFIX", "SHAPE", "ORTH"]
[nlp.pipeline.tok2vec.model.extract.features]
@architectures = "spacy.Doc2Feats.v1"
columns = ${nlp.pipeline.tok2vec.model.extract:columns}
[nlp.pipeline.tok2vec.model.embed]
@architectures = "spacy.LayerNormalizedMaxout.v1"
width = ${nlp.pipeline.tok2vec.model.extract:width}
maxout_pieces = 4
[nlp.pipeline.tok2vec.model.encode]
@architectures = "spacy.MaxoutWindowEncoder.v1"
width = ${nlp.pipeline.tok2vec.model.extract:width}
window_size = 1
maxout_pieces = 2
depth = 2
[nlp.pipeline.ner]
factory = "ner"
[nlp.pipeline.ner.model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 6
hidden_width = 64
maxout_pieces = 2
[nlp.pipeline.ner.model.tok2vec]
@architectures = "spacy.Tok2VecTensors.v1"
width = ${nlp.pipeline.tok2vec.model.extract:width}
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