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Update NER config. Getting 84.8

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This commit is contained in:
Matthw Honnibal 2020-07-08 21:38:01 +02:00
parent 1b20ffac38
commit 9b49787f35
1 changed files with 11 additions and 10 deletions
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21
examples/experiments/onto-ner.cfg
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@ -13,24 +13,25 @@ dropout = 0.1
# Controls early-stopping. 0 or -1 mean unlimited. # Controls early-stopping. 0 or -1 mean unlimited.
patience = 100000 patience = 100000
max_epochs = 0 max_epochs = 0
max_steps = 100000 max_steps = 0
eval_frequency = 2000 eval_frequency = 1000
# Other settings # Other settings
seed = 0 seed = 0
accumulate_gradient = 1 accumulate_gradient = 2
use_pytorch_for_gpu_memory = false use_pytorch_for_gpu_memory = false
# Control how scores are printed and checkpoints are evaluated. # Control how scores are printed and checkpoints are evaluated.
scores = ["speed", "ents_p", "ents_r", "ents_f"] scores = ["speed", "ents_p", "ents_r", "ents_f"]
score_weights = {"ents_f": 1.0} score_weights = {"ents_f": 1.0}
# These settings are invalid for the transformer models. # These settings are invalid for the transformer models.
init_tok2vec = null init_tok2vec = null
discard_oversize = false discard_oversize = true
omit_extra_lookups = false omit_extra_lookups = false
batch_by_words = true
[training.batch_size] [training.batch_size]
@schedules = "compounding.v1" @schedules = "compounding.v1"
start = 100 start = 1000
stop = 2000 stop = 1000
compound = 1.001 compound = 1.001
[training.optimizer] [training.optimizer]
@ -38,7 +39,7 @@ compound = 1.001
beta1 = 0.9 beta1 = 0.9
beta2 = 0.999 beta2 = 0.999
L2_is_weight_decay = true L2_is_weight_decay = true
L2 = 0.0 L2 = 0.01
grad_clip = 1.0 grad_clip = 1.0
use_averages = true use_averages = true
eps = 1e-8 eps = 1e-8
@ -64,15 +65,15 @@ min_action_freq = 1
nr_feature_tokens = 3 nr_feature_tokens = 3
hidden_width = 64 hidden_width = 64
maxout_pieces = 2 maxout_pieces = 2
use_upper = false use_upper = true
[nlp.pipeline.ner.model.tok2vec] [nlp.pipeline.ner.model.tok2vec]
@architectures = "spacy.HashEmbedCNN.v1" @architectures = "spacy.HashEmbedCNN.v1"
pretrained_vectors = ${nlp:vectors} pretrained_vectors = ${nlp:vectors}
width = 300 width = 96
depth = 4 depth = 4
window_size = 1 window_size = 1
embed_size = 7000 embed_size = 2000
maxout_pieces = 1 maxout_pieces = 1
subword_features = true subword_features = true
dropout = ${training:dropout} dropout = ${training:dropout}