Update NER config. Getting 84.8
@@ -13,24 +13,25 @@ dropout = 0.1
# Controls early-stopping. 0 or -1 mean unlimited.
patience = 100000
max_epochs = 0
-max_steps = 100000
-eval_frequency = 2000
+max_steps = 0
+eval_frequency = 1000
# Other settings
seed = 0
-accumulate_gradient = 1
+accumulate_gradient = 2
use_pytorch_for_gpu_memory = false
# Control how scores are printed and checkpoints are evaluated.
scores = ["speed", "ents_p", "ents_r", "ents_f"]
score_weights = {"ents_f": 1.0}
# These settings are invalid for the transformer models.
init_tok2vec = null
-discard_oversize = false
+discard_oversize = true
omit_extra_lookups = false
+batch_by_words = true
[training.batch_size]
@schedules = "compounding.v1"
-start = 100
-stop = 2000
+start = 1000
+stop = 1000
compound = 1.001
[training.optimizer]
@@ -38,7 +39,7 @@ compound = 1.001
beta1 = 0.9
beta2 = 0.999
L2_is_weight_decay = true
-L2 = 0.0
+L2 = 0.01
grad_clip = 1.0
use_averages = true
eps = 1e-8
@@ -64,15 +65,15 @@ min_action_freq = 1
nr_feature_tokens = 3
hidden_width = 64
maxout_pieces = 2
-use_upper = false
+use_upper = true
[nlp.pipeline.ner.model.tok2vec]
@architectures = "spacy.HashEmbedCNN.v1"
pretrained_vectors = ${nlp:vectors}
-width = 300
+width = 96
depth = 4
window_size = 1
-embed_size = 7000
+embed_size = 2000
maxout_pieces = 1
subword_features = true
dropout = ${training:dropout}
