Update NER config
# data is passed in sentence-by-sentence via some prior preprocessing.
gold_preproc = false
# Limitations on training document length or number of examples.
-max_length = 5000
+max_length = 3000
limit = 0
# Data augmentation
orth_variant_level = 0.0
@@ -17,20 +17,20 @@ max_steps = 0
eval_frequency = 1000
# Other settings
seed = 0
-accumulate_gradient = 2
+accumulate_gradient = 1
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 = true
+discard_oversize = false
omit_extra_lookups = false
-batch_by_words = true
+batch_by = "words"
[training.batch_size]
@schedules = "compounding.v1"
-start = 1000
+start = 100
stop = 1000
compound = 1.001
@@ -45,12 +45,6 @@ use_averages = true
eps = 1e-8
learn_rate = 0.001
-#[training.optimizer.learn_rate]
-#@schedules = "warmup_linear.v1"
-#warmup_steps = 1000
-#total_steps = 50000
-#initial_rate = 0.003
-
[nlp]
lang = "en"
vectors = null
@@ -74,6 +68,6 @@ width = 96
depth = 4
window_size = 1
embed_size = 2000
-maxout_pieces = 1
+maxout_pieces = 3
subword_features = true
dropout = ${training:dropout}
