Update joint-core-bert example
@@ -11,10 +11,11 @@ limit = 0
# Data augmentation
orth_variant_level = 0.0
dropout = 0.1
-# Controls early-stopping.
-patience = 10000
-eval_frequency = 200
-max_epochs = 10000
+# Controls early-stopping. 0 or -1 mean unlimited.
+patience = 1600
+max_epochs = 0
+max_steps = 20000
+eval_frequency = 400
# Other settings
seed = 0
accumulate_gradient = 2
@@ -26,11 +27,10 @@ score_weights = {"las": 0.4, "ents_f": 0.4, "tags_acc": 0.2}
init_tok2vec = null
vectors = null
-
[training.batch_size]
@schedules = "compounding.v1"
-start = 500
-stop = 500
+start = 1000
+stop = 1000
compound = 1.001
[optimizer]
@@ -40,14 +40,14 @@ beta2 = 0.999
L2_is_weight_decay = true
L2 = 0.01
grad_clip = 1.0
-use_averages = true
+use_averages = false
eps = 1e-8
-initial_rate = 5e-5
[optimizer.learn_rate]
@schedules = "warmup_linear.v1"
warmup_steps = 250
total_steps = 20000
+initial_rate = 5e-5
[nlp]
lang = "en"
@@ -62,6 +62,7 @@ vectors = ${training:vectors}
# transformer is run once and then the predictions are used by the other
# components. Weights are updated from multi-task gradients.
factory = "transformer"
+max_batch_size = 64
[nlp.pipeline.tagger]
factory = "tagger"
