Update joint-core-bert config. Getting pretty great onto5 results
@@ -20,7 +20,7 @@ seed = 0
accumulate_gradient = 2
width = 768
# Control how scores are printed and checkpoints are evaluated.
-scores = ["tags_acc", "uas", "las", "ents_f"]
+scores = ["speed", "tags_acc", "uas", "las", "ents_f"]
score_weights = {"las": 0.4, "ents_f": 0.4, "tags_acc": 0.2}
# These settings are invalid for the transformer models.
init_tok2vec = null
@@ -40,14 +40,14 @@ beta2 = 0.999
L2_is_weight_decay = true
L2 = 0.01
grad_clip = 1.0
-use_averages = false
+use_averages = true
eps = 1e-8
+initial_rate = 5e-5
[optimizer.learn_rate]
@schedules = "warmup_linear.v1"
-initial_rate = 5e-5
warmup_steps = 250
-total_steps = 10000
+total_steps = 20000
[nlp]
lang = "en"
@@ -87,7 +87,9 @@ fast_tokenizer = true
[nlp.pipeline.transformer.model.get_spans]
# You can set a custom strategy for preparing spans from the batch, e.g. you
# can predict over sentences. Here we predict over the whole document.
-@layers = "spacy-transformers.get_doc_spans.v1"
+@layers = "spacy-transformers.strided_spans.v1"
+window = 128
+stride = 128
[nlp.pipeline.tagger.model]
@architectures = "spacy.Tagger.v1"
