Config that seems to train successfully
# 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 = 800
+max_length = 350
limit = 0
# Data augmentation
orth_variant_level = 0.0
@@ -91,8 +91,8 @@ tokenizer_config = {"use_fast": true}
# 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.
@span_getters = "strided_spans.v1"
-window = 64
-stride = 64
+window = 128
+stride = 128
[nlp.pipeline.tagger.model]
@architectures = "spacy.Tagger.v1"
