Update PTB config

examples/experiments/ptb-joint-pos-dep/defaults.cfg

@@ -1,4 +1,5 @@
 [training]
+max_steps = 0
 patience = 10000
 eval_frequency = 200
 dropout = 0.2
@@ -8,13 +9,20 @@ max_epochs = 100
 orth_variant_level = 0.0
 gold_preproc = true
 max_length = 0
-use_gpu = -1
 scores = ["tags_acc", "uas", "las"]
 score_weights = {"las": 0.8, "tags_acc": 0.2}
 limit = 0
 seed = 0
 accumulate_gradient = 2
 discard_oversize = false
+raw_text = null
+tag_map = null
+morph_rules = null
+base_model = null
+
+eval_batch_size = 128
+use_pytorch_for_gpu_memory = false
+batch_by = "padded"
 
 [training.batch_size]
 @schedules = "compounding.v1"
@@ -30,41 +38,48 @@ beta2 = 0.999
 
 [nlp]
 lang = "en"
-vectors = ${training:vectors}
+pipeline = ["tok2vec", "tagger", "parser"]
+load_vocab_data = false
 
-[nlp.pipeline.tok2vec]
+[nlp.tokenizer]
+@tokenizers = "spacy.Tokenizer.v1"
+
+[nlp.lemmatizer]
+@lemmatizers = "spacy.Lemmatizer.v1"
+
+[components]
+
+[components.tok2vec]
 factory = "tok2vec"
 
-[nlp.pipeline.tagger]
+[components.tagger]
 factory = "tagger"
 
-[nlp.pipeline.parser]
+[components.parser]
 factory = "parser"
 learn_tokens = false
 min_action_freq = 1
-beam_width = 1
-beam_update_prob = 1.0
 
-[nlp.pipeline.tagger.model]
+[components.tagger.model]
 @architectures = "spacy.Tagger.v1"
 
-[nlp.pipeline.tagger.model.tok2vec]
+[components.tagger.model.tok2vec]
 @architectures = "spacy.Tok2VecTensors.v1"
-width = ${nlp.pipeline.tok2vec.model:width}
+width = ${components.tok2vec.model:width}
 
-[nlp.pipeline.parser.model]
+[components.parser.model]
 @architectures = "spacy.TransitionBasedParser.v1"
 nr_feature_tokens = 8
 hidden_width = 64
 maxout_pieces = 3
 
-[nlp.pipeline.parser.model.tok2vec]
+[components.parser.model.tok2vec]
 @architectures = "spacy.Tok2VecTensors.v1"
-width = ${nlp.pipeline.tok2vec.model:width}
+width = ${components.tok2vec.model:width}
 
-[nlp.pipeline.tok2vec.model]
+[components.tok2vec.model]
 @architectures = "spacy.HashEmbedCNN.v1"
-pretrained_vectors = ${nlp:vectors}
+pretrained_vectors = ${training:vectors}
 width = 96
 depth = 4
 window_size = 1