Add onto-joint experiment file

examples/experiments/onto-joint/defaults.cfg

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+# Training hyper-parameters and additional features.
+[training]
+# Whether to train on sequences with 'gold standard' sentence boundaries
+# and tokens. If you set this to true, take care to ensure your run-time
+# 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 = 0
+limit = 0
+# Data augmentation
+orth_variant_level = 0.0
+dropout = 0.1
+# 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 = 1
+use_pytorch_for_gpu_memory = false
+# Control how scores are printed and checkpoints are evaluated.
+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
+vectors = null
+
+[training.batch_size]
+@schedules = "compounding.v1"
+start = 1000
+stop = 1000
+compound = 1.001
+
+[optimizer]
+@optimizers = "Adam.v1"
+beta1 = 0.9
+beta2 = 0.999
+L2_is_weight_decay = true
+L2 = 0.01
+grad_clip = 1.0
+use_averages = true
+eps = 1e-8
+learn_rate = 0.001
+
+#[optimizer.learn_rate]
+#@schedules = "warmup_linear.v1"
+#warmup_steps = 250
+#total_steps = 20000
+#initial_rate = 0.001
+
+[nlp]
+lang = "en"
+vectors = ${training:vectors}
+
+[nlp.pipeline.tok2vec]
+factory = "tok2vec"
+
+[nlp.pipeline.senter]
+factory = "senter"
+
+[nlp.pipeline.ner]
+factory = "ner"
+
+[nlp.pipeline.tagger]
+factory = "tagger"
+
+[nlp.pipeline.parser]
+factory = "parser"
+
+[nlp.pipeline.senter.model]
+@architectures = "spacy.Tagger.v1"
+
+[nlp.pipeline.senter.model.tok2vec]
+@architectures = "spacy.Tok2VecTensors.v1"
+width = ${nlp.pipeline.tok2vec.model:width}
+
+[nlp.pipeline.tagger.model]
+@architectures = "spacy.Tagger.v1"
+
+[nlp.pipeline.tagger.model.tok2vec]
+@architectures = "spacy.Tok2VecTensors.v1"
+width = ${nlp.pipeline.tok2vec.model:width}
+
+[nlp.pipeline.parser.model]
+@architectures = "spacy.TransitionBasedParser.v1"
+nr_feature_tokens = 8
+hidden_width = 128
+maxout_pieces = 3
+use_upper = false
+
+[nlp.pipeline.parser.model.tok2vec]
+@architectures = "spacy.Tok2VecTensors.v1"
+width = ${nlp.pipeline.tok2vec.model:width}
+
+[nlp.pipeline.ner.model]
+@architectures = "spacy.TransitionBasedParser.v1"
+nr_feature_tokens = 3
+hidden_width = 128
+maxout_pieces = 3
+use_upper = false
+
+[nlp.pipeline.ner.model.tok2vec]
+@architectures = "spacy.Tok2VecTensors.v1"
+width = ${nlp.pipeline.tok2vec.model:width}
+
+[nlp.pipeline.tok2vec.model]
+@architectures = "spacy.HashEmbedCNN.v1"
+pretrained_vectors = ${nlp:vectors}
+width = 256
+depth = 6
+window_size = 1
+embed_size = 10000
+maxout_pieces = 3
+subword_features = true