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Update configs
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@ -9,12 +9,12 @@ max_length = 5000
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limit = 0
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# Data augmentation
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orth_variant_level = 0.0
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dropout = 0.2
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dropout = 0.1
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# Controls early-stopping. 0 or -1 mean unlimited.
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patience = 1600
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patience = 100000
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max_epochs = 0
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max_steps = 20000
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eval_frequency = 500
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max_steps = 100000
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eval_frequency = 2000
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# Other settings
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seed = 0
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accumulate_gradient = 1
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@ -30,25 +30,25 @@ omit_extra_lookups = false
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[training.batch_size]
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@schedules = "compounding.v1"
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start = 100
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stop = 1000
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stop = 2000
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compound = 1.001
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[training.optimizer]
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@optimizers = "Adam.v1"
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beta1 = 0.9
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beta2 = 0.999
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L2_is_weight_decay = false
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L2 = 1e-6
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L2_is_weight_decay = true
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L2 = 0.0
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grad_clip = 1.0
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use_averages = true
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eps = 1e-8
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learn_rate = 0.001
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#[optimizer.learn_rate]
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#[training.optimizer.learn_rate]
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#@schedules = "warmup_linear.v1"
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#warmup_steps = 250
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#total_steps = 20000
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#initial_rate = 0.001
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#warmup_steps = 1000
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#total_steps = 50000
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#initial_rate = 0.003
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[nlp]
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lang = "en"
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@ -58,23 +58,21 @@ vectors = null
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factory = "ner"
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learn_tokens = false
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min_action_freq = 1
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beam_width = 1
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beam_update_prob = 1.0
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[nlp.pipeline.ner.model]
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@architectures = "spacy.TransitionBasedParser.v1"
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nr_feature_tokens = 3
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hidden_width = 64
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maxout_pieces = 2
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use_upper = true
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use_upper = false
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[nlp.pipeline.ner.model.tok2vec]
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@architectures = "spacy.HashEmbedCNN.v1"
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pretrained_vectors = ${nlp:vectors}
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width = 96
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width = 300
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depth = 4
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window_size = 1
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embed_size = 2000
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maxout_pieces = 3
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embed_size = 7000
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maxout_pieces = 1
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subword_features = true
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dropout = ${training:dropout}
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