dn
Browse files- config.cfg +263 -0
- meta.json +508 -0
- tokenizer +4 -0
config.cfg
ADDED
@@ -0,0 +1,263 @@
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1 |
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[paths]
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2 |
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train = "corpus/en-core-web/train.spacy"
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dev = "corpus/en-core-web/dev.spacy"
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vectors = "corpus/en_vectors"
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raw = null
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init_tok2vec = null
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vocab_data = null
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[system]
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gpu_allocator = null
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seed = 0
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[nlp]
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lang = "en"
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pipeline = ["tok2vec","tagger","parser","senter","ner","attribute_ruler","lemmatizer"]
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disabled = ["senter"]
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before_creation = null
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after_creation = null
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after_pipeline_creation = null
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batch_size = 256
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tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
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[components]
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[components.attribute_ruler]
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factory = "attribute_ruler"
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validate = false
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[components.lemmatizer]
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factory = "lemmatizer"
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mode = "rule"
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model = null
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overwrite = false
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[components.ner]
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factory = "ner"
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moves = null
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update_with_oracle_cut_size = 100
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[components.ner.model]
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@architectures = "spacy.TransitionBasedParser.v2"
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state_type = "ner"
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extra_state_tokens = false
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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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nO = null
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[components.ner.model.tok2vec]
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@architectures = "spacy.Tok2Vec.v1"
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51 |
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[components.ner.model.tok2vec.embed]
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@architectures = "spacy.MultiHashEmbed.v1"
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width = 96
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attrs = ["NORM","PREFIX","SUFFIX","SHAPE"]
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rows = [5000,2500,2500,2500]
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include_static_vectors = true
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[components.ner.model.tok2vec.encode]
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@architectures = "spacy.MaxoutWindowEncoder.v1"
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width = 96
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depth = 4
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window_size = 1
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maxout_pieces = 3
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[components.parser]
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factory = "parser"
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learn_tokens = false
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min_action_freq = 30
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moves = null
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update_with_oracle_cut_size = 100
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[components.parser.model]
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@architectures = "spacy.TransitionBasedParser.v2"
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state_type = "parser"
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extra_state_tokens = false
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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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nO = null
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81 |
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[components.parser.model.tok2vec]
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@architectures = "spacy.Tok2VecListener.v1"
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width = ${components.tok2vec.model.encode:width}
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upstream = "*"
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[components.senter]
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factory = "senter"
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[components.senter.model]
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@architectures = "spacy.Tagger.v1"
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nO = null
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[components.senter.model.tok2vec]
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@architectures = "spacy.Tok2Vec.v1"
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[components.senter.model.tok2vec.embed]
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@architectures = "spacy.MultiHashEmbed.v1"
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width = 16
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attrs = ["NORM","PREFIX","SUFFIX","SHAPE"]
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rows = [1000,500,500,500]
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include_static_vectors = true
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[components.senter.model.tok2vec.encode]
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@architectures = "spacy.MaxoutWindowEncoder.v1"
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width = 16
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107 |
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depth = 2
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108 |
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window_size = 1
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109 |
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maxout_pieces = 2
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[components.tagger]
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factory = "tagger"
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[components.tagger.model]
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@architectures = "spacy.Tagger.v1"
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nO = null
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[components.tagger.model.tok2vec]
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@architectures = "spacy.Tok2VecListener.v1"
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width = ${components.tok2vec.model.encode:width}
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upstream = "*"
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[components.tok2vec]
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factory = "tok2vec"
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[components.tok2vec.model]
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@architectures = "spacy.Tok2Vec.v1"
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129 |
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[components.tok2vec.model.embed]
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@architectures = "spacy.MultiHashEmbed.v1"
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131 |
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width = ${components.tok2vec.model.encode:width}
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132 |
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attrs = ["NORM","PREFIX","SUFFIX","SHAPE"]
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133 |
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rows = [5000,2500,2500,2500]
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include_static_vectors = true
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136 |
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[components.tok2vec.model.encode]
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@architectures = "spacy.MaxoutWindowEncoder.v1"
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width = 96
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139 |
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depth = 4
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window_size = 1
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maxout_pieces = 3
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[corpora]
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[corpora.dev]
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@readers = "spacy.Corpus.v1"
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147 |
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limit = 0
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148 |
+
max_length = 0
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149 |
+
path = ${paths:dev}
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150 |
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gold_preproc = false
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151 |
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augmenter = null
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152 |
+
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153 |
+
[corpora.train]
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154 |
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@readers = "spacy.Corpus.v1"
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path = ${paths:train}
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156 |
+
max_length = 5000
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157 |
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gold_preproc = false
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limit = 0
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[corpora.train.augmenter]
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@augmenters = "spacy.orth_variants.v1"
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162 |
+
level = 0.2
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163 |
+
lower = 0.5
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164 |
+
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165 |
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[corpora.train.augmenter.orth_variants]
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166 |
+
@readers = "srsly.read_json.v1"
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167 |
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path = "assets/orth_variants.json"
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168 |
+
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169 |
+
[training]
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170 |
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train_corpus = "corpora.train"
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dev_corpus = "corpora.dev"
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172 |
+
seed = ${system:seed}
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173 |
+
gpu_allocator = ${system:gpu_allocator}
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174 |
+
dropout = 0.1
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175 |
+
accumulate_gradient = 1
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176 |
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patience = 5000
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177 |
+
max_epochs = 0
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178 |
+
max_steps = 0
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179 |
+
eval_frequency = 1000
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180 |
+
frozen_components = []
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181 |
+
before_to_disk = null
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182 |
+
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183 |
+
[training.batcher]
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184 |
+
@batchers = "spacy.batch_by_words.v1"
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185 |
+
discard_oversize = false
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186 |
+
tolerance = 0.2
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187 |
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get_length = null
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188 |
+
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189 |
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[training.batcher.size]
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190 |
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@schedules = "compounding.v1"
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191 |
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start = 100
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192 |
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stop = 1000
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193 |
+
compound = 1.001
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194 |
+
t = 0.0
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195 |
+
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196 |
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[training.logger]
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197 |
+
@loggers = "spacy.WandbLogger.v1"
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198 |
+
project_name = "spacy-v3.0.0a2"
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199 |
+
remove_config_values = []
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200 |
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201 |
+
[training.optimizer]
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202 |
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@optimizers = "Adam.v1"
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203 |
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beta1 = 0.9
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204 |
+
beta2 = 0.999
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L2_is_weight_decay = true
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L2 = 0.01
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+
grad_clip = 1.0
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use_averages = true
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eps = 0.00000001
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learn_rate = 0.001
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[training.score_weights]
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213 |
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tag_acc = 0.2
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dep_uas = 0.0
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215 |
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dep_las = 0.18
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216 |
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dep_las_per_type = null
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sents_p = null
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sents_r = null
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sents_f = 0.22
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ents_f = 0.2
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ents_p = 0.0
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ents_r = 0.0
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+
ents_per_type = null
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lemma_acc = 0.2
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+
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226 |
+
[pretraining]
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227 |
+
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[initialize]
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229 |
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vocab_data = ${paths.vocab_data}
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+
vectors = ${paths.vectors}
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init_tok2vec = ${paths.init_tok2vec}
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before_init = null
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233 |
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after_init = null
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[initialize.components]
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236 |
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237 |
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[initialize.components.ner]
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238 |
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239 |
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[initialize.components.ner.labels]
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240 |
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@readers = "spacy.read_labels.v1"
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241 |
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path = "corpus/labels/ner.json"
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require = false
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243 |
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244 |
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[initialize.components.parser]
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[initialize.components.parser.labels]
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247 |
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@readers = "spacy.read_labels.v1"
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248 |
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path = "corpus/labels/parser.json"
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require = false
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251 |
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[initialize.components.tagger]
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252 |
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253 |
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[initialize.components.tagger.labels]
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254 |
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@readers = "spacy.read_labels.v1"
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255 |
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path = "corpus/labels/tagger.json"
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256 |
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require = false
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257 |
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258 |
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[initialize.lookups]
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259 |
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@misc = "spacy.LookupsDataLoader.v1"
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260 |
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lang = ${nlp.lang}
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261 |
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tables = ["lexeme_norm"]
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262 |
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263 |
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[initialize.tokenizer]
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meta.json
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|
1 |
+
{
|
2 |
+
"lang":"en",
|
3 |
+
"name":"core_web_lg",
|
4 |
+
"version":"3.0.0",
|
5 |
+
"spacy_version":">=3.0.0,<3.1.0",
|
6 |
+
"description":"English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.",
|
7 |
+
"author":"Explosion",
|
8 |
+
"email":"contact@explosion.ai",
|
9 |
+
"url":"https://explosion.ai",
|
10 |
+
"license":"MIT",
|
11 |
+
"spacy_git_version":"e7db07a0b",
|
12 |
+
"vectors":{
|
13 |
+
"width":300,
|
14 |
+
"vectors":684830,
|
15 |
+
"keys":684830,
|
16 |
+
"name":"en_vectors"
|
17 |
+
},
|
18 |
+
"labels":{
|
19 |
+
"tok2vec":[
|
20 |
+
|
21 |
+
],
|
22 |
+
"tagger":[
|
23 |
+
"$",
|
24 |
+
"''",
|
25 |
+
",",
|
26 |
+
"-LRB-",
|
27 |
+
"-RRB-",
|
28 |
+
".",
|
29 |
+
":",
|
30 |
+
"ADD",
|
31 |
+
"AFX",
|
32 |
+
"CC",
|
33 |
+
"CD",
|
34 |
+
"DT",
|
35 |
+
"EX",
|
36 |
+
"FW",
|
37 |
+
"HYPH",
|
38 |
+
"IN",
|
39 |
+
"JJ",
|
40 |
+
"JJR",
|
41 |
+
"JJS",
|
42 |
+
"LS",
|
43 |
+
"MD",
|
44 |
+
"NFP",
|
45 |
+
"NN",
|
46 |
+
"NNP",
|
47 |
+
"NNPS",
|
48 |
+
"NNS",
|
49 |
+
"PDT",
|
50 |
+
"POS",
|
51 |
+
"PRP",
|
52 |
+
"PRP$",
|
53 |
+
"RB",
|
54 |
+
"RBR",
|
55 |
+
"RBS",
|
56 |
+
"RP",
|
57 |
+
"SYM",
|
58 |
+
"TO",
|
59 |
+
"UH",
|
60 |
+
"VB",
|
61 |
+
"VBD",
|
62 |
+
"VBG",
|
63 |
+
"VBN",
|
64 |
+
"VBP",
|
65 |
+
"VBZ",
|
66 |
+
"WDT",
|
67 |
+
"WP",
|
68 |
+
"WP$",
|
69 |
+
"WRB",
|
70 |
+
"XX",
|
71 |
+
"``"
|
72 |
+
],
|
73 |
+
"parser":[
|
74 |
+
"ROOT",
|
75 |
+
"acl",
|
76 |
+
"acomp",
|
77 |
+
"advcl",
|
78 |
+
"advmod",
|
79 |
+
"agent",
|
80 |
+
"amod",
|
81 |
+
"appos",
|
82 |
+
"attr",
|
83 |
+
"aux",
|
84 |
+
"auxpass",
|
85 |
+
"case",
|
86 |
+
"cc",
|
87 |
+
"ccomp",
|
88 |
+
"compound",
|
89 |
+
"conj",
|
90 |
+
"csubj",
|
91 |
+
"csubjpass",
|
92 |
+
"dative",
|
93 |
+
"dep",
|
94 |
+
"det",
|
95 |
+
"dobj",
|
96 |
+
"expl",
|
97 |
+
"intj",
|
98 |
+
"mark",
|
99 |
+
"meta",
|
100 |
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"neg",
|
101 |
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|
102 |
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"npadvmod",
|
103 |
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"nsubj",
|
104 |
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|
105 |
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"nummod",
|
106 |
+
"oprd",
|
107 |
+
"parataxis",
|
108 |
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"pcomp",
|
109 |
+
"pobj",
|
110 |
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"poss",
|
111 |
+
"preconj",
|
112 |
+
"predet",
|
113 |
+
"prep",
|
114 |
+
"prt",
|
115 |
+
"punct",
|
116 |
+
"quantmod",
|
117 |
+
"relcl",
|
118 |
+
"xcomp"
|
119 |
+
],
|
120 |
+
"senter":[
|
121 |
+
"I",
|
122 |
+
"S"
|
123 |
+
],
|
124 |
+
"ner":[
|
125 |
+
"CARDINAL",
|
126 |
+
"DATE",
|
127 |
+
"EVENT",
|
128 |
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"FAC",
|
129 |
+
"GPE",
|
130 |
+
"LANGUAGE",
|
131 |
+
"LAW",
|
132 |
+
"LOC",
|
133 |
+
"MONEY",
|
134 |
+
"NORP",
|
135 |
+
"ORDINAL",
|
136 |
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"ORG",
|
137 |
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"PERCENT",
|
138 |
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"PERSON",
|
139 |
+
"PRODUCT",
|
140 |
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"QUANTITY",
|
141 |
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"TIME",
|
142 |
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"WORK_OF_ART"
|
143 |
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],
|
144 |
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|
145 |
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|
146 |
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],
|
147 |
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|
148 |
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|
149 |
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]
|
150 |
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},
|
151 |
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|
152 |
+
"tok2vec",
|
153 |
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"tagger",
|
154 |
+
"parser",
|
155 |
+
"ner",
|
156 |
+
"attribute_ruler",
|
157 |
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"lemmatizer"
|
158 |
+
],
|
159 |
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|
160 |
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"tok2vec",
|
161 |
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"tagger",
|
162 |
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"parser",
|
163 |
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"senter",
|
164 |
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"ner",
|
165 |
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"attribute_ruler",
|
166 |
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"lemmatizer"
|
167 |
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],
|
168 |
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|
169 |
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|
170 |
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],
|
171 |
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172 |
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173 |
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174 |
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