mirror of https://github.com/explosion/spaCy.git
regression test for 7029
This commit is contained in:
svlandeg
parent
a52d466bfc
commit
ebeedfc70b
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[paths]
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train = null
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dev = null
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vectors = null
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init_tok2vec = null
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raw_text = 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","ner"]
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batch_size = 1000
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disabled = []
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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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tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
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[components]
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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.Tok2VecListener.v1"
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width = ${components.tok2vec.model.encode.width}
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upstream = "*"
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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 = 128
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maxout_pieces = 3
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use_upper = true
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nO = null
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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.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.v2"
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[components.tok2vec.model.embed]
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@architectures = "spacy.MultiHashEmbed.v1"
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width = ${components.tok2vec.model.encode.width}
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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 = false
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[components.tok2vec.model.encode]
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@architectures = "spacy.MaxoutWindowEncoder.v2"
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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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[corpora]
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[corpora.dev]
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@readers = "spacy.Corpus.v1"
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path = ${paths.dev}
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max_length = 0
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gold_preproc = false
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limit = 0
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augmenter = null
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[corpora.pretrain]
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@readers = "spacy.JsonlCorpus.v1"
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path = ${paths.raw_text}
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min_length = 5
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max_length = 500
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limit = 0
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[corpora.train]
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@readers = "spacy.Corpus.v1"
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path = ${paths.train}
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max_length = 2000
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gold_preproc = false
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limit = 0
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augmenter = null
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[training]
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dev_corpus = "corpora.dev"
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train_corpus = "corpora.train"
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seed = ${system.seed}
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gpu_allocator = ${system.gpu_allocator}
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dropout = 0.1
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accumulate_gradient = 1
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patience = 1600
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max_epochs = 0
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max_steps = 20000
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eval_frequency = 200
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frozen_components = []
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before_to_disk = null
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[training.batcher]
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@batchers = "spacy.batch_by_words.v1"
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discard_oversize = false
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tolerance = 0.2
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get_length = null
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[training.batcher.size]
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@schedules = "compounding.v1"
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start = 100
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stop = 1000
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compound = 1.001
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t = 0.0
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[training.logger]
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@loggers = "spacy.ConsoleLogger.v1"
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progress_bar = false
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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 = true
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L2 = 0.01
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grad_clip = 1.0
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use_averages = false
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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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dep_las_per_type = null
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sents_p = null
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sents_r = null
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ents_per_type = null
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tag_acc = 0.33
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dep_uas = 0.17
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dep_las = 0.17
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sents_f = 0.0
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ents_f = 0.33
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ents_p = 0.0
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ents_r = 0.0
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[pretraining]
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max_epochs = 1000
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dropout = 0.2
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n_save_every = null
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component = "tok2vec"
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layer = ""
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corpus = "corpora.pretrain"
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[pretraining.batcher]
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@batchers = "spacy.batch_by_words.v1"
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size = 3000
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discard_oversize = false
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tolerance = 0.2
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get_length = null
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[pretraining.objective]
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@architectures = "spacy.PretrainCharacters.v1"
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maxout_pieces = 3
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hidden_size = 300
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n_characters = 4
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[pretraining.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 = 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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[initialize]
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vectors = null
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init_tok2vec = ${paths.init_tok2vec}
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vocab_data = null
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lookups = null
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before_init = null
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after_init = null
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[initialize.components]
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[initialize.tokenizer]
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@ -0,0 +1,217 @@
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[paths]
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train = null
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dev = null
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vectors = null
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init_tok2vec = null
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raw_text = null
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[system]
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gpu_allocator = "pytorch"
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seed = 0
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[nlp]
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lang = "en"
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pipeline = ["transformer","tagger","parser","ner"]
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batch_size = 128
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disabled = []
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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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tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
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[components]
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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 = false
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nO = null
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[components.ner.model.tok2vec]
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@architectures = "spacy-transformers.TransformerListener.v1"
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grad_factor = 1.0
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pooling = {"@layers":"reduce_mean.v1"}
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upstream = "*"
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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 = 128
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maxout_pieces = 3
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use_upper = false
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nO = null
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[components.parser.model.tok2vec]
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@architectures = "spacy-transformers.TransformerListener.v1"
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grad_factor = 1.0
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pooling = {"@layers":"reduce_mean.v1"}
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upstream = "*"
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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-transformers.TransformerListener.v1"
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grad_factor = 1.0
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pooling = {"@layers":"reduce_mean.v1"}
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upstream = "*"
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[components.transformer]
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factory = "transformer"
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max_batch_items = 4096
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set_extra_annotations = {"@annotation_setters":"spacy-transformers.null_annotation_setter.v1"}
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[components.transformer.model]
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@architectures = "spacy-transformers.TransformerModel.v1"
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name = "roberta-base"
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[components.transformer.model.get_spans]
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@span_getters = "spacy-transformers.strided_spans.v1"
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window = 128
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stride = 96
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[components.transformer.model.tokenizer_config]
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use_fast = true
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[corpora]
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[corpora.dev]
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@readers = "spacy.Corpus.v1"
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path = ${paths.dev}
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max_length = 0
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gold_preproc = false
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limit = 0
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augmenter = null
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[corpora.pretrain]
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@readers = "spacy.JsonlCorpus.v1"
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path = ${paths.raw_text}
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min_length = 5
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max_length = 500
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limit = 0
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[corpora.train]
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@readers = "spacy.Corpus.v1"
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path = ${paths.train}
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max_length = 500
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gold_preproc = false
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limit = 0
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augmenter = null
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[training]
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accumulate_gradient = 3
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dev_corpus = "corpora.dev"
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train_corpus = "corpora.train"
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seed = ${system.seed}
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gpu_allocator = ${system.gpu_allocator}
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dropout = 0.1
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patience = 1600
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max_epochs = 0
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max_steps = 20000
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eval_frequency = 200
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frozen_components = []
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before_to_disk = null
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[training.batcher]
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@batchers = "spacy.batch_by_padded.v1"
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discard_oversize = true
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size = 2000
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buffer = 256
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get_length = null
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[training.logger]
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@loggers = "spacy.ConsoleLogger.v1"
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progress_bar = false
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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 = true
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L2 = 0.01
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grad_clip = 1.0
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use_averages = false
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eps = 0.00000001
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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 = 0.00005
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[training.score_weights]
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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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ents_per_type = null
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tag_acc = 0.33
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dep_uas = 0.17
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dep_las = 0.17
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sents_f = 0.0
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ents_f = 0.33
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ents_p = 0.0
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ents_r = 0.0
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[pretraining]
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max_epochs = 1000
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dropout = 0.2
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n_save_every = null
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component = "tok2vec"
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layer = ""
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corpus = "corpora.pretrain"
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[pretraining.batcher]
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@batchers = "spacy.batch_by_words.v1"
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size = 3000
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discard_oversize = false
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tolerance = 0.2
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get_length = null
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[pretraining.objective]
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@architectures = "spacy.PretrainCharacters.v1"
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maxout_pieces = 3
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hidden_size = 300
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n_characters = 4
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[pretraining.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 = 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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[initialize]
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vectors = null
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init_tok2vec = ${paths.init_tok2vec}
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vocab_data = null
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lookups = null
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before_init = null
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after_init = null
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[initialize.components]
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[initialize.tokenizer]
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@ -0,0 +1,71 @@
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from spacy.lang.en import English
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from spacy.training import Example
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from spacy.util import load_config_from_str
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CONFIG = """
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[nlp]
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lang = "en"
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pipeline = ["tok2vec", "tagger"]
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[components]
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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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[components.tok2vec.model.embed]
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@architectures = "spacy.MultiHashEmbed.v1"
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width = ${components.tok2vec.model.encode:width}
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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 = false
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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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depth = 4
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window_size = 1
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maxout_pieces = 3
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[components.ner]
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factory = "ner"
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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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"""
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TRAIN_DATA = [
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("I like green eggs", {"tags": ["N", "V", "J", "N"]}),
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("Eat blue ham", {"tags": ["V", "J", "N"]}),
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]
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def test_issue7029():
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"""Test that an empty document doesn't mess up an entire batch.
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"""
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nlp = English.from_config(load_config_from_str(CONFIG))
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train_examples = []
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for t in TRAIN_DATA:
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train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
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optimizer = nlp.initialize(get_examples=lambda: train_examples)
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for i in range(50):
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losses = {}
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nlp.update(train_examples, sgd=optimizer, losses=losses)
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texts = ["first", "second", "thrid", "fourth", "and", "then", "some", ""]
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nlp.select_pipes(enable=["tok2vec", "tagger"])
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docs1 = list(nlp.pipe(texts, batch_size=1))
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docs2 = list(nlp.pipe(texts, batch_size=4))
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assert [doc[0].tag_ for doc in docs1[:-1]] == [doc[0].tag_ for doc in docs2[:-1]]
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