mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-26 01:46:28 +03:00
Remove outdated configs
This commit is contained in:
Matthew Honnibal
parent
8303d101a5
commit
30e85b2a42
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@ -1,133 +0,0 @@
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[paths]
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train = ""
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dev = ""
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raw = null
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init_tok2vec = null
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[system]
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seed = 0
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use_pytorch_for_gpu_memory = false
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[training]
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seed = ${system:seed}
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dropout = 0.1
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init_tok2vec = ${paths:init_tok2vec}
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vectors = null
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accumulate_gradient = 1
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max_steps = 0
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max_epochs = 0
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patience = 10000
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eval_frequency = 200
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score_weights = {"dep_las": 0.4, "ents_f": 0.4, "tag_acc": 0.2}
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frozen_components = []
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[training.train_corpus]
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@readers = "spacy.Corpus.v1"
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path = ${paths:train}
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gold_preproc = true
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max_length = 0
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limit = 0
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[training.dev_corpus]
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@readers = "spacy.Corpus.v1"
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path = ${paths:dev}
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gold_preproc = ${training.read_train:gold_preproc}
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max_length = 0
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limit = 0
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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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[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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[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 = 1e-8
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learn_rate = 0.001
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[nlp]
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lang = "en"
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load_vocab_data = false
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pipeline = ["tok2vec", "ner", "tagger", "parser"]
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[nlp.tokenizer]
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@tokenizers = "spacy.Tokenizer.v1"
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[nlp.lemmatizer]
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@lemmatizers = "spacy.Lemmatizer.v1"
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[components]
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[components.tok2vec]
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factory = "tok2vec"
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[components.ner]
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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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[components.tagger]
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factory = "tagger"
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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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[components.tagger.model]
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@architectures = "spacy.Tagger.v1"
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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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[components.parser.model]
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@architectures = "spacy.TransitionBasedParser.v1"
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nr_feature_tokens = 8
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hidden_width = 128
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maxout_pieces = 2
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use_upper = true
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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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[components.ner.model]
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@architectures = "spacy.TransitionBasedParser.v1"
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nr_feature_tokens = 3
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hidden_width = 128
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maxout_pieces = 2
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use_upper = true
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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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[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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rows = 2000
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also_embed_subwords = true
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also_use_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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@ -1,152 +0,0 @@
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# Training hyper-parameters and additional features.
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[training]
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# Whether to train on sequences with 'gold standard' sentence boundaries
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# and tokens. If you set this to true, take care to ensure your run-time
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# data is passed in sentence-by-sentence via some prior preprocessing.
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gold_preproc = false
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# Limitations on training document length or number of examples.
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max_length = 0
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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 = 0.1
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# Controls early-stopping. 0 or -1 mean unlimited.
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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 = 400
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# Other settings
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seed = 0
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accumulate_gradient = 1
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use_pytorch_for_gpu_memory = false
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# Control how scores are printed and checkpoints are evaluated.
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scores = ["speed", "tags_acc", "uas", "las", "ents_f"]
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score_weights = {"las": 0.4, "ents_f": 0.4, "tags_acc": 0.2}
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# These settings are invalid for the transformer models.
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init_tok2vec = null
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discard_oversize = false
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omit_extra_lookups = false
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batch_by = "words"
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use_gpu = -1
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raw_text = null
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tag_map = null
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[training.batch_size]
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@schedules = "compounding.v1"
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start = 1000
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stop = 1000
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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 = 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 = 1e-8
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learn_rate = 0.001
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[pretraining]
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max_epochs = 1000
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min_length = 5
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max_length = 500
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dropout = 0.2
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n_save_every = null
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batch_size = 3000
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seed = ${training:seed}
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use_pytorch_for_gpu_memory = ${training:use_pytorch_for_gpu_memory}
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tok2vec_model = "nlp.pipeline.tok2vec.model"
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[pretraining.objective]
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type = "characters"
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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 = 1e-8
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learn_rate = 0.001
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[nlp]
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lang = "en"
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vectors = null
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base_model = null
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[nlp.pipeline]
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[nlp.pipeline.tok2vec]
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factory = "tok2vec"
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[nlp.pipeline.senter]
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factory = "senter"
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[nlp.pipeline.ner]
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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.tagger]
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factory = "tagger"
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[nlp.pipeline.parser]
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factory = "parser"
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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.senter.model]
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@architectures = "spacy.Tagger.v1"
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[nlp.pipeline.senter.model.tok2vec]
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@architectures = "spacy.Tok2VecTensors.v1"
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width = ${nlp.pipeline.tok2vec.model:width}
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[nlp.pipeline.tagger.model]
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@architectures = "spacy.Tagger.v1"
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[nlp.pipeline.tagger.model.tok2vec]
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@architectures = "spacy.Tok2VecTensors.v1"
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width = ${nlp.pipeline.tok2vec.model:width}
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[nlp.pipeline.parser.model]
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@architectures = "spacy.TransitionBasedParser.v1"
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nr_feature_tokens = 8
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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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[nlp.pipeline.parser.model.tok2vec]
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@architectures = "spacy.Tok2VecTensors.v1"
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width = ${nlp.pipeline.tok2vec.model:width}
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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 = 128
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maxout_pieces = 3
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use_upper = false
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[nlp.pipeline.ner.model.tok2vec]
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@architectures = "spacy.Tok2VecTensors.v1"
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width = ${nlp.pipeline.tok2vec.model:width}
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[nlp.pipeline.tok2vec.model]
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@architectures = "spacy.HashEmbedCNN.v1"
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pretrained_vectors = ${nlp:vectors}
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width = 256
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depth = 6
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window_size = 1
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embed_size = 10000
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maxout_pieces = 3
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subword_features = true
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dropout = null
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@ -1,73 +0,0 @@
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# Training hyper-parameters and additional features.
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[training]
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# Whether to train on sequences with 'gold standard' sentence boundaries
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# and tokens. If you set this to true, take care to ensure your run-time
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# data is passed in sentence-by-sentence via some prior preprocessing.
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gold_preproc = false
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# Limitations on training document length or number of examples.
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max_length = 3000
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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 = 0.1
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# Controls early-stopping. 0 or -1 mean unlimited.
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patience = 100000
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max_epochs = 0
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max_steps = 0
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eval_frequency = 1000
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# Other settings
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seed = 0
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accumulate_gradient = 1
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use_pytorch_for_gpu_memory = false
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# Control how scores are printed and checkpoints are evaluated.
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scores = ["speed", "ents_p", "ents_r", "ents_f"]
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score_weights = {"ents_f": 1.0}
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# These settings are invalid for the transformer models.
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init_tok2vec = null
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discard_oversize = false
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omit_extra_lookups = false
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batch_by = "words"
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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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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 = 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 = 1e-8
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learn_rate = 0.001
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[nlp]
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lang = "en"
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vectors = null
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[nlp.pipeline.ner]
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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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[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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[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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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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subword_features = true
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dropout = ${training:dropout}
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@ -1,73 +0,0 @@
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[training]
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patience = 10000
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eval_frequency = 200
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dropout = 0.2
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init_tok2vec = null
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vectors = null
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max_epochs = 100
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orth_variant_level = 0.0
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gold_preproc = true
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max_length = 0
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use_gpu = 0
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scores = ["tags_acc", "uas", "las"]
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score_weights = {"las": 0.8, "tags_acc": 0.2}
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limit = 0
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seed = 0
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accumulate_gradient = 2
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discard_oversize = 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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compound = 1.001
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[training.optimizer]
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@optimizers = "Adam.v1"
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learn_rate = 0.001
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beta1 = 0.9
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beta2 = 0.999
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[nlp]
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lang = "en"
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vectors = ${training:vectors}
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[nlp.pipeline.tok2vec]
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factory = "tok2vec"
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[nlp.pipeline.tagger]
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factory = "tagger"
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[nlp.pipeline.parser]
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factory = "parser"
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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.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 = 64
|
|
||||||
maxout_pieces = 3
|
|
||||||
|
|
||||||
[nlp.pipeline.parser.model.tok2vec]
|
|
||||||
@architectures = "spacy.Tok2VecTensors.v1"
|
|
||||||
width = ${nlp.pipeline.tok2vec.model:width}
|
|
||||||
|
|
||||||
[nlp.pipeline.tok2vec.model]
|
|
||||||
@architectures = "spacy.HashEmbedBiLSTM.v1"
|
|
||||||
pretrained_vectors = ${nlp:vectors}
|
|
||||||
width = 96
|
|
||||||
depth = 4
|
|
||||||
embed_size = 2000
|
|
||||||
subword_features = true
|
|
||||||
maxout_pieces = 3
|
|
||||||
dropout = null
|
|
||||||
|
|
@ -1,110 +0,0 @@
|
||||||
[paths]
|
|
||||||
train = ""
|
|
||||||
dev = ""
|
|
||||||
raw = null
|
|
||||||
init_tok2vec = null
|
|
||||||
|
|
||||||
[system]
|
|
||||||
seed = 0
|
|
||||||
use_pytorch_for_gpu_memory = false
|
|
||||||
|
|
||||||
[training]
|
|
||||||
seed = ${system:seed}
|
|
||||||
dropout = 0.2
|
|
||||||
init_tok2vec = ${paths:init_tok2vec}
|
|
||||||
vectors = null
|
|
||||||
accumulate_gradient = 1
|
|
||||||
max_steps = 0
|
|
||||||
max_epochs = 0
|
|
||||||
patience = 10000
|
|
||||||
eval_frequency = 200
|
|
||||||
score_weights = {"dep_las": 0.8, "tag_acc": 0.2}
|
|
||||||
|
|
||||||
[training.read_train]
|
|
||||||
@readers = "spacy.Corpus.v1"
|
|
||||||
path = ${paths:train}
|
|
||||||
gold_preproc = true
|
|
||||||
max_length = 0
|
|
||||||
limit = 0
|
|
||||||
|
|
||||||
[training.read_dev]
|
|
||||||
@readers = "spacy.Corpus.v1"
|
|
||||||
path = ${paths:dev}
|
|
||||||
gold_preproc = ${training.read_train:gold_preproc}
|
|
||||||
max_length = 0
|
|
||||||
limit = 0
|
|
||||||
|
|
||||||
[training.batcher]
|
|
||||||
@batchers = "spacy.batch_by_words.v1"
|
|
||||||
discard_oversize = false
|
|
||||||
tolerance = 0.2
|
|
||||||
|
|
||||||
[training.batcher.size]
|
|
||||||
@schedules = "compounding.v1"
|
|
||||||
start = 100
|
|
||||||
stop = 1000
|
|
||||||
compound = 1.001
|
|
||||||
|
|
||||||
[training.optimizer]
|
|
||||||
@optimizers = "Adam.v1"
|
|
||||||
learn_rate = 0.001
|
|
||||||
beta1 = 0.9
|
|
||||||
beta2 = 0.999
|
|
||||||
|
|
||||||
[nlp]
|
|
||||||
lang = "en"
|
|
||||||
pipeline = ["tok2vec", "tagger", "parser"]
|
|
||||||
load_vocab_data = false
|
|
||||||
|
|
||||||
[nlp.tokenizer]
|
|
||||||
@tokenizers = "spacy.Tokenizer.v1"
|
|
||||||
|
|
||||||
[nlp.lemmatizer]
|
|
||||||
@lemmatizers = "spacy.Lemmatizer.v1"
|
|
||||||
|
|
||||||
[components]
|
|
||||||
|
|
||||||
[components.tok2vec]
|
|
||||||
factory = "tok2vec"
|
|
||||||
|
|
||||||
[components.tagger]
|
|
||||||
factory = "tagger"
|
|
||||||
|
|
||||||
[components.parser]
|
|
||||||
factory = "parser"
|
|
||||||
learn_tokens = false
|
|
||||||
min_action_freq = 1
|
|
||||||
|
|
||||||
[components.tagger.model]
|
|
||||||
@architectures = "spacy.Tagger.v1"
|
|
||||||
|
|
||||||
[components.tagger.model.tok2vec]
|
|
||||||
@architectures = "spacy.Tok2VecListener.v1"
|
|
||||||
width = ${components.tok2vec.model.encode:width}
|
|
||||||
|
|
||||||
[components.parser.model]
|
|
||||||
@architectures = "spacy.TransitionBasedParser.v1"
|
|
||||||
nr_feature_tokens = 8
|
|
||||||
hidden_width = 64
|
|
||||||
maxout_pieces = 3
|
|
||||||
|
|
||||||
[components.parser.model.tok2vec]
|
|
||||||
@architectures = "spacy.Tok2VecListener.v1"
|
|
||||||
width = ${components.tok2vec.model.encode:width}
|
|
||||||
|
|
||||||
[components.tok2vec.model]
|
|
||||||
@architectures = "spacy.Tok2Vec.v1"
|
|
||||||
|
|
||||||
[components.tok2vec.model.embed]
|
|
||||||
@architectures = "spacy.MultiHashEmbed.v1"
|
|
||||||
width = ${components.tok2vec.model.encode:width}
|
|
||||||
rows = 2000
|
|
||||||
also_embed_subwords = true
|
|
||||||
also_use_static_vectors = false
|
|
||||||
|
|
||||||
[components.tok2vec.model.encode]
|
|
||||||
@architectures = "spacy.MaxoutWindowEncoder.v1"
|
|
||||||
width = 96
|
|
||||||
depth = 4
|
|
||||||
window_size = 1
|
|
||||||
maxout_pieces = 3
|
|
||||||
|
|
@ -1,69 +0,0 @@
|
||||||
[training]
|
|
||||||
use_gpu = -1
|
|
||||||
limit = 0
|
|
||||||
dropout = 0.2
|
|
||||||
patience = 10000
|
|
||||||
eval_frequency = 200
|
|
||||||
scores = ["ents_f"]
|
|
||||||
score_weights = {"ents_f": 1}
|
|
||||||
orth_variant_level = 0.0
|
|
||||||
gold_preproc = true
|
|
||||||
max_length = 0
|
|
||||||
batch_size = 25
|
|
||||||
seed = 0
|
|
||||||
accumulate_gradient = 2
|
|
||||||
discard_oversize = false
|
|
||||||
|
|
||||||
[training.optimizer]
|
|
||||||
@optimizers = "Adam.v1"
|
|
||||||
learn_rate = 0.001
|
|
||||||
beta1 = 0.9
|
|
||||||
beta2 = 0.999
|
|
||||||
|
|
||||||
[nlp]
|
|
||||||
lang = "en"
|
|
||||||
vectors = null
|
|
||||||
|
|
||||||
[nlp.pipeline.tok2vec]
|
|
||||||
factory = "tok2vec"
|
|
||||||
|
|
||||||
[nlp.pipeline.tok2vec.model]
|
|
||||||
@architectures = "spacy.Tok2Vec.v1"
|
|
||||||
|
|
||||||
[nlp.pipeline.tok2vec.model.extract]
|
|
||||||
@architectures = "spacy.CharacterEmbed.v1"
|
|
||||||
width = 96
|
|
||||||
nM = 64
|
|
||||||
nC = 8
|
|
||||||
rows = 2000
|
|
||||||
columns = ["ID", "NORM", "PREFIX", "SUFFIX", "SHAPE", "ORTH"]
|
|
||||||
dropout = null
|
|
||||||
|
|
||||||
[nlp.pipeline.tok2vec.model.extract.features]
|
|
||||||
@architectures = "spacy.Doc2Feats.v1"
|
|
||||||
columns = ${nlp.pipeline.tok2vec.model.extract:columns}
|
|
||||||
|
|
||||||
[nlp.pipeline.tok2vec.model.embed]
|
|
||||||
@architectures = "spacy.LayerNormalizedMaxout.v1"
|
|
||||||
width = ${nlp.pipeline.tok2vec.model.extract:width}
|
|
||||||
maxout_pieces = 4
|
|
||||||
|
|
||||||
[nlp.pipeline.tok2vec.model.encode]
|
|
||||||
@architectures = "spacy.MaxoutWindowEncoder.v1"
|
|
||||||
width = ${nlp.pipeline.tok2vec.model.extract:width}
|
|
||||||
window_size = 1
|
|
||||||
maxout_pieces = 2
|
|
||||||
depth = 2
|
|
||||||
|
|
||||||
[nlp.pipeline.ner]
|
|
||||||
factory = "ner"
|
|
||||||
|
|
||||||
[nlp.pipeline.ner.model]
|
|
||||||
@architectures = "spacy.TransitionBasedParser.v1"
|
|
||||||
nr_feature_tokens = 6
|
|
||||||
hidden_width = 64
|
|
||||||
maxout_pieces = 2
|
|
||||||
|
|
||||||
[nlp.pipeline.ner.model.tok2vec]
|
|
||||||
@architectures = "spacy.Tok2VecTensors.v1"
|
|
||||||
width = ${nlp.pipeline.tok2vec.model.extract:width}
|
|
||||||
|
|
@ -1,51 +0,0 @@
|
||||||
[training]
|
|
||||||
use_gpu = -1
|
|
||||||
limit = 0
|
|
||||||
dropout = 0.2
|
|
||||||
patience = 10000
|
|
||||||
eval_frequency = 200
|
|
||||||
scores = ["ents_p", "ents_r", "ents_f"]
|
|
||||||
score_weights = {"ents_f": 1}
|
|
||||||
orth_variant_level = 0.0
|
|
||||||
gold_preproc = true
|
|
||||||
max_length = 0
|
|
||||||
seed = 0
|
|
||||||
accumulate_gradient = 2
|
|
||||||
discard_oversize = false
|
|
||||||
|
|
||||||
[training.batch_size]
|
|
||||||
@schedules = "compounding.v1"
|
|
||||||
start = 3000
|
|
||||||
stop = 3000
|
|
||||||
compound = 1.001
|
|
||||||
|
|
||||||
|
|
||||||
[training.optimizer]
|
|
||||||
@optimizers = "Adam.v1"
|
|
||||||
learn_rate = 0.001
|
|
||||||
beta1 = 0.9
|
|
||||||
beta2 = 0.999
|
|
||||||
|
|
||||||
[nlp]
|
|
||||||
lang = "en"
|
|
||||||
vectors = null
|
|
||||||
|
|
||||||
[nlp.pipeline.ner]
|
|
||||||
factory = "ner"
|
|
||||||
|
|
||||||
[nlp.pipeline.ner.model]
|
|
||||||
@architectures = "spacy.TransitionBasedParser.v1"
|
|
||||||
nr_feature_tokens = 6
|
|
||||||
hidden_width = 64
|
|
||||||
maxout_pieces = 2
|
|
||||||
|
|
||||||
[nlp.pipeline.ner.model.tok2vec]
|
|
||||||
@architectures = "spacy.HashEmbedCNN.v1"
|
|
||||||
width = 128
|
|
||||||
depth = 4
|
|
||||||
embed_size = 7000
|
|
||||||
maxout_pieces = 3
|
|
||||||
window_size = 1
|
|
||||||
subword_features = true
|
|
||||||
pretrained_vectors = null
|
|
||||||
dropout = null
|
|
||||||
Loading…
Reference in New Issue
Block a user