mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-26 18:06:29 +03:00
75d9019343
* fix TorchBiLSTMEncoder documentation * ensure the types of the encoding Tok2vec layers are correct * update references from v1 to v2 for the new architectures
427 lines
10 KiB
Django/Jinja
427 lines
10 KiB
Django/Jinja
{# This is a template for training configs used for the quickstart widget in
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the docs and the init config command. It encodes various best practices and
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can help generate the best possible configuration, given a user's requirements. #}
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{%- set use_transformer = (transformer_data and hardware != "cpu") -%}
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{%- set transformer = transformer_data[optimize] if use_transformer else {} -%}
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[paths]
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train = null
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dev = null
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[system]
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{% if use_transformer -%}
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gpu_allocator = "pytorch"
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{% else -%}
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gpu_allocator = null
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{% endif %}
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[nlp]
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lang = "{{ lang }}"
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{%- set full_pipeline = ["transformer" if use_transformer else "tok2vec"] + components %}
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pipeline = {{ full_pipeline|pprint()|replace("'", '"')|safe }}
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tokenizer = {"@tokenizers": "spacy.Tokenizer.v1"}
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batch_size = {{ 128 if hardware == "gpu" else 1000 }}
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[components]
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{# TRANSFORMER PIPELINE #}
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{%- if use_transformer -%}
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[components.transformer]
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factory = "transformer"
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[components.transformer.model]
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@architectures = "spacy-transformers.TransformerModel.v1"
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name = "{{ transformer["name"] }}"
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tokenizer_config = {"use_fast": true}
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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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{% if "morphologizer" in components %}
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[components.morphologizer]
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factory = "morphologizer"
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[components.morphologizer.model]
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@architectures = "spacy.Tagger.v1"
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nO = null
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[components.morphologizer.model.tok2vec]
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@architectures = "spacy-transformers.TransformerListener.v1"
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grad_factor = 1.0
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[components.morphologizer.model.tok2vec.pooling]
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@layers = "reduce_mean.v1"
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{%- endif %}
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{% if "tagger" in components %}
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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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[components.tagger.model.tok2vec.pooling]
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@layers = "reduce_mean.v1"
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{%- endif %}
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{% if "parser" in components -%}
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[components.parser]
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factory = "parser"
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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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[components.parser.model.tok2vec.pooling]
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@layers = "reduce_mean.v1"
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{%- endif %}
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{% if "ner" in components -%}
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[components.ner]
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factory = "ner"
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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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[components.ner.model.tok2vec.pooling]
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@layers = "reduce_mean.v1"
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{% endif -%}
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{% if "entity_linker" in components -%}
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[components.entity_linker]
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factory = "entity_linker"
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get_candidates = {"@misc":"spacy.CandidateGenerator.v1"}
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incl_context = true
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incl_prior = true
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[components.entity_linker.model]
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@architectures = "spacy.EntityLinker.v1"
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nO = null
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[components.entity_linker.model.tok2vec]
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@architectures = "spacy-transformers.TransformerListener.v1"
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grad_factor = 1.0
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[components.entity_linker.model.tok2vec.pooling]
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@layers = "reduce_mean.v1"
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{% endif -%}
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{% if "textcat" in components %}
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[components.textcat]
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factory = "textcat"
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{% if optimize == "accuracy" %}
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[components.textcat.model]
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@architectures = "spacy.TextCatEnsemble.v2"
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nO = null
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[components.textcat.model.tok2vec]
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@architectures = "spacy-transformers.TransformerListener.v1"
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grad_factor = 1.0
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[components.textcat.model.tok2vec.pooling]
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@layers = "reduce_mean.v1"
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[components.textcat.model.linear_model]
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@architectures = "spacy.TextCatBOW.v1"
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exclusive_classes = true
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ngram_size = 1
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no_output_layer = false
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{% else -%}
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[components.textcat.model]
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@architectures = "spacy.TextCatBOW.v1"
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exclusive_classes = true
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ngram_size = 1
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no_output_layer = false
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{%- endif %}
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{%- endif %}
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{% if "textcat_multilabel" in components %}
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[components.textcat_multilabel]
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factory = "textcat_multilabel"
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{% if optimize == "accuracy" %}
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[components.textcat_multilabel.model]
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@architectures = "spacy.TextCatEnsemble.v2"
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nO = null
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[components.textcat_multilabel.model.tok2vec]
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@architectures = "spacy-transformers.TransformerListener.v1"
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grad_factor = 1.0
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[components.textcat_multilabel.model.tok2vec.pooling]
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@layers = "reduce_mean.v1"
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[components.textcat_multilabel.model.linear_model]
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@architectures = "spacy.TextCatBOW.v1"
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exclusive_classes = false
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ngram_size = 1
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no_output_layer = false
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{% else -%}
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[components.textcat_multilabel.model]
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@architectures = "spacy.TextCatBOW.v1"
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exclusive_classes = false
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ngram_size = 1
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no_output_layer = false
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{%- endif %}
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{%- endif %}
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{# NON-TRANSFORMER PIPELINE #}
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{% else -%}
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{%- if hardware == "gpu" -%}
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# There are no recommended transformer weights available for language '{{ lang }}'
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# yet, so the pipeline described here is not transformer-based.
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{%- endif %}
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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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{% if has_letters -%}
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attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
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rows = [5000, 2500, 2500, 2500]
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{% else -%}
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attrs = ["ORTH", "SHAPE"]
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rows = [5000, 2500]
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{% endif -%}
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include_static_vectors = {{ "true" if optimize == "accuracy" else "false" }}
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[components.tok2vec.model.encode]
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@architectures = "spacy.MaxoutWindowEncoder.v2"
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width = {{ 96 if optimize == "efficiency" else 256 }}
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depth = {{ 4 if optimize == "efficiency" else 8 }}
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window_size = 1
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maxout_pieces = 3
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{% if "morphologizer" in components %}
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[components.morphologizer]
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factory = "morphologizer"
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[components.morphologizer.model]
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@architectures = "spacy.Tagger.v1"
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nO = null
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[components.morphologizer.model.tok2vec]
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@architectures = "spacy.Tok2VecListener.v1"
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width = ${components.tok2vec.model.encode.width}
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{%- endif %}
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{% if "tagger" in components %}
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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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{%- endif %}
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{% if "parser" in components -%}
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[components.parser]
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factory = "parser"
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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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{%- endif %}
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{% if "ner" in components %}
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[components.ner]
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factory = "ner"
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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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{% endif %}
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{% if "entity_linker" in components -%}
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[components.entity_linker]
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factory = "entity_linker"
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get_candidates = {"@misc":"spacy.CandidateGenerator.v1"}
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incl_context = true
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incl_prior = true
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[components.entity_linker.model]
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@architectures = "spacy.EntityLinker.v1"
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nO = null
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[components.entity_linker.model.tok2vec]
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@architectures = "spacy.Tok2VecListener.v1"
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width = ${components.tok2vec.model.encode.width}
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{% endif %}
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{% if "textcat" in components %}
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[components.textcat]
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factory = "textcat"
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{% if optimize == "accuracy" %}
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[components.textcat.model]
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@architectures = "spacy.TextCatEnsemble.v2"
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nO = null
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[components.textcat.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.textcat.model.linear_model]
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@architectures = "spacy.TextCatBOW.v1"
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exclusive_classes = true
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ngram_size = 1
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no_output_layer = false
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{% else -%}
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[components.textcat.model]
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@architectures = "spacy.TextCatBOW.v1"
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exclusive_classes = true
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ngram_size = 1
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no_output_layer = false
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{%- endif %}
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{%- endif %}
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{% if "textcat_multilabel" in components %}
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[components.textcat_multilabel]
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factory = "textcat_multilabel"
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{% if optimize == "accuracy" %}
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[components.textcat_multilabel.model]
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@architectures = "spacy.TextCatEnsemble.v2"
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nO = null
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[components.textcat_multilabel.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.textcat_multilabel.model.linear_model]
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@architectures = "spacy.TextCatBOW.v1"
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exclusive_classes = false
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ngram_size = 1
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no_output_layer = false
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{% else -%}
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[components.textcat_multilabel.model]
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@architectures = "spacy.TextCatBOW.v1"
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exclusive_classes = false
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ngram_size = 1
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no_output_layer = false
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{%- endif %}
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{%- endif %}
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{% endif %}
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{% for pipe in components %}
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{% if pipe not in ["tagger", "morphologizer", "parser", "ner", "textcat", "textcat_multilabel", "entity_linker"] %}
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{# Other components defined by the user: we just assume they're factories #}
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[components.{{ pipe }}]
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factory = "{{ pipe }}"
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{% endif %}
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{% endfor %}
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[corpora]
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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 if hardware == "gpu" else 2000 }}
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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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[training]
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{% if use_transformer -%}
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accumulate_gradient = {{ transformer["size_factor"] }}
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{% endif -%}
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dev_corpus = "corpora.dev"
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train_corpus = "corpora.train"
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[training.optimizer]
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@optimizers = "Adam.v1"
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{% if use_transformer -%}
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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 = 5e-5
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{% endif %}
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{% if use_transformer %}
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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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{%- else %}
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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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{% endif %}
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[initialize]
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{% if use_transformer or optimize == "efficiency" or not word_vectors -%}
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vectors = null
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{% else -%}
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vectors = "{{ word_vectors }}"
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{% endif -%}
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