mirror of https://github.com/explosion/spaCy.git
140 lines
3.0 KiB
Django/Jinja
140 lines
3.0 KiB
Django/Jinja
{# Template for "CPU" configs. The transformer will use a different template. #}
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# This is an auto-generated partial config for training a model.
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# To use it for training, auto-fill it with all default values.
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# python -m spacy init config config.cfg --base base_config.cfg
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[paths]
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train = ""
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dev = ""
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[nlp]
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lang = "{{ lang }}"
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pipeline = {{ pipeline|safe }}
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vectors = null
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tokenizer = {"@tokenizers": "spacy.Tokenizer.v1"}
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[components]
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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_info["name"] }} #}
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name = "roberta-base"
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tokenizer_config = {"use_fast": true}
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[components.transformer.model.get_spans]
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@span_getters = "strided_spans.v1"
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window = 128
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stride = 96
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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.ner.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.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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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.ner.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.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 = 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.parser.model.tok2vec.pooling]
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@layers = "reduce_mean.v1"
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{% endif -%}
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[training]
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{#- accumulate_gradient = {{ transformer_info["size_factor"] }} #}
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accumulate_gradient = 3
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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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[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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[training.train_corpus]
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@readers = "spacy.Corpus.v1"
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path = ${paths:train}
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gold_preproc = false
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max_length = 500
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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 = false
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max_length = 0
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limit = 0
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[training.batcher]
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@batchers = "batch_by_padded.v1"
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discard_oversize = true
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batch_size = 2000
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[training.score_weights]
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{%- if "tagger" in components %}
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tag_acc = {{ (1.0 / components|length)|round(2) }}
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{%- endif -%}
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{%- if "parser" in components %}
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dep_uas = 0.0
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dep_las = {{ (1.0 / components|length)|round(2) }}
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sents_f = 0.0
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{%- endif %}
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{%- if "ner" in components %}
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ents_f = {{ (1.0 / components|length)|round(2) }}
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ents_p = 0.0
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ents_r = 0.0
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{%- endif -%}
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