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Add spancat, trainable_lemmatizer to quickstart (#10524)

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* Add `SPACY` and `IS_SPACE` as default `tok2vec` features
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Adriane Boyd 2022-04-01 09:01:04 +02:00 committed by GitHub
parent 7d1edc0c25
commit 03762b4b92
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2 changed files with 108 additions and 5 deletions
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111
spacy/cli/templates/quickstart_training.jinja
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@ -3,6 +3,7 @@ the docs and the init config command. It encodes various best practices and
can help generate the best possible configuration, given a user's requirements. #}
{%- set use_transformer = hardware != "cpu" -%}
{%- set transformer = transformer_data[optimize] if use_transformer else {} -%}
{%- set listener_components = ["tagger", "morphologizer", "parser", "ner", "textcat", "textcat_multilabel", "entity_linker", "spancat", "trainable_lemmatizer"] -%}
[paths]
train = null
dev = null
@ -24,10 +25,10 @@ lang = "{{ lang }}"
{%- set has_textcat = ("textcat" in components or "textcat_multilabel" in components) -%}
{%- set with_accuracy = optimize == "accuracy" -%}
{%- set has_accurate_textcat = has_textcat and with_accuracy -%}
{%- if ("tagger" in components or "morphologizer" in components or "parser" in components or "ner" in components or "entity_linker" in components or has_accurate_textcat) -%}
{%- set full_pipeline = ["transformer" if use_transformer else "tok2vec"] + components %}
{%- if ("tagger" in components or "morphologizer" in components or "parser" in components or "ner" in components or "spancat" in components or "trainable_lemmatizer" in components or "entity_linker" in components or has_accurate_textcat) -%}
{%- set full_pipeline = ["transformer" if use_transformer else "tok2vec"] + components -%}
{%- else -%}
{%- set full_pipeline = components %}
{%- set full_pipeline = components -%}
{%- endif %}
pipeline = {{ full_pipeline|pprint()|replace("'", '"')|safe }}
batch_size = {{ 128 if hardware == "gpu" else 1000 }}
@ -123,6 +124,60 @@ grad_factor = 1.0
@layers = "reduce_mean.v1"
{% endif -%}
{% if "spancat" in components -%}
[components.spancat]
factory = "spancat"
max_positive = null
scorer = {"@scorers":"spacy.spancat_scorer.v1"}
spans_key = "sc"
threshold = 0.5
[components.spancat.model]
@architectures = "spacy.SpanCategorizer.v1"
[components.spancat.model.reducer]
@layers = "spacy.mean_max_reducer.v1"
hidden_size = 128
[components.spancat.model.scorer]
@layers = "spacy.LinearLogistic.v1"
nO = null
nI = null
[components.spancat.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0
[components.spancat.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
[components.spancat.suggester]
@misc = "spacy.ngram_suggester.v1"
sizes = [1,2,3]
{% endif -%}
{% if "trainable_lemmatizer" in components -%}
[components.trainable_lemmatizer]
factory = "trainable_lemmatizer"
backoff = "orth"
min_tree_freq = 3
overwrite = false
scorer = {"@scorers":"spacy.lemmatizer_scorer.v1"}
top_k = 1
[components.trainable_lemmatizer.model]
@architectures = "spacy.Tagger.v2"
nO = null
normalize = false
[components.trainable_lemmatizer.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0
[components.trainable_lemmatizer.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
{% endif -%}
{% if "entity_linker" in components -%}
[components.entity_linker]
factory = "entity_linker"
@ -295,6 +350,54 @@ nO = null
width = ${components.tok2vec.model.encode.width}
{% endif %}
{% if "spancat" in components %}
[components.spancat]
factory = "spancat"
max_positive = null
scorer = {"@scorers":"spacy.spancat_scorer.v1"}
spans_key = "sc"
threshold = 0.5
[components.spancat.model]
@architectures = "spacy.SpanCategorizer.v1"
[components.spancat.model.reducer]
@layers = "spacy.mean_max_reducer.v1"
hidden_size = 128
[components.spancat.model.scorer]
@layers = "spacy.LinearLogistic.v1"
nO = null
nI = null
[components.spancat.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}
[components.spancat.suggester]
@misc = "spacy.ngram_suggester.v1"
sizes = [1,2,3]
{% endif %}
{% if "trainable_lemmatizer" in components -%}
[components.trainable_lemmatizer]
factory = "trainable_lemmatizer"
backoff = "orth"
min_tree_freq = 3
overwrite = false
scorer = {"@scorers":"spacy.lemmatizer_scorer.v1"}
top_k = 1
[components.trainable_lemmatizer.model]
@architectures = "spacy.Tagger.v2"
nO = null
normalize = false
[components.trainable_lemmatizer.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}
{% endif -%}
{% if "entity_linker" in components -%}
[components.entity_linker]
factory = "entity_linker"
@ -369,7 +472,7 @@ no_output_layer = false
{% endif %}
{% for pipe in components %}
{% if pipe not in ["tagger", "morphologizer", "parser", "ner", "textcat", "textcat_multilabel", "entity_linker"] %}
{% if pipe not in listener_components %}
{# Other components defined by the user: we just assume they're factories #}
[components.{{ pipe }}]
factory = "{{ pipe }}"

2
website/src/widgets/quickstart-training.js
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@ -10,7 +10,7 @@ const DEFAULT_LANG = 'en'
const DEFAULT_HARDWARE = 'cpu'
const DEFAULT_OPT = 'efficiency'
const DEFAULT_TEXTCAT_EXCLUSIVE = true
const COMPONENTS = ['tagger', 'morphologizer', 'parser', 'ner', 'textcat']
const COMPONENTS = ['tagger', 'morphologizer', 'trainable_lemmatizer', 'parser', 'ner', 'spancat', 'textcat']
const COMMENT = `# This is an auto-generated partial config. To use it with 'spacy train'
# you can run spacy init fill-config to auto-fill all default settings:
# python -m spacy init fill-config ./base_config.cfg ./config.cfg`