Add spancat, trainable_lemmatizer to quickstart (#10524)

* Add `SPACY` and `IS_SPACE` as default `tok2vec` features

spacy/cli/templates/quickstart_training.jinja

@@ -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) -%}
+{%- 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 %}
+{%- 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 }}"

website/src/widgets/quickstart-training.js

@@ -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`