{# This is a template for training configs used for the quickstart widget in
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" and transformer_data -%}
{%- set transformer = transformer_data[optimize] if use_transformer else {} -%}
{%- set listener_components = ["tagger", "morphologizer", "parser", "ner", "textcat", "textcat_multilabel", "entity_linker", "span_finder", "spancat", "spancat_singlelabel", "trainable_lemmatizer"] -%}
[paths]
train = null
dev = null
{% if use_transformer or optimize == "efficiency" or not word_vectors -%}
vectors = null
{% else -%}
vectors = "{{ word_vectors }}"
{% endif -%}

[system]
{% if use_transformer -%}
gpu_allocator = "pytorch"
{% else -%}
gpu_allocator = null
{% endif %}

[nlp]
lang = "{{ lang }}"
{%- set has_textcat = ("textcat" in components or "textcat_multilabel" in components) -%}
{%- set with_accuracy = optimize == "accuracy" -%}
{# The BOW textcat doesn't need a source of features, so it can omit the
tok2vec/transformer. #}
{%- set with_accuracy_or_transformer = (use_transformer or with_accuracy) -%}
{%- set textcat_needs_features = has_textcat and with_accuracy_or_transformer -%}
{%- if ("tagger" in components or "morphologizer" in components or "parser" in components or "ner" in components or "span_finder" in components or "spancat" in components or "spancat_singlelabel" in components or "trainable_lemmatizer" in components or "entity_linker" in components or textcat_needs_features) -%}
{%- set full_pipeline = ["transformer" if use_transformer else "tok2vec"] + components -%}
{%- else -%}
{%- set full_pipeline = components -%}
{%- endif %}
pipeline = {{ full_pipeline|pprint()|replace("'", '"')|safe }}
batch_size = {{ 128 if hardware == "gpu" else 1000 }}

[components]

{# TRANSFORMER PIPELINE #}
{%- if use_transformer -%}
[components.transformer]
factory = "transformer"

[components.transformer.model]
@architectures = "spacy-transformers.TransformerModel.v3"
name = "{{ transformer["name"] }}"
tokenizer_config = {"use_fast": true}

[components.transformer.model.get_spans]
@span_getters = "spacy-transformers.strided_spans.v1"
window = 128
stride = 96

{% if "morphologizer" in components %}
[components.morphologizer]
factory = "morphologizer"

[components.morphologizer.model]
@architectures = "spacy.Tagger.v2"
nO = null

[components.morphologizer.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0

[components.morphologizer.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
{%- endif %}

{% if "tagger" in components %}
[components.tagger]
factory = "tagger"

[components.tagger.model]
@architectures = "spacy.Tagger.v2"
nO = null

[components.tagger.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0

[components.tagger.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
{%- endif %}

{% if "parser" in components -%}
[components.parser]
factory = "parser"

[components.parser.model]
@architectures = "spacy.TransitionBasedParser.v2"
state_type = "parser"
extra_state_tokens = false
hidden_width = 128
maxout_pieces = 3
use_upper = false
nO = null

[components.parser.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0

[components.parser.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
{%- endif %}

{% if "ner" in components -%}
[components.ner]
factory = "ner"

[components.ner.model]
@architectures = "spacy.TransitionBasedParser.v2"
state_type = "ner"
extra_state_tokens = false
hidden_width = 64
maxout_pieces = 2
use_upper = false
nO = null

[components.ner.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0

[components.ner.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
{% endif -%}

{% if "span_finder" in components -%}
[components.span_finder]
factory = "span_finder"
max_length = 25
min_length = null
scorer = {"@scorers":"spacy.span_finder_scorer.v1"}
spans_key = "sc"
threshold = 0.5

[components.span_finder.model]
@architectures = "spacy.SpanFinder.v1"

[components.span_finder.model.scorer]
@layers = "spacy.LinearLogistic.v1"
nO = 2

[components.span_finder.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0

[components.span_finder.model.tok2vec.pooling]
@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 "spancat_singlelabel" in components %}
[components.spancat_singlelabel]
factory = "spancat_singlelabel"
negative_weight = 1.0
allow_overlap = true
scorer = {"@scorers":"spacy.spancat_scorer.v1"}
spans_key = "sc"

[components.spancat_singlelabel.model]
@architectures = "spacy.SpanCategorizer.v1"

[components.spancat_singlelabel.model.reducer]
@layers = "spacy.mean_max_reducer.v1"
hidden_size = 128

[components.spancat_singlelabel.model.scorer]
@layers = "Softmax.v2"

[components.spancat_singlelabel.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0

[components.spancat_singlelabel.model.tok2vec.pooling]
@layers = "reduce_mean.v1"

[components.spancat_singlelabel.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"
get_candidates = {"@misc":"spacy.CandidateGenerator.v1"}
incl_context = true
incl_prior = true

[components.entity_linker.model]
@architectures = "spacy.EntityLinker.v2"
nO = null

[components.entity_linker.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0

[components.entity_linker.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
{% endif -%}

{% if "textcat" in components %}
[components.textcat]
factory = "textcat"

{% if optimize == "accuracy" %}
[components.textcat.model]
@architectures = "spacy.TextCatEnsemble.v2"
nO = null

[components.textcat.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0

[components.textcat.model.tok2vec.pooling]
@layers = "reduce_mean.v1"

[components.textcat.model.linear_model]
@architectures = "spacy.TextCatBOW.v2"
exclusive_classes = true
ngram_size = 1
no_output_layer = false

{% else -%}
[components.textcat.model]
@architectures = "spacy.TextCatCNN.v2"
exclusive_classes = true
nO = null

[components.textcat.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0

[components.textcat.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
{%- endif %}
{%- endif %}

{% if "textcat_multilabel" in components %}
[components.textcat_multilabel]
factory = "textcat_multilabel"

{% if optimize == "accuracy" %}
[components.textcat_multilabel.model]
@architectures = "spacy.TextCatEnsemble.v2"
nO = null

[components.textcat_multilabel.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0

[components.textcat_multilabel.model.tok2vec.pooling]
@layers = "reduce_mean.v1"

[components.textcat_multilabel.model.linear_model]
@architectures = "spacy.TextCatBOW.v2"
exclusive_classes = false
ngram_size = 1
no_output_layer = false

{% else -%}
[components.textcat_multilabel.model]
@architectures = "spacy.TextCatCNN.v2"
exclusive_classes = false
nO = null

[components.textcat_multilabel.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0

[components.textcat_multilabel.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
{%- endif %}
{%- endif %}

{# NON-TRANSFORMER PIPELINE #}
{% else -%}
{% if "tok2vec" in full_pipeline -%}
[components.tok2vec]
factory = "tok2vec"

[components.tok2vec.model]
@architectures = "spacy.Tok2Vec.v2"

[components.tok2vec.model.embed]
@architectures = "spacy.MultiHashEmbed.v2"
width = ${components.tok2vec.model.encode.width}
attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
rows = [5000, 1000, 2500, 2500]
include_static_vectors = {{ "true" if optimize == "accuracy" else "false" }}

[components.tok2vec.model.encode]
@architectures = "spacy.MaxoutWindowEncoder.v2"
width = {{ 96 if optimize == "efficiency" else 256 }}
depth = {{ 4 if optimize == "efficiency" else 8 }}
window_size = 1
maxout_pieces = 3
{% endif -%}

{% if "morphologizer" in components %}
[components.morphologizer]
factory = "morphologizer"
label_smoothing = 0.05

[components.morphologizer.model]
@architectures = "spacy.Tagger.v2"
nO = null

[components.morphologizer.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}
{%- endif %}

{% if "tagger" in components %}
[components.tagger]
factory = "tagger"
label_smoothing = 0.05

[components.tagger.model]
@architectures = "spacy.Tagger.v2"
nO = null

[components.tagger.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}
{%- endif %}

{% if "parser" in components -%}
[components.parser]
factory = "parser"

[components.parser.model]
@architectures = "spacy.TransitionBasedParser.v2"
state_type = "parser"
extra_state_tokens = false
hidden_width = 128
maxout_pieces = 3
use_upper = true
nO = null

[components.parser.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}
{%- endif %}

{% if "ner" in components %}
[components.ner]
factory = "ner"

[components.ner.model]
@architectures = "spacy.TransitionBasedParser.v2"
state_type = "ner"
extra_state_tokens = false
hidden_width = 64
maxout_pieces = 2
use_upper = true
nO = null

[components.ner.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}
{% endif %}

{% if "span_finder" in components %}
[components.span_finder]
factory = "span_finder"
max_length = 25
min_length = null
scorer = {"@scorers":"spacy.span_finder_scorer.v1"}
spans_key = "sc"
threshold = 0.5

[components.span_finder.model]
@architectures = "spacy.SpanFinder.v1"

[components.span_finder.model.scorer]
@layers = "spacy.LinearLogistic.v1"
nO = 2

[components.span_finder.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
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 "spancat_singlelabel" in components %}
[components.spancat_singlelabel]
factory = "spancat_singlelabel"
negative_weight = 1.0
allow_overlap = true
scorer = {"@scorers":"spacy.spancat_scorer.v1"}
spans_key = "sc"

[components.spancat_singlelabel.model]
@architectures = "spacy.SpanCategorizer.v1"

[components.spancat_singlelabel.model.reducer]
@layers = "spacy.mean_max_reducer.v1"
hidden_size = 128

[components.spancat_singlelabel.model.scorer]
@layers = "Softmax.v2"

[components.spancat_singlelabel.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}

[components.spancat_singlelabel.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"
get_candidates = {"@misc":"spacy.CandidateGenerator.v1"}
incl_context = true
incl_prior = true

[components.entity_linker.model]
@architectures = "spacy.EntityLinker.v2"
nO = null

[components.entity_linker.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}
{% endif %}

{% if "textcat" in components %}
[components.textcat]
factory = "textcat"

{% if optimize == "accuracy" %}
[components.textcat.model]
@architectures = "spacy.TextCatEnsemble.v2"
nO = null

[components.textcat.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}

[components.textcat.model.linear_model]
@architectures = "spacy.TextCatBOW.v2"
exclusive_classes = true
ngram_size = 1
no_output_layer = false

{% else -%}
[components.textcat.model]
@architectures = "spacy.TextCatBOW.v2"
exclusive_classes = true
ngram_size = 1
no_output_layer = false
{%- endif %}
{%- endif %}

{% if "textcat_multilabel" in components %}
[components.textcat_multilabel]
factory = "textcat_multilabel"

{% if optimize == "accuracy" %}
[components.textcat_multilabel.model]
@architectures = "spacy.TextCatEnsemble.v2"
nO = null

[components.textcat_multilabel.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}

[components.textcat_multilabel.model.linear_model]
@architectures = "spacy.TextCatBOW.v2"
exclusive_classes = false
ngram_size = 1
no_output_layer = false

{% else -%}
[components.textcat_multilabel.model]
@architectures = "spacy.TextCatBOW.v2"
exclusive_classes = false
ngram_size = 1
no_output_layer = false
{%- endif %}
{%- endif %}
{% endif %}

{% for pipe in components %}
{% if pipe not in listener_components %}
{# Other components defined by the user: we just assume they're factories #}
[components.{{ pipe }}]
factory = "{{ pipe }}"
{% endif %}
{% endfor %}

[corpora]

[corpora.train]
@readers = "spacy.Corpus.v1"
path = ${paths.train}
max_length = 0

[corpora.dev]
@readers = "spacy.Corpus.v1"
path = ${paths.dev}
max_length = 0

[training]
{% if use_transformer -%}
accumulate_gradient = {{ transformer["size_factor"] }}
{% endif -%}
dev_corpus = "corpora.dev"
train_corpus = "corpora.train"

[training.optimizer]
@optimizers = "Adam.v1"

{% if use_transformer -%}
[training.optimizer.learn_rate]
@schedules = "warmup_linear.v1"
warmup_steps = 250
total_steps = 20000
initial_rate = 5e-5
{% endif %}

{% if use_transformer %}
[training.batcher]
@batchers = "spacy.batch_by_padded.v1"
discard_oversize = true
size = 2000
buffer = 256
{%- else %}
[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
{% endif %}

[initialize]
vectors = ${paths.vectors}