{# 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" -%}
{%- set transformer = transformer_data[optimize] if use_transformer else {} -%}
[paths]
train = null
dev = null

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

[nlp]
lang = "{{ lang }}"
{%- if "tagger" in components or "morphologizer" in components or "parser" in components or "ner" in components or "entity_linker" in components or (("textcat" in components or "textcat_multilabel" in components) and optimize == "accuracy") -%}
{%- 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.v1"
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.v1"
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.v1"
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 "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.v1"
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.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-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.TextCatBOW.v2"
exclusive_classes = false
ngram_size = 1
no_output_layer = false
{%- endif %}
{%- endif %}

{# NON-TRANSFORMER PIPELINE #}
{% else -%}

[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}
{% if has_letters -%}
attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
rows = [5000, 2500, 2500, 2500]
{% else -%}
attrs = ["ORTH", "SHAPE"]
rows = [5000, 2500]
{% endif -%}
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

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

[components.morphologizer.model]
@architectures = "spacy.Tagger.v1"
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"

[components.tagger.model]
@architectures = "spacy.Tagger.v1"
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 "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.v1"
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 ["tagger", "morphologizer", "parser", "ner", "textcat", "textcat_multilabel", "entity_linker"] %}
{# 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]
{% if use_transformer or optimize == "efficiency" or not word_vectors -%}
vectors = null
{% else -%}
vectors = "{{ word_vectors }}"
{% endif -%}