mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 03:19:13 +03:00
da7ad97519
* Update `TextCatBOW` to use the fixed `SparseLinear` layer A while ago, we fixed the `SparseLinear` layer to use all available parameters: https://github.com/explosion/thinc/pull/754 This change updates `TextCatBOW` to `v3` which uses the new `SparseLinear_v2` layer. This results in a sizeable improvement on a text categorization task that was tested. While at it, this `spacy.TextCatBOW.v3` also adds the `length_exponent` option to make it possible to change the hidden size. Ideally, we'd just have an option called `length`. But the way that `TextCatBOW` uses hashes results in a non-uniform distribution of parameters when the length is not a power of two. * Replace TexCatBOW `length_exponent` parameter by `length` We now round up the length to the next power of two if it isn't a power of two. * Remove some tests for TextCatBOW.v2 * Fix missing import
652 lines
16 KiB
Django/Jinja
652 lines
16 KiB
Django/Jinja
{# 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.v3"
|
|
exclusive_classes = true
|
|
length = 262144
|
|
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.v3"
|
|
exclusive_classes = false
|
|
length = 262144
|
|
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.v3"
|
|
exclusive_classes = true
|
|
length = 262144
|
|
ngram_size = 1
|
|
no_output_layer = false
|
|
|
|
{% else -%}
|
|
[components.textcat.model]
|
|
@architectures = "spacy.TextCatBOW.v3"
|
|
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.v3"
|
|
exclusive_classes = false
|
|
length = 262144
|
|
ngram_size = 1
|
|
no_output_layer = false
|
|
|
|
{% else -%}
|
|
[components.textcat_multilabel.model]
|
|
@architectures = "spacy.TextCatBOW.v3"
|
|
exclusive_classes = false
|
|
length = 262144
|
|
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}
|