mirror of
https://github.com/explosion/spaCy.git
synced 2025-11-11 13:25:43 +03:00
53687b5bca
* [wip] Update
* [wip] Update
* Add initial port
* [wip] Update
* Fix all imports
* Add spancat_exclusive to pipeline
* [WIP] Update
* [ci skip] Add breakpoint for debugging
* Use spacy.SpanCategorizer.v1 as default archi
* Update spacy/pipeline/spancat_exclusive.py
Co-authored-by: kadarakos <kadar.akos@gmail.com>
* [ci skip] Small updates
* Use Softmax v2 directly from thinc
* Cache the label map
* Fix mypy errors
However, I ignored line 370 because it opened up a bunch of type errors
that might be trickier to solve and might lead to a more complicated
codebase.
* avoid multiplication with 1.0
Co-authored-by: kadarakos <kadar.akos@gmail.com>
* Update spacy/pipeline/spancat_exclusive.py
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Update component versions to v2
* Add scorer to docstring
* Add _n_labels property to SpanCategorizer
Instead of using len(self.labels) in initialize() I am using a private
property self._n_labels. This achieves implementation parity and allows
me to delete the whole initialize() method for spancat_exclusive (since
it's now the same with spancat).
* Inherit from SpanCat instead of TrainablePipe
This commit changes the inheritance structure of Exclusive_Spancat,
now it's inheriting from SpanCategorizer than TrainablePipe. This
allows me to remove duplicate methods that are already present in
the parent function.
* Revert documentation link to spancat
* Fix init call for exclusive spancat
* Update spacy/pipeline/spancat_exclusive.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Import Suggester from spancat
* Include zero_init.v1 for spancat
* Implement _allow_extra_label to use _n_labels
To ensure that spancat / spancat_exclusive cannot be resized after
initialization, I inherited the _allow_extra_label() method from
spacy/pipeline/trainable_pipe.pyx and used self._n_labels instead
of len(self.labels) for checking.
I think that changing it locally is a better solution rather than
forcing each class that inherits TrainablePipe to use the self._n_labels
attribute.
Also note that I turned-off black formatting in this block of code
because it reads better without the overhang.
* Extend existing tests to spancat_exclusive
In this commit, I extended the existing tests for spancat to include
spancat_exclusive. I parametrized the test functions with 'name'
(similar var name with textcat and textcat_multilabel) for each
applicable test.
TODO: Add overfitting tests for spancat_exclusive
* Update documentation for spancat
* Turn on formatting for allow_extra_label
* Remove initializers in default config
* Use DEFAULT_EXCL_SPANCAT_MODEL
I also renamed spancat_exclusive_default_config into
spancat_excl_default_config because black does some not pretty
formatting changes.
* Update documentation
Update grammar and usage
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Clarify docstring for Exclusive_SpanCategorizer
* Remove mypy ignore and typecast labels to list
* Fix documentation API
* Use a single variable for tests
* Update defaults for number of rows
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Put back initializers in spancat config
Whenever I remove model.scorer.init_w and model.scorer.init_b,
I encounter an error in the test:
SystemError: <method '__getitem__' of 'dict' objects> returned a result
with an error set.
My Thinc version is 8.1.5, but I can't seem to check what's causing the
error.
* Update spancat_exclusive docstring
* Remove init_W and init_B parameters
This commit is expected to fail until the new Thinc release.
* Require thinc>=8.1.6 for serializable Softmax defaults
* Handle zero suggestions to make tests pass
I'm not sure if this is the most elegant solution. But what should
happen is that the _make_span_group function MUST return an empty
SpanGroup if there are no suggestions.
The error happens when the 'scores' variable is empty. We cannot
get the 'predicted' and other downstream vars.
* Better approach for handling zero suggestions
* Update website/docs/api/spancategorizer.md
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update spancategorizer headers
* Apply suggestions from code review
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Add default value in negative_weight in docs
* Add default value in allow_overlap in docs
* Update how spancat_exclusive is constructed
In this commit, I added the following:
- Put the default values of negative_weight and allow_overlap
in the default_config dictionary.
- Rename make_spancat -> make_exclusive_spancat
* Run prettier on spancategorizer.mdx
* Change exactly one -> at most one
* Add suggester documentation in Exclusive_SpanCategorizer
* Add suggester to spancat docstrings
* merge multilabel and singlelabel spancat
* rename spancat_exclusive to singlelable
* wire up different make_spangroups for single and multilabel
* black
* black
* add docstrings
* more docstring and fix negative_label
* don't rely on default arguments
* black
* remove spancat exclusive
* replace single_label with add_negative_label and adjust inference
* mypy
* logical bug in configuration check
* add spans.attrs[scores]
* single label make_spangroup test
* bugfix
* black
* tests for make_span_group with negative labels
* refactor make_span_group
* black
* Update spacy/tests/pipeline/test_spancat.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* remove duplicate declaration
* Update spacy/pipeline/spancat.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* raise error instead of just print
* make label mapper private
* update docs
* run prettier
* Update website/docs/api/spancategorizer.mdx
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update website/docs/api/spancategorizer.mdx
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update spacy/pipeline/spancat.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update spacy/pipeline/spancat.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update spacy/pipeline/spancat.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update spacy/pipeline/spancat.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* don't keep recomputing self._label_map for each span
* typo in docs
* Intervals to private and document 'name' param
* Update spacy/pipeline/spancat.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update spacy/pipeline/spancat.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* add Tag to new features
* replace tags
* revert
* revert
* revert
* revert
* Update website/docs/api/spancategorizer.mdx
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update website/docs/api/spancategorizer.mdx
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* prettier
* Fix merge
* Update website/docs/api/spancategorizer.mdx
* remove references to 'single_label'
* remove old paragraph
* Add spancat_singlelabel to config template
* Format
* Extend init config tests
---------
Co-authored-by: kadarakos <kadar.akos@gmail.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
600 lines
15 KiB
Django/Jinja
600 lines
15 KiB
Django/Jinja
{# This is a template for training configs used for the quickstart widget in
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the docs and the init config command. It encodes various best practices and
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can help generate the best possible configuration, given a user's requirements. #}
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{%- set use_transformer = hardware != "cpu" and transformer_data -%}
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{%- set transformer = transformer_data[optimize] if use_transformer else {} -%}
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{%- set listener_components = ["tagger", "morphologizer", "parser", "ner", "textcat", "textcat_multilabel", "entity_linker", "spancat", "spancat_singlelabel", "trainable_lemmatizer"] -%}
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[paths]
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train = null
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dev = null
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{% if use_transformer or optimize == "efficiency" or not word_vectors -%}
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vectors = null
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{% else -%}
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vectors = "{{ word_vectors }}"
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{% endif -%}
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[system]
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{% if use_transformer -%}
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gpu_allocator = "pytorch"
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{% else -%}
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gpu_allocator = null
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{% endif %}
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[nlp]
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lang = "{{ lang }}"
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{%- set has_textcat = ("textcat" in components or "textcat_multilabel" in components) -%}
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{%- set with_accuracy = optimize == "accuracy" -%}
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{# The BOW textcat doesn't need a source of features, so it can omit the
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tok2vec/transformer. #}
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{%- set with_accuracy_or_transformer = (use_transformer or with_accuracy) -%}
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{%- set textcat_needs_features = has_textcat and with_accuracy_or_transformer -%}
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{%- if ("tagger" in components or "morphologizer" in components or "parser" in components or "ner" 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) -%}
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{%- set full_pipeline = ["transformer" if use_transformer else "tok2vec"] + components -%}
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{%- else -%}
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{%- set full_pipeline = components -%}
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{%- endif %}
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pipeline = {{ full_pipeline|pprint()|replace("'", '"')|safe }}
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batch_size = {{ 128 if hardware == "gpu" else 1000 }}
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[components]
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{# TRANSFORMER PIPELINE #}
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{%- if use_transformer -%}
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[components.transformer]
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factory = "transformer"
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[components.transformer.model]
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@architectures = "spacy-transformers.TransformerModel.v3"
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name = "{{ transformer["name"] }}"
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tokenizer_config = {"use_fast": true}
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[components.transformer.model.get_spans]
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@span_getters = "spacy-transformers.strided_spans.v1"
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window = 128
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stride = 96
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{% if "morphologizer" in components %}
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[components.morphologizer]
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factory = "morphologizer"
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[components.morphologizer.model]
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@architectures = "spacy.Tagger.v2"
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nO = null
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[components.morphologizer.model.tok2vec]
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@architectures = "spacy-transformers.TransformerListener.v1"
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grad_factor = 1.0
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[components.morphologizer.model.tok2vec.pooling]
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@layers = "reduce_mean.v1"
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{%- endif %}
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{% if "tagger" in components %}
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[components.tagger]
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factory = "tagger"
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[components.tagger.model]
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@architectures = "spacy.Tagger.v2"
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nO = null
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[components.tagger.model.tok2vec]
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@architectures = "spacy-transformers.TransformerListener.v1"
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grad_factor = 1.0
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[components.tagger.model.tok2vec.pooling]
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@layers = "reduce_mean.v1"
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{%- endif %}
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{% if "parser" in components -%}
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[components.parser]
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factory = "parser"
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[components.parser.model]
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@architectures = "spacy.TransitionBasedParser.v2"
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state_type = "parser"
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extra_state_tokens = false
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hidden_width = 128
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maxout_pieces = 3
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use_upper = false
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nO = null
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[components.parser.model.tok2vec]
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@architectures = "spacy-transformers.TransformerListener.v1"
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grad_factor = 1.0
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[components.parser.model.tok2vec.pooling]
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@layers = "reduce_mean.v1"
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{%- endif %}
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{% if "ner" in components -%}
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[components.ner]
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factory = "ner"
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[components.ner.model]
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@architectures = "spacy.TransitionBasedParser.v2"
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state_type = "ner"
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extra_state_tokens = false
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hidden_width = 64
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maxout_pieces = 2
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use_upper = false
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nO = null
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[components.ner.model.tok2vec]
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@architectures = "spacy-transformers.TransformerListener.v1"
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grad_factor = 1.0
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[components.ner.model.tok2vec.pooling]
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@layers = "reduce_mean.v1"
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{% endif -%}
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{% if "spancat" in components -%}
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[components.spancat]
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factory = "spancat"
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max_positive = null
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scorer = {"@scorers":"spacy.spancat_scorer.v1"}
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spans_key = "sc"
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threshold = 0.5
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[components.spancat.model]
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@architectures = "spacy.SpanCategorizer.v1"
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[components.spancat.model.reducer]
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@layers = "spacy.mean_max_reducer.v1"
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hidden_size = 128
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[components.spancat.model.scorer]
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@layers = "spacy.LinearLogistic.v1"
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nO = null
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nI = null
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[components.spancat.model.tok2vec]
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@architectures = "spacy-transformers.TransformerListener.v1"
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grad_factor = 1.0
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[components.spancat.model.tok2vec.pooling]
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@layers = "reduce_mean.v1"
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[components.spancat.suggester]
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@misc = "spacy.ngram_suggester.v1"
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sizes = [1,2,3]
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{% endif -%}
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{% if "spancat_singlelabel" in components %}
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[components.spancat_singlelabel]
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factory = "spancat_singlelabel"
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negative_weight = 1.0
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allow_overlap = true
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scorer = {"@scorers":"spacy.spancat_scorer.v1"}
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spans_key = "sc"
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[components.spancat_singlelabel.model]
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@architectures = "spacy.SpanCategorizer.v1"
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[components.spancat_singlelabel.model.reducer]
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@layers = "spacy.mean_max_reducer.v1"
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hidden_size = 128
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[components.spancat_singlelabel.model.scorer]
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@layers = "Softmax.v2"
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[components.spancat_singlelabel.model.tok2vec]
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@architectures = "spacy-transformers.TransformerListener.v1"
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grad_factor = 1.0
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[components.spancat_singlelabel.model.tok2vec.pooling]
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@layers = "reduce_mean.v1"
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[components.spancat_singlelabel.suggester]
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@misc = "spacy.ngram_suggester.v1"
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sizes = [1,2,3]
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{% endif %}
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{% if "trainable_lemmatizer" in components -%}
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[components.trainable_lemmatizer]
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factory = "trainable_lemmatizer"
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backoff = "orth"
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min_tree_freq = 3
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overwrite = false
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scorer = {"@scorers":"spacy.lemmatizer_scorer.v1"}
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top_k = 1
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[components.trainable_lemmatizer.model]
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@architectures = "spacy.Tagger.v2"
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nO = null
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normalize = false
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[components.trainable_lemmatizer.model.tok2vec]
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@architectures = "spacy-transformers.TransformerListener.v1"
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grad_factor = 1.0
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[components.trainable_lemmatizer.model.tok2vec.pooling]
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@layers = "reduce_mean.v1"
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{% endif -%}
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{% if "entity_linker" in components -%}
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[components.entity_linker]
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factory = "entity_linker"
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get_candidates = {"@misc":"spacy.CandidateGenerator.v1"}
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incl_context = true
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incl_prior = true
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[components.entity_linker.model]
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@architectures = "spacy.EntityLinker.v2"
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nO = null
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[components.entity_linker.model.tok2vec]
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@architectures = "spacy-transformers.TransformerListener.v1"
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grad_factor = 1.0
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[components.entity_linker.model.tok2vec.pooling]
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@layers = "reduce_mean.v1"
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{% endif -%}
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{% if "textcat" in components %}
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[components.textcat]
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factory = "textcat"
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{% if optimize == "accuracy" %}
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[components.textcat.model]
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@architectures = "spacy.TextCatEnsemble.v2"
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nO = null
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[components.textcat.model.tok2vec]
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@architectures = "spacy-transformers.TransformerListener.v1"
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grad_factor = 1.0
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[components.textcat.model.tok2vec.pooling]
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@layers = "reduce_mean.v1"
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[components.textcat.model.linear_model]
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@architectures = "spacy.TextCatBOW.v2"
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exclusive_classes = true
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ngram_size = 1
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no_output_layer = false
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{% else -%}
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[components.textcat.model]
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@architectures = "spacy.TextCatCNN.v2"
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exclusive_classes = true
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nO = null
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[components.textcat.model.tok2vec]
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@architectures = "spacy-transformers.TransformerListener.v1"
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grad_factor = 1.0
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[components.textcat.model.tok2vec.pooling]
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@layers = "reduce_mean.v1"
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{%- endif %}
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{%- endif %}
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{% if "textcat_multilabel" in components %}
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[components.textcat_multilabel]
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factory = "textcat_multilabel"
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{% if optimize == "accuracy" %}
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[components.textcat_multilabel.model]
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@architectures = "spacy.TextCatEnsemble.v2"
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nO = null
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[components.textcat_multilabel.model.tok2vec]
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@architectures = "spacy-transformers.TransformerListener.v1"
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grad_factor = 1.0
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[components.textcat_multilabel.model.tok2vec.pooling]
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@layers = "reduce_mean.v1"
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[components.textcat_multilabel.model.linear_model]
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@architectures = "spacy.TextCatBOW.v2"
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exclusive_classes = false
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ngram_size = 1
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no_output_layer = false
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{% else -%}
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[components.textcat_multilabel.model]
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@architectures = "spacy.TextCatCNN.v2"
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exclusive_classes = false
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nO = null
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[components.textcat_multilabel.model.tok2vec]
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@architectures = "spacy-transformers.TransformerListener.v1"
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grad_factor = 1.0
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[components.textcat_multilabel.model.tok2vec.pooling]
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@layers = "reduce_mean.v1"
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{%- endif %}
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{%- endif %}
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{# NON-TRANSFORMER PIPELINE #}
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{% else -%}
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{% if "tok2vec" in full_pipeline -%}
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[components.tok2vec]
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factory = "tok2vec"
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[components.tok2vec.model]
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@architectures = "spacy.Tok2Vec.v2"
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[components.tok2vec.model.embed]
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@architectures = "spacy.MultiHashEmbed.v2"
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width = ${components.tok2vec.model.encode.width}
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attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
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rows = [5000, 1000, 2500, 2500]
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include_static_vectors = {{ "true" if optimize == "accuracy" else "false" }}
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[components.tok2vec.model.encode]
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@architectures = "spacy.MaxoutWindowEncoder.v2"
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width = {{ 96 if optimize == "efficiency" else 256 }}
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depth = {{ 4 if optimize == "efficiency" else 8 }}
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window_size = 1
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maxout_pieces = 3
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{% endif -%}
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{% if "morphologizer" in components %}
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[components.morphologizer]
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factory = "morphologizer"
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[components.morphologizer.model]
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@architectures = "spacy.Tagger.v2"
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nO = null
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[components.morphologizer.model.tok2vec]
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@architectures = "spacy.Tok2VecListener.v1"
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width = ${components.tok2vec.model.encode.width}
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{%- endif %}
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{% if "tagger" in components %}
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[components.tagger]
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factory = "tagger"
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[components.tagger.model]
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@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 "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}
|