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Read pipeline and vector data off model in --generate-meta

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This commit is contained in:
ines 2017-10-25 16:03:26 +02:00
parent 273e638183
commit 057954695b
1 changed files with 10 additions and 19 deletions
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29
spacy/cli/package.py
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@ -43,7 +43,7 @@ def package(cmd, input_dir, output_dir, meta_path=None, create_meta=False, force
prints(meta_path, title="Reading meta.json from file")
meta = util.read_json(meta_path)
else:
meta = generate_meta()
meta = generate_meta(input_dir)
meta = validate_meta(meta, ['lang', 'name', 'version'])
model_name = meta['lang'] + '_' + meta['name']
@ -77,7 +77,8 @@ def create_file(file_path, contents):
file_path.open('w', encoding='utf-8').write(contents)
def generate_meta():
def generate_meta(model_path):
meta = {}
settings = [('lang', 'Model language', 'en'),
('name', 'Model name', 'model'),
('version', 'Model version', '0.0.0'),
@ -87,31 +88,21 @@ def generate_meta():
('email', 'Author email', False),
('url', 'Author website', False),
('license', 'License', 'CC BY-NC 3.0')]
prints("Enter the package settings for your model.", title="Generating meta.json")
meta = {}
nlp = util.load_model_from_path(Path(model_path))
meta['pipeline'] = nlp.pipe_names
meta['vectors'] = {'width': nlp.vocab.vectors_length,
'entries': len(nlp.vocab.vectors)}
prints("Enter the package settings for your model. The following "
"information will be read from your model data: pipeline, vectors.",
title="Generating meta.json")
for setting, desc, default in settings:
response = util.get_raw_input(desc, default)
meta[setting] = default if response == '' and default else response
meta['pipeline'] = generate_pipeline()
if about.__title__ != 'spacy':
meta['parent_package'] = about.__title__
return meta
def generate_pipeline():
prints("If set to 'True', the default pipeline is used. If set to 'False', "
"the pipeline will be disabled. Components should be specified as a "
"comma-separated list of component names, e.g. tagger, "
"parser, ner. For more information, see the docs on processing pipelines.",
title="Enter your model's pipeline components")
pipeline = util.get_raw_input("Pipeline components", True)
subs = {'True': True, 'False': False}
if pipeline in subs:
return subs[pipeline]
else:
return [p.strip() for p in pipeline.split(',')]
def validate_meta(meta, keys):
for key in keys:
if key not in meta or meta[key] == '':