def generate_meta(model_path, existing_meta, msg):
meta = existing_meta or {}
settings = [
("lang", "Model language", meta.get("lang", "en")),
("name", "Model name", meta.get("name", "model")),
("version", "Model version", meta.get("version", "0.0.0")),
("spacy_version", "Required spaCy version", ">=%s,<3.0.0" % about.__version__),
("description", "Model description", meta.get("description", False)),
("author", "Author", meta.get("author", False)),
("email", "Author email", meta.get("email", False)),
("url", "Author website", meta.get("url", False)),
("license", "License", meta.get("license", "CC BY-SA 3.0")),
]
nlp = util.load_model_from_path(Path(model_path))
meta["pipeline"] = nlp.pipe_names
meta["vectors"] = {
"width": nlp.vocab.vectors_length,
"vectors": len(nlp.vocab.vectors),
"keys": nlp.vocab.vectors.n_keys,
"name": nlp.vocab.vectors.name,
}
msg.divider("Generating meta.json")
msg.text(
"Enter the package settings for your model. The following information "
"will be read from your model data: pipeline, vectors."
)
for setting, desc, default in settings:
response = get_raw_input(desc, default)
meta[setting] = default if response == "" and default else response
if about.__title__ != "spacy":
meta["parent_package"] = about.__title__
return meta
def generate_meta(model_path, existing_meta, msg):
meta = existing_meta or {}
settings = [
("lang", "Model language", meta.get("lang", "en")),
("name", "Model name", meta.get("name", "model")),
("version", "Model version", meta.get("version", "0.0.0")),
("spacy_version", "Required spaCy version", ">=%s,<3.0.0" % about.__version__),
("description", "Model description", meta.get("description", False)),
("author", "Author", meta.get("author", False)),
("email", "Author email", meta.get("email", False)),
("url", "Author website", meta.get("url", False)),
("license", "License", meta.get("license", "CC BY-SA 3.0")),
]
nlp = util.load_model_from_path(Path(model_path))
meta["pipeline"] = nlp.pipe_names
meta["vectors"] = {
"width": nlp.vocab.vectors_length,
"vectors": len(nlp.vocab.vectors),
"keys": nlp.vocab.vectors.n_keys,
"name": nlp.vocab.vectors.name,
}
msg.divider("Generating meta.json")
msg.text(
"Enter the package settings for your model. The following information "
"will be read from your model data: pipeline, vectors."
)
for setting, desc, default in settings:
response = get_raw_input(desc, default)
meta[setting] = default if response == "" and default else response
if about.__title__ != "spacy":
meta["parent_package"] = about.__title__
return meta
def generate_meta(existing_meta: Dict[str, Any],
msg: Printer) -> Dict[str, Any]:
meta = existing_meta or {}
settings = [
("lang", "Pipeline language", meta.get("lang", "en")),
("name", "Pipeline name", meta.get("name", "pipeline")),
("version", "Package version", meta.get("version", "0.0.0")),
("description", "Package description", meta.get("description", None)),
("author", "Author", meta.get("author", None)),
("email", "Author email", meta.get("email", None)),
("url", "Author website", meta.get("url", None)),
("license", "License", meta.get("license", "MIT")),
]
msg.divider("Generating meta.json")
msg.text(
"Enter the package settings for your pipeline. The following information "
"will be read from your pipeline data: pipeline, vectors.")
for setting, desc, default in settings:
response = get_raw_input(desc, default)
meta[setting] = default if response == "" and default else response
return meta