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Merge branch 'develop' of https://github.com/explosion/spaCy into develop

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Matthew Honnibal 2017-10-30 19:44:58 +01:00
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{"lang": "en", "settings": {"oov_prob": -20.502029418945312}}
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{"orth": "as", "id": 36, "lower": "as", "norm": "as", "shape": "xx", "prefix": "a", "suffix": "as", "length": 2, "cluster": "212", "prob": -5.53448486328125, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "like", "id": 37, "lower": "like", "norm": "like", "shape": "xxxx", "prefix": "l", "suffix": "ike", "length": 4, "cluster": "1684", "prob": -5.610429763793945, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "just", "id": 38, "lower": "just", "norm": "just", "shape": "xxxx", "prefix": "j", "suffix": "ust", "length": 4, "cluster": "31978", "prob": -5.630868434906006, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
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{"orth": "from", "id": 57, "lower": "from", "norm": "from", "shape": "xxxx", "prefix": "f", "suffix": "rom", "length": 4, "cluster": "380", "prob": -6.010132312774658, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
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{"orth": "]", "id": 61, "lower": "]", "norm": "]", "shape": "]", "prefix": "]", "suffix": "]", "length": 1, "cluster": "0", "prob": -6.0386552810668945, "is_alpha": false, "is_ascii": true, "is_digit": false, "is_lower": false, "is_punct": true, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": true}
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{"orth": "more", "id": 63, "lower": "more", "norm": "more", "shape": "xxxx", "prefix": "m", "suffix": "ore", "length": 4, "cluster": "1514", "prob": -6.081598281860352, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
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{"orth": "think", "id": 67, "lower": "think", "norm": "think", "shape": "xxxx", "prefix": "t", "suffix": "ink", "length": 5, "cluster": "1674", "prob": -6.180924892425537, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "will", "id": 68, "lower": "will", "norm": "will", "shape": "xxxx", "prefix": "w", "suffix": "ill", "length": 4, "cluster": "442", "prob": -6.199834823608398, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "them", "id": 69, "lower": "them", "norm": "them", "shape": "xxxx", "prefix": "t", "suffix": "hem", "length": 4, "cluster": "5994", "prob": -6.2177276611328125, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
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{"orth": "when", "id": 72, "lower": "when", "norm": "when", "shape": "xxxx", "prefix": "w", "suffix": "hen", "length": 4, "cluster": "16340", "prob": -6.2623114585876465, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "You", "id": 73, "lower": "you", "norm": "You", "shape": "Xxx", "prefix": "Y", "suffix": "You", "length": 3, "cluster": "858", "prob": -6.276494026184082, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": false, "is_punct": false, "is_space": false, "is_title": true, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "...", "id": 74, "lower": "...", "norm": "...", "shape": "...", "prefix": ".", "suffix": "...", "length": 3, "cluster": "966", "prob": -6.278521537780762, "is_alpha": false, "is_ascii": true, "is_digit": false, "is_lower": false, "is_punct": true, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "some", "id": 75, "lower": "some", "norm": "some", "shape": "xxxx", "prefix": "s", "suffix": "ome", "length": 4, "cluster": "239", "prob": -6.318882465362549, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "has", "id": 76, "lower": "has", "norm": "has", "shape": "xxx", "prefix": "h", "suffix": "has", "length": 3, "cluster": "890", "prob": -6.325605392456055, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "because", "id": 77, "lower": "because", "norm": "because", "shape": "xxxx", "prefix": "b", "suffix": "use", "length": 7, "cluster": "980", "prob": -6.349620342254639, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "know", "id": 78, "lower": "know", "norm": "know", "shape": "xxxx", "prefix": "k", "suffix": "now", "length": 4, "cluster": "3722", "prob": -6.368943214416504, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "really", "id": 79, "lower": "really", "norm": "really", "shape": "xxxx", "prefix": "r", "suffix": "lly", "length": 6, "cluster": "7802", "prob": -6.370757102966309, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "by", "id": 80, "lower": "by", "norm": "by", "shape": "xx", "prefix": "b", "suffix": "by", "length": 2, "cluster": "252", "prob": -6.375086784362793, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "time", "id": 81, "lower": "time", "norm": "time", "shape": "xxxx", "prefix": "t", "suffix": "ime", "length": 4, "cluster": "477", "prob": -6.3782219886779785, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "did", "id": 82, "lower": "did", "norm": "did", "shape": "xxx", "prefix": "d", "suffix": "did", "length": 3, "cluster": "8186", "prob": -6.389003753662109, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "no", "id": 83, "lower": "no", "norm": "no", "shape": "xx", "prefix": "n", "suffix": "no", "length": 2, "cluster": "4074", "prob": -6.402691841125488, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "had", "id": 84, "lower": "had", "norm": "had", "shape": "xxx", "prefix": "h", "suffix": "had", "length": 3, "cluster": "1914", "prob": -6.45427131652832, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "their", "id": 85, "lower": "their", "norm": "their", "shape": "xxxx", "prefix": "t", "suffix": "eir", "length": 5, "cluster": "187", "prob": -6.461463928222656, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "If", "id": 86, "lower": "if", "norm": "If", "shape": "Xx", "prefix": "I", "suffix": "If", "length": 2, "cluster": "190", "prob": -6.469156742095947, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": false, "is_punct": false, "is_space": false, "is_title": true, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "how", "id": 87, "lower": "how", "norm": "how", "shape": "xxx", "prefix": "h", "suffix": "how", "length": 3, "cluster": "10218", "prob": -6.496722221374512, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "does", "id": 88, "lower": "does", "norm": "does", "shape": "xxxx", "prefix": "d", "suffix": "oes", "length": 4, "cluster": "4090", "prob": -6.500738143920898, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "who", "id": 89, "lower": "who", "norm": "who", "shape": "xxx", "prefix": "w", "suffix": "who", "length": 3, "cluster": "410", "prob": -6.504637241363525, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "than", "id": 90, "lower": "than", "norm": "than", "shape": "xxxx", "prefix": "t", "suffix": "han", "length": 4, "cluster": "106", "prob": -6.512253761291504, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "good", "id": 91, "lower": "good", "norm": "good", "shape": "xxxx", "prefix": "g", "suffix": "ood", "length": 4, "cluster": "551", "prob": -6.518923759460449, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "only", "id": 92, "lower": "only", "norm": "only", "shape": "xxxx", "prefix": "o", "suffix": "nly", "length": 4, "cluster": "15594", "prob": -6.535442352294922, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "his", "id": 93, "lower": "his", "norm": "his", "shape": "xxx", "prefix": "h", "suffix": "his", "length": 3, "cluster": "123", "prob": -6.574275016784668, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "much", "id": 94, "lower": "much", "norm": "much", "shape": "xxxx", "prefix": "m", "suffix": "uch", "length": 4, "cluster": "2794", "prob": -6.584301948547363, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": ";", "id": 95, "lower": ";", "norm": ";", "shape": ";", "prefix": ";", "suffix": ";", "length": 1, "cluster": "36", "prob": -6.586422920227051, "is_alpha": false, "is_ascii": true, "is_digit": false, "is_lower": false, "is_punct": true, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "'ve", "id": 96, "lower": "'ve", "norm": "'ve", "shape": "'xx", "prefix": "'", "suffix": "'ve", "length": 3, "cluster": "1018", "prob": -6.593011379241943, "is_alpha": false, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}
{"orth": "could", "id": 97, "lower": "could", "norm": "could", "shape": "xxxx", "prefix": "c", "suffix": "uld", "length": 5, "cluster": "954", "prob": -6.595959186553955, "is_alpha": true, "is_ascii": true, "is_digit": false, "is_lower": true, "is_punct": false, "is_space": false, "is_title": false, "is_upper": false, "like_url": false, "like_num": false, "like_email": false, "is_stop": false, "is_oov": false, "is_quote": false, "is_left_punct": false, "is_right_punct": false}

2
spacy/__main__.py
View File

@ -19,7 +19,7 @@ if __name__ == '__main__':
'convert': convert,
'package': package,
'model': model,
'model': vocab,
'vocab': vocab,
'profile': profile,
'validate': validate
}

12
spacy/cli/evaluate.py
View File

@ -17,14 +17,14 @@ numpy.random.seed(0)
@plac.annotations(
model=("Model name or path", "positional", None, str),
data_path=("Location of JSON-formatted evaluation data", "positional",
model=("model name or path", "positional", None, str),
data_path=("location of JSON-formatted evaluation data", "positional",
None, str),
gold_preproc=("Use gold preprocessing", "flag", "G", bool),
gpu_id=("Use GPU", "option", "g", int),
displacy_path=("Directory to output rendered parses as HTML", "option",
gold_preproc=("use gold preprocessing", "flag", "G", bool),
gpu_id=("use GPU", "option", "g", int),
displacy_path=("directory to output rendered parses as HTML", "option",
"dp", str),
displacy_limit=("Limit of parses to render as HTML", "option", "dl", int))
displacy_limit=("limit of parses to render as HTML", "option", "dl", int))
def evaluate(cmd, model, data_path, gpu_id=-1, gold_preproc=False,
displacy_path=None, displacy_limit=25):
"""

40
spacy/cli/package.py
View File

@ -16,10 +16,11 @@ from .. import about
input_dir=("directory with model data", "positional", None, str),
output_dir=("output parent directory", "positional", None, str),
meta_path=("path to meta.json", "option", "m", str),
create_meta=("create meta.json, even if one exists in directory", "flag",
"c", bool),
force=("force overwriting of existing folder in output directory", "flag",
"f", bool))
create_meta=("create meta.json, even if one exists in directory – if "
"existing meta is found, entries are shown as defaults in "
"the command line prompt", "flag", "c", bool),
force=("force overwriting of existing model directory in output directory",
"flag", "f", bool))
def package(cmd, input_dir, output_dir, meta_path=None, create_meta=False,
force=False):
"""
@ -41,13 +42,13 @@ def package(cmd, input_dir, output_dir, meta_path=None, create_meta=False,
template_manifest = get_template('MANIFEST.in')
template_init = get_template('xx_model_name/__init__.py')
meta_path = meta_path or input_path / 'meta.json'
if not create_meta and meta_path.is_file():
prints(meta_path, title="Reading meta.json from file")
if meta_path.is_file():
meta = util.read_json(meta_path)
else:
meta = generate_meta(input_dir)
if not create_meta: # only print this if user doesn't want to overwrite
prints(meta_path, title="Loaded meta.json from file")
else:
meta = generate_meta(input_dir, meta)
meta = validate_meta(meta, ['lang', 'name', 'version'])
model_name = meta['lang'] + '_' + meta['name']
model_name_v = model_name + '-' + meta['version']
main_path = output_path / model_name_v
@ -82,18 +83,19 @@ def create_file(file_path, contents):
file_path.open('w', encoding='utf-8').write(contents)
def generate_meta(model_path):
meta = {}
settings = [('lang', 'Model language', 'en'),
('name', 'Model name', 'model'),
('version', 'Model version', '0.0.0'),
def generate_meta(model_path, existing_meta):
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', False),
('author', 'Author', False),
('email', 'Author email', False),
('url', 'Author website', False),
('license', 'License', 'CC BY-NC 3.0')]
('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,

38
spacy/cli/vocab.py
View File

@ -1,31 +1,33 @@
'''Compile a vocabulary from a lexicon jsonl file and word vectors.'''
# coding: utf8
from __future__ import unicode_literals
from pathlib import Path
import plac
import json
import spacy
import numpy
from spacy.util import ensure_path
from pathlib import Path
from ..util import prints, ensure_path
@plac.annotations(
lang=("model language", "positional", None, str),
output_dir=("output directory to store model in", "positional", None, str),
output_dir=("model output directory", "positional", None, Path),
lexemes_loc=("location of JSONL-formatted lexical data", "positional",
None, str),
vectors_loc=("location of vectors data, as numpy .npz (optional)",
"positional", None, str),
version=("Model version", "option", "V", str),
)
def make_vocab(lang, output_dir, lexemes_loc, vectors_loc=None, version=None):
out_dir = ensure_path(output_dir)
jsonl_loc = ensure_path(lexemes_loc)
None, Path),
vectors_loc=("optional: location of vectors data, as numpy .npz",
"positional", None, str))
def make_vocab(cmd, lang, output_dir, lexemes_loc, vectors_loc=None):
"""Compile a vocabulary from a lexicon jsonl file and word vectors."""
if not lexemes_loc.exists():
prints(lexemes_loc, title="Can't find lexical data", exits=1)
vectors_loc = ensure_path(vectors_loc)
nlp = spacy.blank(lang)
for word in nlp.vocab:
word.rank = 0
with jsonl_loc.open() as file_:
lex_added = 0
vec_added = 0
with lexemes_loc.open() as file_:
for line in file_:
if line.strip():
attrs = json.loads(line)
@ -35,14 +37,18 @@ def make_vocab(lang, output_dir, lexemes_loc, vectors_loc=None, version=None):
lex = nlp.vocab[attrs['orth']]
lex.set_attrs(**attrs)
assert lex.rank == attrs['id']
lex_added += 1
if vectors_loc is not None:
vector_data = numpy.load(open(vectors_loc, 'rb'))
nlp.vocab.clear_vectors(width=vector_data.shape[1])
added = 0
for word in nlp.vocab:
if word.rank:
nlp.vocab.vectors.add(word.orth_, row=word.rank,
vector=vector_data[word.rank])
added += 1
nlp.to_disk(out_dir)
vec_added += 1
if not output_dir.exists():
output_dir.mkdir()
nlp.to_disk(output_dir)
prints("{} entries, {} vectors".format(lex_added, vec_added), output_dir,
title="Sucessfully compiled vocab and vectors, and saved model")
return nlp

2
spacy/language.py
View File

@ -154,6 +154,8 @@ class Language(object):
self._meta.setdefault('email', '')
self._meta.setdefault('url', '')
self._meta.setdefault('license', '')
self._meta['vectors'] = {'width': self.vocab.vectors_length,
'entries': len(self.vocab.vectors)}
self._meta['pipeline'] = self.pipe_names
return self._meta

12
spacy/vocab.pyx
View File

@ -252,7 +252,7 @@ cdef class Vocab:
"""Reduce the current vector table to `nr_row` unique entries. Words
mapped to the discarded vectors will be remapped to the closest vector
among those remaining.
For example, suppose the original table had vectors for the words:
['sat', 'cat', 'feline', 'reclined']. If we prune the vector table to,
two rows, we would discard the vectors for 'feline' and 'reclined'.
@ -263,6 +263,15 @@ cdef class Vocab:
The similarities are judged by cosine. The original vectors may
be large, so the cosines are calculated in minibatches, to reduce
memory usage.
nr_row (int): The number of rows to keep in the vector table.
batch_size (int): Batch of vectors for calculating the similarities.
Larger batch sizes might be faster, while temporarily requiring
more memory.
RETURNS (dict): A dictionary keyed by removed words mapped to
`(string, score)` tuples, where `string` is the entry the removed
word was mapped to, and `score` the similarity score between the
two words.
"""
xp = get_array_module(self.vectors.data)
# Work in batches, to avoid memory problems.
@ -294,6 +303,7 @@ cdef class Vocab:
self.vectors.key2row[key] = neighbours[row-nr_row]
# Make copy, to encourage the original table to be garbage collected.
self.vectors.data = xp.ascontiguousarray(self.vectors.data[:nr_row])
# TODO: return new mapping
def get_vector(self, orth):
"""Retrieve a vector for a word in the vocabulary. Words can be looked

3
website/_includes/_functions.jade
View File

@ -41,9 +41,6 @@
- var comps = path.split('#');
- return "top-level#" + comps[0] + '.' + comps[1];
- }
- else if (path.startsWith('cli#')) {
- return "top-level#" + path.split('#')[1];
- }
- return path;
- }

56
website/api/_annotation/_training.jade
View File

@ -1,5 +1,7 @@
//- 💫 DOCS > API > ANNOTATION > TRAINING
+h(3, "json-input") JSON input format for training
p
| spaCy takes training data in JSON format. The built-in
| #[+api("cli#convert") #[code convert]] command helps you convert the
@ -46,3 +48,57 @@ p
| Treebank:
+github("spacy", "examples/training/training-data.json", false, false, "json")
+h(3, "vocab-jsonl") Lexical data for vocabulary
+tag-new(2)
p
| The populate a model's vocabulary, you can use the
| #[+api("cli#vocab") #[code spacy vocab]] command and load in a
| #[+a("https://jsonlines.readthedocs.io/en/latest/") newline-delimited JSON]
| (JSONL) file containing one lexical entry per line. The first line
| defines the language and vocabulary settings. All other lines are
| expected to be JSON objects describing an individual lexeme. The lexical
| attributes will be then set as attributes on spaCy's
| #[+api("lexeme#attributes") #[code Lexeme]] object. The #[code vocab]
| command outputs a ready-to-use spaCy model with a #[code Vocab]
| containing the lexical data.
+code("First line").
{"lang": "en", "settings": {"oov_prob": -20.502029418945312}}
+code("Entry structure").
{
"orth": string,
"id": int,
"lower": string,
"norm": string,
"shape": string
"prefix": string,
"suffix": string,
"length": int,
"cluster": string,
"prob": float,
"is_alpha": bool,
"is_ascii": bool,
"is_digit": bool,
"is_lower": bool,
"is_punct": bool,
"is_space": bool,
"is_title": bool,
"is_upper": bool,
"like_url": bool,
"like_num": bool,
"like_email": bool,
"is_stop": bool,
"is_oov": bool,
"is_quote": bool,
"is_left_punct": bool,
"is_right_punct": bool
}
p
| Here's an example of the 100 most frequent lexemes in the English
| training data:
+github("spacy", "examples/training/vocab-data.jsonl", false, false, "json")

13
website/api/_data.json
View File

@ -3,8 +3,10 @@
"Overview": {
"Architecture": "./",
"Annotation Specs": "annotation",
"Command Line": "cli",
"Functions": "top-level"
},
"Containers": {
"Doc": "doc",
"Token": "token",
@ -45,14 +47,19 @@
}
},
"cli": {
"title": "Command Line Interface",
"teaser": "Download, train and package models, and debug spaCy.",
"source": "spacy/cli"
},
"top-level": {
"title": "Top-level Functions",
"menu": {
"spacy": "spacy",
"displacy": "displacy",
"Utility Functions": "util",
"Compatibility": "compat",
"Command Line": "cli"
"Compatibility": "compat"
}
},
@ -213,7 +220,7 @@
"Lemmatization": "lemmatization",
"Dependencies": "dependency-parsing",
"Named Entities": "named-entities",
"Training Data": "training"
"Models & Training": "training"
}
}
}

4
website/api/_top-level/_spacy.jade
View File

@ -85,7 +85,9 @@ p
+row
+cell #[code name]
+cell unicode
+cell ISO code of the language class to load.
+cell
| #[+a("https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes") ISO code]
| of the language class to load.
+row
+cell #[code disable]

2
website/api/annotation.jade
View File

@ -99,6 +99,6 @@ p This document describes the target annotations spaCy is trained to predict.
include _annotation/_biluo
+section("training")
+h(2, "json-input") JSON input format for training
+h(2, "training") Models and training data
include _annotation/_training

121
website/api/_top-level/_cli.jade → website/api/cli.jade
View File

@ -1,4 +1,6 @@
//- 💫 DOCS > API > TOP-LEVEL > COMMAND LINE INTERFACE
//- 💫 DOCS > API > COMMAND LINE INTERFACE
include ../_includes/_mixins
p
| As of v1.7.0, spaCy comes with new command line helpers to download and
@ -34,6 +36,13 @@ p
+cell flag
+cell Show help message and available arguments.
+row("foot")
+cell creates
+cell directory, symlink
+cell
| The installed model package in your #[code site-packages]
| directory and a shortcut link as a symlink in #[code spacy/data].
+aside("Downloading best practices")
| The #[code download] command is mostly intended as a convenient,
| interactive wrapper – it performs compatibility checks and prints
@ -86,6 +95,13 @@ p
+cell flag
+cell Show help message and available arguments.
+row("foot")
+cell creates
+cell symlink
+cell
| A shortcut link of the given name as a symlink in
| #[code spacy/data].
+h(3, "info") Info
p
@ -113,6 +129,11 @@ p
+cell flag
+cell Show help message and available arguments.
+row("foot")
+cell prints
+cell #[code stdout]
+cell Information about your spaCy installation.
+h(3, "validate") Validate
+tag-new(2)
@ -129,6 +150,12 @@ p
+code(false, "bash", "$").
spacy validate
+table(["Argument", "Type", "Description"])
+row("foot")
+cell prints
+cell #[code stdout]
+cell Details about the compatibility of your installed models.
+h(3, "convert") Convert
p
@ -172,6 +199,11 @@ p
+cell flag
+cell Show help message and available arguments.
+row("foot")
+cell creates
+cell JSON
+cell Data in spaCy's #[+a("/api/annotation#json-input") JSON format].
p The following converters are available:
+table(["ID", "Description"])
@ -286,6 +318,11 @@ p
+cell flag
+cell Show help message and available arguments.
+row("foot")
+cell creates
+cell model, pickle
+cell A spaCy model on each epoch, and a final #[code .pickle] file.
+h(4, "train-hyperparams") Environment variables for hyperparameters
+tag-new(2)
@ -395,6 +432,50 @@ p
+cell Gradient L2 norm constraint.
+cell #[code 1.0]
+h(3, "vocab") Vocab
+tag-new(2)
p
| Compile a vocabulary from a
| #[+a("/api/annotation#vocab-jsonl") lexicon JSONL] file and optional
| word vectors. Will save out a valid spaCy model that you can load via
| #[+api("spacy#load") #[code spacy.load]] or package using the
| #[+api("cli#package") #[code package]] command.
+code(false, "bash", "$").
spacy vocab [lang] [output_dir] [lexemes_loc] [vectors_loc]
+table(["Argument", "Type", "Description"])
+row
+cell #[code lang]
+cell positional
+cell
| Model language
| #[+a("https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes") ISO code],
| e.g. #[code en].
+row
+cell #[code output_dir]
+cell positional
+cell Model output directory. Will be created if it doesn't exist.
+row
+cell #[code lexemes_loc]
+cell positional
+cell
| Location of lexical data in spaCy's
| #[+a("/api/annotation#vocab-jsonl") JSONL format].
+row
+cell #[code vectors_loc]
+cell positional
+cell Optional location of vectors data as numpy #[code .npz] file.
+row("foot")
+cell creates
+cell model
+cell A spaCy model containing the vocab and vectors.
+h(3, "evaluate") Evaluate
+tag-new(2)
@ -447,22 +528,36 @@ p
+cell flag
+cell Use gold preprocessing.
+row("foot")
+cell prints / creates
+cell #[code stdout], HTML
+cell Training results and optional displaCy visualizations.
+h(3, "package") Package
p
| Generate a #[+a("/usage/training#models-generating") model Python package]
| from an existing model data directory. All data files are copied over.
| If the path to a meta.json is supplied, or a meta.json is found in the
| input directory, this file is used. Otherwise, the data can be entered
| directly from the command line. The required file templates are downloaded
| from #[+src(gh("spacy-dev-resources", "templates/model")) GitHub] to make
| If the path to a #[code meta.json] is supplied, or a #[code meta.json] is
| found in the input directory, this file is used. Otherwise, the data can
| be entered directly from the command line. The required file templates
| are downloaded from
| #[+src(gh("spacy-dev-resources", "templates/model")) GitHub] to make
| sure you're always using the latest versions. This means you need to be
| connected to the internet to use this command.
| connected to the internet to use this command. After packaging, you
| can run #[code python setup.py sdist] from the newly created directory
| to turn your model into an installable archive file.
+code(false, "bash", "$", false, false, true).
spacy package [input_dir] [output_dir] [--meta-path] [--create-meta] [--force]
+aside-code("Example", "bash").
spacy package /input /output
cd /output/en_model-0.0.0
python setup.py sdist
pip install dist/en_model-0.0.0.tar.gz
+table(["Argument", "Type", "Description"])
+row
+cell #[code input_dir]
@ -477,15 +572,16 @@ p
+row
+cell #[code --meta-path], #[code -m]
+cell option
+cell #[+tag-new(2)] Path to meta.json file (optional).
+cell #[+tag-new(2)] Path to #[code meta.json] file (optional).
+row
+cell #[code --create-meta], #[code -c]
+cell flag
+cell
| #[+tag-new(2)] Create a meta.json file on the command line, even
| if one already exists in the directory.
| #[+tag-new(2)] Create a #[code meta.json] file on the command
| line, even if one already exists in the directory. If an
| existing file is found, its entries will be shown as the defaults
| in the command line prompt.
+row
+cell #[code --force], #[code -f]
+cell flag
@ -495,3 +591,8 @@ p
+cell #[code --help], #[code -h]
+cell flag
+cell Show help message and available arguments.
+row("foot")
+cell creates
+cell directory
+cell A Python package containing the spaCy model.

4
website/api/top-level.jade
View File

@ -18,7 +18,3 @@ include ../_includes/_mixins
+section("compat")
+h(2, "compat", "spacy/compaty.py") Compatibility functions
include _top-level/_compat
+section("cli", "spacy/cli")
+h(2, "cli") Command line
include _top-level/_cli

51
website/api/vocab.jade
View File

@ -162,7 +162,7 @@ p
+cell int
+cell The integer ID by which the flag value can be checked.
+h(2, "add_flag") Vocab.clear_vectors
+h(2, "clear_vectors") Vocab.clear_vectors
+tag method
+tag-new(2)
@ -181,7 +181,50 @@ p
| Number of dimensions of the new vectors. If #[code None], size
| is not changed.
+h(2, "add_flag") Vocab.get_vector
+h(2, "prune_vectors") Vocab.prune_vectors
+tag method
+tag-new(2)
p
| Reduce the current vector table to #[code nr_row] unique entries. Words
| mapped to the discarded vectors will be remapped to the closest vector
| among those remaining. For example, suppose the original table had
| vectors for the words:
| #[code.u-break ['sat', 'cat', 'feline', 'reclined']]. If we prune the
| vector table to, two rows, we would discard the vectors for "feline"
| and "reclined". These words would then be remapped to the closest
| remaining vector – so "feline" would have the same vector as "cat",
| and "reclined" would have the same vector as "sat". The similarities are
| judged by cosine. The original vectors may be large, so the cosines are
| calculated in minibatches, to reduce memory usage.
+aside-code("Example").
nlp.vocab.prune_vectors(10000)
assert len(nlp.vocab.vectors) &lt;= 1000
+table(["Name", "Type", "Description"])
+row
+cell #[code nr_row]
+cell int
+cell The number of rows to keep in the vector table.
+row
+cell #[code batch_size]
+cell int
+cell
| Batch of vectors for calculating the similarities. Larger batch
| sizes might be faster, while temporarily requiring more memory.
+row("foot")
+cell returns
+cell dict
+cell
| A dictionary keyed by removed words mapped to
| #[code (string, score)] tuples, where #[code string] is the entry
| the removed word was mapped to, and #[code score] the similarity
| score between the two words.
+h(2, "get_vector") Vocab.get_vector
+tag method
+tag-new(2)
@ -206,7 +249,7 @@ p
| A word vector. Size and shape are determined by the
| #[code Vocab.vectors] instance.
+h(2, "add_flag") Vocab.set_vector
+h(2, "set_vector") Vocab.set_vector
+tag method
+tag-new(2)
@ -228,7 +271,7 @@ p
+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
+cell The vector to set.
+h(2, "add_flag") Vocab.has_vector
+h(2, "has_vector") Vocab.has_vector
+tag method
+tag-new(2)