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https://github.com/explosion/spaCy.git
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Merge branch 'develop' of https://github.com/explosion/spaCy into develop
This commit is contained in:
commit
3fe0d76e6d
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@ -229,7 +229,7 @@ Compile from source
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The other way to install spaCy is to clone its
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`GitHub repository <https://github.com/explosion/spaCy>`_ and build it from
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source. That is the common way if you want to make changes to the code base.
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You'll need to make sure that you have a development enviroment consisting of a
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You'll need to make sure that you have a development environment consisting of a
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Python distribution including header files, a compiler,
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`pip <https://pip.pypa.io/en/latest/installing/>`__, `virtualenv <https://virtualenv.pypa.io/>`_
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and `git <https://git-scm.com>`_ installed. The compiler part is the trickiest.
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@ -3,15 +3,21 @@ from __future__ import print_function
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# NB! This breaks in plac on Python 2!!
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#from __future__ import unicode_literals
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if __name__ == '__main__':
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import plac
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import sys
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from spacy.cli import download, link, info, package, train, convert
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from spacy.cli import download, link, info, package, train, convert, model
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from spacy.util import prints
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commands = {'download': download, 'link': link, 'info': info, 'train': train,
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'convert': convert, 'package': package}
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commands = {
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'download': download,
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'link': link,
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'info': info,
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'train': train,
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'convert': convert,
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'package': package,
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'model': model
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}
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if len(sys.argv) == 1:
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prints(', '.join(commands), title="Available commands", exits=1)
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command = sys.argv.pop(1)
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@ -19,5 +25,7 @@ if __name__ == '__main__':
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if command in commands:
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plac.call(commands[command])
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else:
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prints("Available: %s" % ', '.join(commands),
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title="Unknown command: %s" % command, exits=1)
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prints(
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"Available: %s" % ', '.join(commands),
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title="Unknown command: %s" % command,
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exits=1)
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@ -4,3 +4,4 @@ from .link import link
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from .package import package
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from .train import train
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from .convert import convert
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from .model import model
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119
spacy/cli/model.py
Normal file
119
spacy/cli/model.py
Normal file
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@ -0,0 +1,119 @@
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# coding: utf8
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from __future__ import unicode_literals
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import gzip
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import math
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from ast import literal_eval
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from pathlib import Path
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from preshed.counter import PreshCounter
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import spacy
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from ..compat import fix_text
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from .. import util
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def model(cmd, lang, model_dir, freqs_data, clusters_data, vectors_data):
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model_path = Path(model_dir)
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freqs_path = Path(freqs_data)
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clusters_path = Path(clusters_data) if clusters_data else None
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vectors_path = Path(vectors_data) if vectors_data else None
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check_dirs(freqs_path, clusters_path, vectors_path)
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# vocab = util.get_lang_class(lang).Defaults.create_vocab()
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nlp = spacy.blank(lang)
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vocab = nlp.vocab
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probs, oov_prob = read_probs(freqs_path)
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clusters = read_clusters(clusters_path) if clusters_path else {}
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populate_vocab(vocab, clusters, probs, oov_prob)
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create_model(model_path, nlp)
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def create_model(model_path, model):
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if not model_path.exists():
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model_path.mkdir()
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model.to_disk(model_path.as_posix())
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def read_probs(freqs_path, max_length=100, min_doc_freq=5, min_freq=200):
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counts = PreshCounter()
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total = 0
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freqs_file = check_unzip(freqs_path)
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for i, line in enumerate(freqs_file):
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freq, doc_freq, key = line.rstrip().split('\t', 2)
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freq = int(freq)
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counts.inc(i + 1, freq)
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total += freq
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counts.smooth()
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log_total = math.log(total)
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freqs_file = check_unzip(freqs_path)
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probs = {}
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for line in freqs_file:
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freq, doc_freq, key = line.rstrip().split('\t', 2)
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doc_freq = int(doc_freq)
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freq = int(freq)
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if doc_freq >= min_doc_freq and freq >= min_freq and len(
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key) < max_length:
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word = literal_eval(key)
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smooth_count = counts.smoother(int(freq))
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probs[word] = math.log(smooth_count) - log_total
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oov_prob = math.log(counts.smoother(0)) - log_total
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return probs, oov_prob
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def read_clusters(clusters_path):
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clusters = {}
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with clusters_path.open() as f:
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for line in f:
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try:
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cluster, word, freq = line.split()
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word = fix_text(word)
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except ValueError:
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continue
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# If the clusterer has only seen the word a few times, its
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# cluster is unreliable.
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if int(freq) >= 3:
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clusters[word] = cluster
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else:
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clusters[word] = '0'
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# Expand clusters with re-casing
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for word, cluster in list(clusters.items()):
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if word.lower() not in clusters:
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clusters[word.lower()] = cluster
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if word.title() not in clusters:
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clusters[word.title()] = cluster
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if word.upper() not in clusters:
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clusters[word.upper()] = cluster
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return clusters
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def populate_vocab(vocab, clusters, probs, oov_prob):
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for word, prob in reversed(
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sorted(list(probs.items()), key=lambda item: item[1])):
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lexeme = vocab[word]
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lexeme.prob = prob
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lexeme.is_oov = False
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# Decode as a little-endian string, so that we can do & 15 to get
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# the first 4 bits. See _parse_features.pyx
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if word in clusters:
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lexeme.cluster = int(clusters[word][::-1], 2)
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else:
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lexeme.cluster = 0
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def check_unzip(file_path):
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file_path_str = file_path.as_posix()
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if file_path_str.endswith('gz'):
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return gzip.open(file_path_str)
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else:
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return file_path.open()
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def check_dirs(freqs_data, clusters_data, vectors_data):
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if not freqs_data.is_file():
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util.sys_exit(freqs_data.as_posix(), title="No frequencies file found")
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if clusters_data and not clusters_data.is_file():
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util.sys_exit(
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clusters_data.as_posix(), title="No Brown clusters file found")
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if vectors_data and not vectors_data.is_file():
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util.sys_exit(
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vectors_data.as_posix(), title="No word vectors file found")
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@ -205,7 +205,7 @@ p Retokenize the document, such that the span is merged into a single token.
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p
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| The token within the span that's highest in the parse tree. If there's a
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| tie, the earlist is prefered.
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| tie, the earliest is preferred.
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+aside-code("Example").
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doc = nlp(u'I like New York in Autumn.')
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@ -39,7 +39,7 @@ p
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+h(2, "special-cases") Adding special case tokenization rules
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p
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| Most domains have at least some idiosyncracies that require custom
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| Most domains have at least some idiosyncrasies that require custom
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| tokenization rules. This could be very certain expressions, or
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| abbreviations only used in this specific field.
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@ -109,7 +109,7 @@ p
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| The other way to install spaCy is to clone its
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| #[+a(gh("spaCy")) GitHub repository] and build it from source. That is
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| the common way if you want to make changes to the code base. You'll need to
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| make sure that you have a development enviroment consisting of a Python
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| make sure that you have a development environment consisting of a Python
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| distribution including header files, a compiler,
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| #[+a("https://pip.pypa.io/en/latest/installing/") pip],
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| #[+a("https://virtualenv.pypa.io/") virtualenv] and
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|
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@ -40,7 +40,7 @@ p
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+cell #[code VerbForm=Fin], #[code Mood=Ind], #[code Tense=Pres]
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+row
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+cell I read the paper yesteday
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+cell I read the paper yesterday
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+cell read
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+cell read
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+cell verb
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@ -94,7 +94,7 @@ p
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| is mostly intended as a convenient, interactive wrapper. It performs
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| compatibility checks and prints detailed error messages and warnings.
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| However, if you're downloading models as part of an automated build
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| process, this only adds an unecessary layer of complexity. If you know
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| process, this only adds an unnecessary layer of complexity. If you know
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| which models your application needs, you should be specifying them directly.
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p
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