explosion/spaCy
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Merge branch 'develop' into master-tmp

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This commit is contained in:
Ines Montani 2020-06-20 15:52:00 +02:00
commit 52728d8fa3
711 changed files with 11924 additions and 11926 deletions
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4
.gitignore vendored
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@ -44,6 +44,7 @@ __pycache__/
.env*
.~env/
.venv
env3.6/
venv/
env3.*/
.dev
@ -118,3 +119,6 @@ Desktop.ini
# Pycharm project files
*.idea
# IPython
.ipynb_checkpoints/

23
.travis.yml
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@ -1,23 +0,0 @@
language: python
sudo: false
cache: pip
dist: trusty
group: edge
python:
- "2.7"
os:
- linux
install:
- "pip install -r requirements.txt"
- "python setup.py build_ext --inplace"
- "pip install -e ."
script:
- "cat /proc/cpuinfo | grep flags | head -n 1"
- "python -m pytest --tb=native spacy"
branches:
except:
- spacy.io
notifications:
slack:
secure: F8GvqnweSdzImuLL64TpfG0i5rYl89liyr9tmFVsHl4c0DNiDuGhZivUz0M1broS8svE3OPOllLfQbACG/4KxD890qfF9MoHzvRDlp7U+RtwMV/YAkYn8MGWjPIbRbX0HpGdY7O2Rc9Qy4Kk0T8ZgiqXYIqAz2Eva9/9BlSmsJQ=
email: false

18
CONTRIBUTING.md
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@ -280,23 +280,7 @@ except: # noqa: E722
### Python conventions
All Python code must be written in an **intersection of Python 2 and Python 3**.
This is easy in Cython, but somewhat ugly in Python. Logic that deals with
Python or platform compatibility should only live in
[`spacy.compat`](spacy/compat.py). To distinguish them from the builtin
functions, replacement functions are suffixed with an underscore, for example
`unicode_`. If you need to access the user's version or platform information,
for example to show more specific error messages, you can use the `is_config()`
helper function.
```python
from .compat import unicode_, is_config
compatible_unicode = unicode_('hello world')
if is_config(windows=True, python2=True):
print("You are using Python 2 on Windows.")
```
All Python code must be written **compatible with Python 3.6+**.
Code that interacts with the file-system should accept objects that follow the
`pathlib.Path` API, without assuming that the object inherits from `pathlib.Path`.
If the function is user-facing and takes a path as an argument, it should check

2
MANIFEST.in
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@ -1,5 +1,5 @@
recursive-include include *.h
recursive-include spacy *.txt *.pyx *.pxd
recursive-include spacy *.pyx *.pxd *.txt *.cfg
include LICENSE
include README.md
include bin/spacy

4
Makefile
View File

@ -5,7 +5,7 @@ VENV := ./env$(PYVER)
version := $(shell "bin/get-version.sh")
dist/spacy-$(version).pex : wheelhouse/spacy-$(version).stamp
$(VENV)/bin/pex -f ./wheelhouse --no-index --disable-cache -m spacy -o $@ spacy==$(version) jsonschema spacy-lookups-data jieba pkuseg==0.0.22 sudachipy sudachidict_core
$(VENV)/bin/pex -f ./wheelhouse --no-index --disable-cache -m spacy -o $@ spacy==$(version) spacy-lookups-data jieba pkuseg==0.0.22 sudachipy sudachidict_core
chmod a+rx $@
cp $@ dist/spacy.pex
@ -15,7 +15,7 @@ dist/pytest.pex : wheelhouse/pytest-*.whl
wheelhouse/spacy-$(version).stamp : $(VENV)/bin/pex setup.py spacy/*.py* spacy/*/*.py*
$(VENV)/bin/pip wheel . -w ./wheelhouse
$(VENV)/bin/pip wheel jsonschema spacy-lookups-data jieba pkuseg==0.0.22 sudachipy sudachidict_core -w ./wheelhouse
$(VENV)/bin/pip wheel spacy-lookups-data jieba pkuseg==0.0.22 sudachipy sudachidict_core -w ./wheelhouse
touch $@
wheelhouse/pytest-%.whl : $(VENV)/bin/pex

14
README.md
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@ -15,7 +15,6 @@ It's commercial open-source software, released under the MIT license.
[Check out the release notes here.](https://github.com/explosion/spaCy/releases)
[![Azure Pipelines](<https://img.shields.io/azure-devops/build/explosion-ai/public/8/master.svg?logo=azure-pipelines&style=flat-square&label=build+(3.x)>)](https://dev.azure.com/explosion-ai/public/_build?definitionId=8)
[![Travis Build Status](<https://img.shields.io/travis/explosion/spaCy/master.svg?style=flat-square&logo=travis-ci&logoColor=white&label=build+(2.7)>)](https://travis-ci.org/explosion/spaCy)
[![Current Release Version](https://img.shields.io/github/release/explosion/spacy.svg?style=flat-square&logo=github)](https://github.com/explosion/spaCy/releases)
[![pypi Version](https://img.shields.io/pypi/v/spacy.svg?style=flat-square&logo=pypi&logoColor=white)](https://pypi.org/project/spacy/)
[![conda Version](https://img.shields.io/conda/vn/conda-forge/spacy.svg?style=flat-square&logo=conda-forge&logoColor=white)](https://anaconda.org/conda-forge/spacy)
@ -98,12 +97,19 @@ For detailed installation instructions, see the
- **Operating system**: macOS / OS X · Linux · Windows (Cygwin, MinGW, Visual
Studio)
- **Python version**: Python 2.7, 3.5+ (only 64 bit)
- **Python version**: Python 3.6+ (only 64 bit)
- **Package managers**: [pip] · [conda] (via `conda-forge`)
[pip]: https://pypi.org/project/spacy/
[conda]: https://anaconda.org/conda-forge/spacy
> ⚠️ **Important note for Python 3.8:** We can't yet ship pre-compiled binary
> wheels for spaCy that work on Python 3.8, as we're still waiting for our CI
> providers and other tooling to support it. This means that in order to run
> spaCy on Python 3.8, you'll need [a compiler installed](#source) and compile
> the library and its Cython dependencies locally. If this is causing problems
> for you, the easiest solution is to **use Python 3.7** in the meantime.
### pip
Using pip, spaCy releases are available as source packages and binary wheels (as
@ -263,9 +269,7 @@ and git preinstalled.
Install a version of the
[Visual C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
or [Visual Studio Express](https://visualstudio.microsoft.com/vs/express/) that
matches the version that was used to compile your Python interpreter. For
official distributions these are VS 2008 (Python 2.7), VS 2010 (Python 3.4) and
VS 2015 (Python 3.5).
matches the version that was used to compile your Python interpreter.
## Run tests

10
azure-pipelines.yml
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@ -27,7 +27,7 @@ jobs:
inputs:
versionSpec: '3.7'
- script: |
pip install flake8
pip install flake8==3.5.0
python -m flake8 spacy --count --select=E901,E999,F821,F822,F823 --show-source --statistics
displayName: 'flake8'
@ -35,12 +35,6 @@ jobs:
dependsOn: 'Validate'
strategy:
matrix:
Python35Linux:
imageName: 'ubuntu-16.04'
python.version: '3.5'
Python35Windows:
imageName: 'vs2017-win2016'
python.version: '3.5'
Python36Linux:
imageName: 'ubuntu-16.04'
python.version: '3.6'
@ -58,7 +52,7 @@ jobs:
# imageName: 'vs2017-win2016'
# python.version: '3.7'
# Python37Mac:
# imageName: 'macos-10.13'
# imageName: 'macos-10.14'
# python.version: '3.7'
Python38Linux:
imageName: 'ubuntu-16.04'

169
bin/cythonize.py
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@ -1,169 +0,0 @@
#!/usr/bin/env python
""" cythonize.py
Cythonize pyx files into C++ files as needed.
Usage: cythonize.py [root]
Checks pyx files to see if they have been changed relative to their
corresponding C++ files. If they have, then runs cython on these files to
recreate the C++ files.
Additionally, checks pxd files and setup.py if they have been changed. If
they have, rebuilds everything.
Change detection based on file hashes stored in JSON format.
For now, this script should be run by developers when changing Cython files
and the resulting C++ files checked in, so that end-users (and Python-only
developers) do not get the Cython dependencies.
Based upon:
https://raw.github.com/dagss/private-scipy-refactor/cythonize/cythonize.py
https://raw.githubusercontent.com/numpy/numpy/master/tools/cythonize.py
Note: this script does not check any of the dependent C++ libraries.
"""
from __future__ import print_function
import os
import sys
import json
import hashlib
import subprocess
import argparse
HASH_FILE = "cythonize.json"
def process_pyx(fromfile, tofile, language_level="-2"):
print("Processing %s" % fromfile)
try:
from Cython.Compiler.Version import version as cython_version
from distutils.version import LooseVersion
if LooseVersion(cython_version) < LooseVersion("0.19"):
raise Exception("Require Cython >= 0.19")
except ImportError:
pass
flags = ["--fast-fail", language_level]
if tofile.endswith(".cpp"):
flags += ["--cplus"]
try:
try:
r = subprocess.call(
["cython"] + flags + ["-o", tofile, fromfile], env=os.environ
) # See Issue #791
if r != 0:
raise Exception("Cython failed")
except OSError:
# There are ways of installing Cython that don't result in a cython
# executable on the path, see gh-2397.
r = subprocess.call(
[
sys.executable,
"-c",
"import sys; from Cython.Compiler.Main import "
"setuptools_main as main; sys.exit(main())",
]
+ flags
+ ["-o", tofile, fromfile]
)
if r != 0:
raise Exception("Cython failed")
except OSError:
raise OSError("Cython needs to be installed")
def preserve_cwd(path, func, *args):
orig_cwd = os.getcwd()
try:
os.chdir(path)
func(*args)
finally:
os.chdir(orig_cwd)
def load_hashes(filename):
try:
return json.load(open(filename))
except (ValueError, IOError):
return {}
def save_hashes(hash_db, filename):
with open(filename, "w") as f:
f.write(json.dumps(hash_db))
def get_hash(path):
return hashlib.md5(open(path, "rb").read()).hexdigest()
def hash_changed(base, path, db):
full_path = os.path.normpath(os.path.join(base, path))
return not get_hash(full_path) == db.get(full_path)
def hash_add(base, path, db):
full_path = os.path.normpath(os.path.join(base, path))
db[full_path] = get_hash(full_path)
def process(base, filename, db):
root, ext = os.path.splitext(filename)
if ext in [".pyx", ".cpp"]:
if hash_changed(base, filename, db) or not os.path.isfile(
os.path.join(base, root + ".cpp")
):
preserve_cwd(base, process_pyx, root + ".pyx", root + ".cpp")
hash_add(base, root + ".cpp", db)
hash_add(base, root + ".pyx", db)
def check_changes(root, db):
res = False
new_db = {}
setup_filename = "setup.py"
hash_add(".", setup_filename, new_db)
if hash_changed(".", setup_filename, db):
res = True
for base, _, files in os.walk(root):
for filename in files:
if filename.endswith(".pxd"):
hash_add(base, filename, new_db)
if hash_changed(base, filename, db):
res = True
if res:
db.clear()
db.update(new_db)
return res
def run(root):
db = load_hashes(HASH_FILE)
try:
check_changes(root, db)
for base, _, files in os.walk(root):
for filename in files:
process(base, filename, db)
finally:
save_hashes(db, HASH_FILE)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Cythonize pyx files into C++ files as needed"
)
parser.add_argument("root", help="root directory")
args = parser.parse_args()
run(args.root)

13
bin/ud/ud_run_test.py
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@ -13,23 +13,12 @@ import srsly
import spacy
import spacy.util
from spacy.tokens import Token, Doc
from spacy.gold import GoldParse
from spacy.util import compounding, minibatch_by_words
from spacy.syntax.nonproj import projectivize
from spacy.matcher import Matcher
# from spacy.morphology import Fused_begin, Fused_inside
from spacy import displacy
from collections import defaultdict, Counter
from timeit import default_timer as timer
Fused_begin = None
Fused_inside = None
import itertools
import random
import numpy.random
from . import conll17_ud_eval
from spacy import lang
@ -268,7 +257,7 @@ def load_nlp(experiments_dir, corpus):
return nlp
def initialize_pipeline(nlp, docs, golds, config, device):
def initialize_pipeline(nlp, examples, config, device):
nlp.add_pipe(nlp.create_pipe("parser"))
return nlp

67
bin/ud/ud_train.py
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@ -14,7 +14,7 @@ import spacy
import spacy.util
from bin.ud import conll17_ud_eval
from spacy.tokens import Token, Doc
from spacy.gold import GoldParse
from spacy.gold import GoldParse, Example
from spacy.util import compounding, minibatch, minibatch_by_words
from spacy.syntax.nonproj import projectivize
from spacy.matcher import Matcher
@ -53,7 +53,7 @@ def read_data(
max_doc_length=None,
limit=None,
):
"""Read the CONLLU format into (Doc, GoldParse) tuples. If raw_text=True,
"""Read the CONLLU format into Example objects. If raw_text=True,
include Doc objects created using nlp.make_doc and then aligned against
the gold-standard sequences. If oracle_segments=True, include Doc objects
created from the gold-standard segments. At least one must be True."""
@ -98,15 +98,16 @@ def read_data(
docs.append(doc)
golds.append(gold)
if limit and len(docs) >= limit:
return docs, golds
return golds_to_gold_data(docs, golds)
if raw_text and sent_annots:
doc, gold = _make_gold(nlp, None, sent_annots)
docs.append(doc)
golds.append(gold)
if limit and len(docs) >= limit:
return docs, golds
return docs, golds
return golds_to_gold_data(docs, golds)
return golds_to_gold_data(docs, golds)
def _parse_morph_string(morph_string):
if morph_string == '_':
@ -120,6 +121,7 @@ def _parse_morph_string(morph_string):
output.append('%s_%s' % (key, value.lower()))
return set(output)
def read_conllu(file_):
docs = []
sent = []
@ -180,16 +182,18 @@ def _make_gold(nlp, text, sent_annots, drop_deps=0.0):
#############################
def golds_to_gold_tuples(docs, golds):
"""Get out the annoying 'tuples' format used by begin_training, given the
def golds_to_gold_data(docs, golds):
"""Get out the training data format used by begin_training, given the
GoldParse objects."""
tuples = []
data = []
for doc, gold in zip(docs, golds):
text = doc.text
ids, words, tags, heads, labels, iob = zip(*gold.orig_annot)
sents = [((ids, words, tags, heads, labels, iob), [])]
tuples.append((text, sents))
return tuples
example = Example(doc=doc)
example.add_doc_annotation(cats=gold.cats)
token_annotation_dict = gold.orig.to_dict()
example.add_token_annotation(**token_annotation_dict)
example.goldparse = gold
data.append(example)
return data
##############
@ -327,7 +331,6 @@ def get_token_conllu(token, i):
return "\n".join(lines)
##################
# Initialization #
##################
@ -348,7 +351,7 @@ def load_nlp(corpus, config, vectors=None):
return nlp
def initialize_pipeline(nlp, docs, golds, config, device):
def initialize_pipeline(nlp, examples, config, device):
nlp.add_pipe(nlp.create_pipe("tagger", config={"set_morphology": False}))
nlp.add_pipe(nlp.create_pipe("morphologizer"))
nlp.add_pipe(nlp.create_pipe("parser"))
@ -356,14 +359,15 @@ def initialize_pipeline(nlp, docs, golds, config, device):
nlp.parser.add_multitask_objective("tag")
if config.multitask_sent:
nlp.parser.add_multitask_objective("sent_start")
for gold in golds:
for ex in examples:
gold = ex.gold
for tag in gold.tags:
if tag is not None:
nlp.tagger.add_label(tag)
if torch is not None and device != -1:
torch.set_default_tensor_type("torch.cuda.FloatTensor")
optimizer = nlp.begin_training(
lambda: golds_to_gold_tuples(docs, golds),
lambda: examples,
device=device,
subword_features=config.subword_features,
conv_depth=config.conv_depth,
@ -382,8 +386,8 @@ def _load_pretrained_tok2vec(nlp, loc):
weights_data = file_.read()
loaded = []
for name, component in nlp.pipeline:
if hasattr(component, "model") and hasattr(component.model, "tok2vec"):
component.tok2vec.from_bytes(weights_data)
if hasattr(component, "model") and component.model.has_ref("tok2vec"):
component.get_ref("tok2vec").from_bytes(weights_data)
loaded.append(name)
return loaded
@ -491,6 +495,10 @@ def main(
Token.set_extension("begins_fused", default=False)
Token.set_extension("inside_fused", default=False)
Token.set_extension("get_conllu_lines", method=get_token_conllu)
Token.set_extension("begins_fused", default=False)
Token.set_extension("inside_fused", default=False)
spacy.util.fix_random_seed()
lang.zh.Chinese.Defaults.use_jieba = False
lang.ja.Japanese.Defaults.use_janome = False
@ -505,7 +513,7 @@ def main(
print("Train and evaluate", corpus, "using lang", paths.lang)
nlp = load_nlp(paths.lang, config, vectors=vectors_dir)
docs, golds = read_data(
examples = read_data(
nlp,
paths.train.conllu.open(encoding="utf8"),
paths.train.text.open(encoding="utf8"),
@ -513,12 +521,12 @@ def main(
limit=limit,
)
optimizer = initialize_pipeline(nlp, docs, golds, config, gpu_device)
optimizer = initialize_pipeline(nlp, examples, config, gpu_device)
batch_sizes = compounding(config.min_batch_size, config.max_batch_size, 1.001)
beam_prob = compounding(0.2, 0.8, 1.001)
for i in range(config.nr_epoch):
docs, golds = read_data(
examples = read_data(
nlp,
paths.train.conllu.open(encoding="utf8"),
paths.train.text.open(encoding="utf8"),
@ -527,22 +535,19 @@ def main(
oracle_segments=use_oracle_segments,
raw_text=not use_oracle_segments,
)
Xs = list(zip(docs, golds))
random.shuffle(Xs)
random.shuffle(examples)
if config.batch_by_words:
batches = minibatch_by_words(Xs, size=batch_sizes)
batches = minibatch_by_words(examples, size=batch_sizes)
else:
batches = minibatch(Xs, size=batch_sizes)
batches = minibatch(examples, size=batch_sizes)
losses = {}
n_train_words = sum(len(doc) for doc in docs)
n_train_words = sum(len(ex.doc) for ex in examples)
with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
for batch in batches:
batch_docs, batch_gold = zip(*batch)
pbar.update(sum(len(doc) for doc in batch_docs))
pbar.update(sum(len(ex.doc) for ex in batch))
nlp.parser.cfg["beam_update_prob"] = next(beam_prob)
nlp.update(
batch_docs,
batch_gold,
batch,
sgd=optimizer,
drop=config.dropout,
losses=losses,

5
examples/deep_learning_keras.py
View File

@ -14,7 +14,7 @@ pip install keras==2.0.9
Compatible with: spaCy v2.0.0+
"""
import ml_datasets
import plac
import random
import pathlib
@ -24,7 +24,6 @@ from keras.models import Sequential, model_from_json
from keras.layers import LSTM, Dense, Embedding, Bidirectional
from keras.layers import TimeDistributed
from keras.optimizers import Adam
import thinc.extra.datasets
from spacy.compat import pickle
import spacy
@ -224,7 +223,7 @@ def main(
if model_dir is not None:
model_dir = pathlib.Path(model_dir)
if train_dir is None or dev_dir is None:
imdb_data = thinc.extra.datasets.imdb()
imdb_data = ml_datasets.imdb()
if is_runtime:
if dev_dir is None:
dev_texts, dev_labels = zip(*imdb_data[1])

126
examples/experiments/onto-joint/defaults.cfg Normal file
View File

@ -0,0 +1,126 @@
# Training hyper-parameters and additional features.
[training]
# Whether to train on sequences with 'gold standard' sentence boundaries
# and tokens. If you set this to true, take care to ensure your run-time
# data is passed in sentence-by-sentence via some prior preprocessing.
gold_preproc = false
# Limitations on training document length or number of examples.
max_length = 0
limit = 0
# Data augmentation
orth_variant_level = 0.0
noise_level = 0.0
dropout = 0.1
# Controls early-stopping. 0 or -1 mean unlimited.
patience = 1600
max_epochs = 0
max_steps = 20000
eval_frequency = 400
# Other settings
seed = 0
accumulate_gradient = 1
use_pytorch_for_gpu_memory = false
# Control how scores are printed and checkpoints are evaluated.
scores = ["speed", "tags_acc", "uas", "las", "ents_f"]
score_weights = {"las": 0.4, "ents_f": 0.4, "tags_acc": 0.2}
# These settings are invalid for the transformer models.
init_tok2vec = null
discard_oversize = false
omit_extra_lookups = false
[training.batch_size]
@schedules = "compounding.v1"
start = 1000
stop = 1000
compound = 1.001
[training.optimizer]
@optimizers = "Adam.v1"
beta1 = 0.9
beta2 = 0.999
L2_is_weight_decay = true
L2 = 0.01
grad_clip = 1.0
use_averages = true
eps = 1e-8
learn_rate = 0.001
#[optimizer.learn_rate]
#@schedules = "warmup_linear.v1"
#warmup_steps = 250
#total_steps = 20000
#initial_rate = 0.001
[nlp]
lang = "en"
vectors = null
[nlp.pipeline.tok2vec]
factory = "tok2vec"
[nlp.pipeline.senter]
factory = "senter"
[nlp.pipeline.ner]
factory = "ner"
learn_tokens = false
min_action_freq = 1
beam_width = 1
beam_update_prob = 1.0
[nlp.pipeline.tagger]
factory = "tagger"
[nlp.pipeline.parser]
factory = "parser"
learn_tokens = false
min_action_freq = 1
beam_width = 1
beam_update_prob = 1.0
[nlp.pipeline.senter.model]
@architectures = "spacy.Tagger.v1"
[nlp.pipeline.senter.model.tok2vec]
@architectures = "spacy.Tok2VecTensors.v1"
width = ${nlp.pipeline.tok2vec.model:width}
[nlp.pipeline.tagger.model]
@architectures = "spacy.Tagger.v1"
[nlp.pipeline.tagger.model.tok2vec]
@architectures = "spacy.Tok2VecTensors.v1"
width = ${nlp.pipeline.tok2vec.model:width}
[nlp.pipeline.parser.model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 8
hidden_width = 128
maxout_pieces = 3
use_upper = false
[nlp.pipeline.parser.model.tok2vec]
@architectures = "spacy.Tok2VecTensors.v1"
width = ${nlp.pipeline.tok2vec.model:width}
[nlp.pipeline.ner.model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 3
hidden_width = 128
maxout_pieces = 3
use_upper = false
[nlp.pipeline.ner.model.tok2vec]
@architectures = "spacy.Tok2VecTensors.v1"
width = ${nlp.pipeline.tok2vec.model:width}
[nlp.pipeline.tok2vec.model]
@architectures = "spacy.HashEmbedCNN.v1"
pretrained_vectors = ${nlp:vectors}
width = 256
depth = 6
window_size = 1
embed_size = 10000
maxout_pieces = 3
subword_features = true
dropout = null

145
examples/experiments/onto-joint/pretrain.cfg Normal file
View File

@ -0,0 +1,145 @@
# Training hyper-parameters and additional features.
[training]
# Whether to train on sequences with 'gold standard' sentence boundaries
# and tokens. If you set this to true, take care to ensure your run-time
# data is passed in sentence-by-sentence via some prior preprocessing.
gold_preproc = false
# Limitations on training document length or number of examples.
max_length = 0
limit = 0
# Data augmentation
orth_variant_level = 0.0
noise_level = 0.0
dropout = 0.1
# Controls early-stopping. 0 or -1 mean unlimited.
patience = 1600
max_epochs = 0
max_steps = 20000
eval_frequency = 400
# Other settings
seed = 0
accumulate_gradient = 1
use_pytorch_for_gpu_memory = false
# Control how scores are printed and checkpoints are evaluated.
scores = ["speed", "tags_acc", "uas", "las", "ents_f"]
score_weights = {"las": 0.4, "ents_f": 0.4, "tags_acc": 0.2}
# These settings are invalid for the transformer models.
init_tok2vec = null
discard_oversize = false
[training.batch_size]
@schedules = "compounding.v1"
start = 1000
stop = 1000
compound = 1.001
[training.optimizer]
@optimizers = "Adam.v1"
beta1 = 0.9
beta2 = 0.999
L2_is_weight_decay = true
L2 = 0.01
grad_clip = 1.0
use_averages = true
eps = 1e-8
learn_rate = 0.001
[pretraining]
max_epochs = 1000
min_length = 5
max_length = 500
dropout = 0.2
n_save_every = null
batch_size = 3000
seed = ${training:seed}
use_pytorch_for_gpu_memory = ${training:use_pytorch_for_gpu_memory}
tok2vec_model = "nlp.pipeline.tok2vec.model"
[pretraining.optimizer]
@optimizers = "Adam.v1"
beta1 = 0.9
beta2 = 0.999
L2_is_weight_decay = true
L2 = 0.01
grad_clip = 1.0
use_averages = true
eps = 1e-8
learn_rate = 0.001
[pretraining.loss_func]
@losses = "CosineDistance.v1"
normalize = true
[nlp]
lang = "en"
vectors = null
[nlp.pipeline.tok2vec]
factory = "tok2vec"
[nlp.pipeline.senter]
factory = "senter"
[nlp.pipeline.ner]
factory = "ner"
learn_tokens = false
min_action_freq = 1
beam_width = 1
beam_update_prob = 1.0
[nlp.pipeline.tagger]
factory = "tagger"
[nlp.pipeline.parser]
factory = "parser"
learn_tokens = false
min_action_freq = 1
beam_width = 1
beam_update_prob = 1.0
[nlp.pipeline.senter.model]
@architectures = "spacy.Tagger.v1"
[nlp.pipeline.senter.model.tok2vec]
@architectures = "spacy.Tok2VecTensors.v1"
width = ${nlp.pipeline.tok2vec.model:width}
[nlp.pipeline.tagger.model]
@architectures = "spacy.Tagger.v1"
[nlp.pipeline.tagger.model.tok2vec]
@architectures = "spacy.Tok2VecTensors.v1"
width = ${nlp.pipeline.tok2vec.model:width}
[nlp.pipeline.parser.model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 8
hidden_width = 128
maxout_pieces = 3
use_upper = false
[nlp.pipeline.parser.model.tok2vec]
@architectures = "spacy.Tok2VecTensors.v1"
width = ${nlp.pipeline.tok2vec.model:width}
[nlp.pipeline.ner.model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 3
hidden_width = 128
maxout_pieces = 3
use_upper = false
[nlp.pipeline.ner.model.tok2vec]
@architectures = "spacy.Tok2VecTensors.v1"
width = ${nlp.pipeline.tok2vec.model:width}
[nlp.pipeline.tok2vec.model]
@architectures = "spacy.HashEmbedCNN.v1"
pretrained_vectors = ${nlp:vectors}
width = 256
depth = 6
window_size = 1
embed_size = 10000
maxout_pieces = 3
subword_features = true
dropout = null

74
examples/experiments/ptb-joint-pos-dep/bilstm_tok2vec.cfg Normal file
View File

@ -0,0 +1,74 @@
[training]
patience = 10000
eval_frequency = 200
dropout = 0.2
init_tok2vec = null
vectors = null
max_epochs = 100
orth_variant_level = 0.0
noise_level = 0.0
gold_preproc = true
max_length = 0
use_gpu = 0
scores = ["tags_acc", "uas", "las"]
score_weights = {"las": 0.8, "tags_acc": 0.2}
limit = 0
seed = 0
accumulate_gradient = 2
discard_oversize = false
[training.batch_size]
@schedules = "compounding.v1"
start = 100
stop = 1000
compound = 1.001
[training.optimizer]
@optimizers = "Adam.v1"
learn_rate = 0.001
beta1 = 0.9
beta2 = 0.999
[nlp]
lang = "en"
vectors = ${training:vectors}
[nlp.pipeline.tok2vec]
factory = "tok2vec"
[nlp.pipeline.tagger]
factory = "tagger"
[nlp.pipeline.parser]
factory = "parser"
learn_tokens = false
min_action_freq = 1
beam_width = 1
beam_update_prob = 1.0
[nlp.pipeline.tagger.model]
@architectures = "spacy.Tagger.v1"
[nlp.pipeline.tagger.model.tok2vec]
@architectures = "spacy.Tok2VecTensors.v1"
width = ${nlp.pipeline.tok2vec.model:width}
[nlp.pipeline.parser.model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 8
hidden_width = 64
maxout_pieces = 3
[nlp.pipeline.parser.model.tok2vec]
@architectures = "spacy.Tok2VecTensors.v1"
width = ${nlp.pipeline.tok2vec.model:width}
[nlp.pipeline.tok2vec.model]
@architectures = "spacy.HashEmbedBiLSTM.v1"
pretrained_vectors = ${nlp:vectors}
width = 96
depth = 4
embed_size = 2000
subword_features = true
maxout_pieces = 3
dropout = null

75
examples/experiments/ptb-joint-pos-dep/defaults.cfg Normal file
View File

@ -0,0 +1,75 @@
[training]
patience = 10000
eval_frequency = 200
dropout = 0.2
init_tok2vec = null
vectors = null
max_epochs = 100
orth_variant_level = 0.0
noise_level = 0.0
gold_preproc = true
max_length = 0
use_gpu = -1
scores = ["tags_acc", "uas", "las"]
score_weights = {"las": 0.8, "tags_acc": 0.2}
limit = 0
seed = 0
accumulate_gradient = 2
discard_oversize = false
[training.batch_size]
@schedules = "compounding.v1"
start = 100
stop = 1000
compound = 1.001
[training.optimizer]
@optimizers = "Adam.v1"
learn_rate = 0.001
beta1 = 0.9
beta2 = 0.999
[nlp]
lang = "en"
vectors = ${training:vectors}
[nlp.pipeline.tok2vec]
factory = "tok2vec"
[nlp.pipeline.tagger]
factory = "tagger"
[nlp.pipeline.parser]
factory = "parser"
learn_tokens = false
min_action_freq = 1
beam_width = 1
beam_update_prob = 1.0
[nlp.pipeline.tagger.model]
@architectures = "spacy.Tagger.v1"
[nlp.pipeline.tagger.model.tok2vec]
@architectures = "spacy.Tok2VecTensors.v1"
width = ${nlp.pipeline.tok2vec.model:width}
[nlp.pipeline.parser.model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 8
hidden_width = 64
maxout_pieces = 3
[nlp.pipeline.parser.model.tok2vec]
@architectures = "spacy.Tok2VecTensors.v1"
width = ${nlp.pipeline.tok2vec.model:width}
[nlp.pipeline.tok2vec.model]
@architectures = "spacy.HashEmbedCNN.v1"
pretrained_vectors = ${nlp:vectors}
width = 96
depth = 4
window_size = 1
embed_size = 2000
maxout_pieces = 3
subword_features = true
dropout = null

69
examples/experiments/tok2vec-ner/charembed_tok2vec.cfg Normal file
View File

@ -0,0 +1,69 @@
[training]
use_gpu = -1
limit = 0
dropout = 0.2
patience = 10000
eval_frequency = 200
scores = ["ents_f"]
score_weights = {"ents_f": 1}
orth_variant_level = 0.0
gold_preproc = true
max_length = 0
batch_size = 25
seed = 0
accumulate_gradient = 2
discard_oversize = false
[training.optimizer]
@optimizers = "Adam.v1"
learn_rate = 0.001
beta1 = 0.9
beta2 = 0.999
[nlp]
lang = "en"
vectors = null
[nlp.pipeline.tok2vec]
factory = "tok2vec"
[nlp.pipeline.tok2vec.model]
@architectures = "spacy.Tok2Vec.v1"
[nlp.pipeline.tok2vec.model.extract]
@architectures = "spacy.CharacterEmbed.v1"
width = 96
nM = 64
nC = 8
rows = 2000
columns = ["ID", "NORM", "PREFIX", "SUFFIX", "SHAPE", "ORTH"]
dropout = null
[nlp.pipeline.tok2vec.model.extract.features]
@architectures = "spacy.Doc2Feats.v1"
columns = ${nlp.pipeline.tok2vec.model.extract:columns}
[nlp.pipeline.tok2vec.model.embed]
@architectures = "spacy.LayerNormalizedMaxout.v1"
width = ${nlp.pipeline.tok2vec.model.extract:width}
maxout_pieces = 4
[nlp.pipeline.tok2vec.model.encode]
@architectures = "spacy.MaxoutWindowEncoder.v1"
width = ${nlp.pipeline.tok2vec.model.extract:width}
window_size = 1
maxout_pieces = 2
depth = 2
[nlp.pipeline.ner]
factory = "ner"
[nlp.pipeline.ner.model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 6
hidden_width = 64
maxout_pieces = 2
[nlp.pipeline.ner.model.tok2vec]
@architectures = "spacy.Tok2VecTensors.v1"
width = ${nlp.pipeline.tok2vec.model.extract:width}

48
examples/experiments/tok2vec-ner/multihashembed_tok2vec.cfg Normal file
View File

@ -0,0 +1,48 @@
[training]
use_gpu = -1
limit = 0
dropout = 0.2
patience = 10000
eval_frequency = 200
scores = ["ents_p", "ents_r", "ents_f"]
score_weights = {"ents_f": 1}
orth_variant_level = 0.0
gold_preproc = true
max_length = 0
seed = 0
accumulate_gradient = 2
discard_oversize = false
[training.batch_size]
@schedules = "compounding.v1"
start = 3000
stop = 3000
compound = 1.001
[training.optimizer]
@optimizers = "Adam.v1"
learn_rate = 0.001
beta1 = 0.9
beta2 = 0.999
[nlp]
lang = "en"
vectors = null
[nlp.pipeline.ner]
factory = "simple_ner"
[nlp.pipeline.ner.model]
@architectures = "spacy.BiluoTagger.v1"
[nlp.pipeline.ner.model.tok2vec]
@architectures = "spacy.HashEmbedCNN.v1"
width = 128
depth = 4
embed_size = 7000
maxout_pieces = 3
window_size = 1
subword_features = true
pretrained_vectors = null
dropout = null

5
examples/pipeline/multi_processing.py
View File

@ -13,9 +13,10 @@ Prerequisites: pip install joblib
from __future__ import print_function, unicode_literals
from pathlib import Path
import ml_datasets
from joblib import Parallel, delayed
from functools import partial
import thinc.extra.datasets
import plac
import spacy
from spacy.util import minibatch
@ -35,7 +36,7 @@ def main(output_dir, model="en_core_web_sm", n_jobs=4, batch_size=1000, limit=10
output_dir.mkdir()
# load and pre-process the IMBD dataset
print("Loading IMDB data...")
data, _ = thinc.extra.datasets.imdb()
data, _ = ml_datasets.imdb()
texts, _ = zip(*data[-limit:])
print("Processing texts...")
partitions = minibatch(texts, size=batch_size)

18
examples/streamlit_spacy.py
View File

@ -1,7 +1,7 @@
# coding: utf-8
"""
Example of a Streamlit app for an interactive spaCy model visualizer. You can
either download the script, or point streamlit run to the raw URL of this
either download the script, or point `streamlit run` to the raw URL of this
file. For more details, see https://streamlit.io.
Installation:
@ -15,6 +15,8 @@ streamlit run streamlit_spacy.py
"""
from __future__ import unicode_literals
import base64
import streamlit as st
import spacy
from spacy import displacy
@ -54,6 +56,14 @@ model_load_state.empty()
text = st.text_area("Text to analyze", DEFAULT_TEXT)
doc = process_text(spacy_model, text)
def render_svg(svg):
"""Renders the given svg string."""
b64 = base64.b64encode(svg.encode('utf-8')).decode("utf-8")
html = r'<img src="data:image/svg+xml;base64,%s"/>' % b64
st.write(html, unsafe_allow_html=True)
if "parser" in nlp.pipe_names:
st.header("Dependency Parse & Part-of-speech tags")
st.sidebar.header("Dependency Parse")
@ -68,12 +78,14 @@ if "parser" in nlp.pipe_names:
}
docs = [span.as_doc() for span in doc.sents] if split_sents else [doc]
for sent in docs:
html = displacy.render(sent, options=options)
html = displacy.render(sent, options=options, style="dep")
# Double newlines seem to mess with the rendering
html = html.replace("\n\n", "\n")
if split_sents and len(docs) > 1:
st.markdown(f"> {sent.text}")
st.write(HTML_WRAPPER.format(html), unsafe_allow_html=True)
render_svg(html)
# this didn't show the dep arc labels properly, cf #5089
# st.write(HTML_WRAPPER.format(html), unsafe_allow_html=True)
if "ner" in nlp.pipe_names:
st.header("Named Entities")

72
examples/training/conllu.py
View File

@ -12,7 +12,7 @@ import tqdm
import spacy
import spacy.util
from spacy.tokens import Token, Doc
from spacy.gold import GoldParse
from spacy.gold import GoldParse, Example
from spacy.syntax.nonproj import projectivize
from collections import defaultdict
from spacy.matcher import Matcher
@ -33,25 +33,25 @@ random.seed(0)
numpy.random.seed(0)
def minibatch_by_words(items, size=5000):
random.shuffle(items)
def minibatch_by_words(examples, size=5000):
random.shuffle(examples)
if isinstance(size, int):
size_ = itertools.repeat(size)
else:
size_ = size
items = iter(items)
examples = iter(examples)
while True:
batch_size = next(size_)
batch = []
while batch_size >= 0:
try:
doc, gold = next(items)
example = next(examples)
except StopIteration:
if batch:
yield batch
return
batch_size -= len(doc)
batch.append((doc, gold))
batch_size -= len(example.doc)
batch.append(example)
if batch:
yield batch
else:
@ -78,7 +78,7 @@ def read_data(
max_doc_length=None,
limit=None,
):
"""Read the CONLLU format into (Doc, GoldParse) tuples. If raw_text=True,
"""Read the CONLLU format into Example objects. If raw_text=True,
include Doc objects created using nlp.make_doc and then aligned against
the gold-standard sequences. If oracle_segments=True, include Doc objects
created from the gold-standard segments. At least one must be True."""
@ -119,15 +119,15 @@ def read_data(
docs.append(doc)
golds.append(gold)
if limit and len(docs) >= limit:
return docs, golds
return golds_to_gold_data(docs, golds)
if raw_text and sent_annots:
doc, gold = _make_gold(nlp, None, sent_annots)
docs.append(doc)
golds.append(gold)
if limit and len(docs) >= limit:
return docs, golds
return docs, golds
return golds_to_gold_data(docs, golds)
return golds_to_gold_data(docs, golds)
def read_conllu(file_):
@ -181,16 +181,18 @@ def _make_gold(nlp, text, sent_annots):
#############################
def golds_to_gold_tuples(docs, golds):
"""Get out the annoying 'tuples' format used by begin_training, given the
def golds_to_gold_data(docs, golds):
"""Get out the training data format used by begin_training, given the
GoldParse objects."""
tuples = []
data = []
for doc, gold in zip(docs, golds):
text = doc.text
ids, words, tags, heads, labels, iob = zip(*gold.orig_annot)
sents = [((ids, words, tags, heads, labels, iob), [])]
tuples.append((text, sents))
return tuples
example = Example(doc=doc)
example.add_doc_annotation(cats=gold.cats)
token_annotation_dict = gold.orig.to_dict()
example.add_token_annotation(**token_annotation_dict)
example.goldparse = gold
data.append(example)
return data
##############
@ -303,7 +305,7 @@ def load_nlp(corpus, config):
return nlp
def initialize_pipeline(nlp, docs, golds, config):
def initialize_pipeline(nlp, examples, config):
nlp.add_pipe(nlp.create_pipe("parser"))
if config.multitask_tag:
nlp.parser.add_multitask_objective("tag")
@ -311,18 +313,19 @@ def initialize_pipeline(nlp, docs, golds, config):
nlp.parser.add_multitask_objective("sent_start")
nlp.parser.moves.add_action(2, "subtok")
nlp.add_pipe(nlp.create_pipe("tagger"))
for gold in golds:
for tag in gold.tags:
for ex in examples:
for tag in ex.gold.tags:
if tag is not None:
nlp.tagger.add_label(tag)
# Replace labels that didn't make the frequency cutoff
actions = set(nlp.parser.labels)
label_set = set([act.split("-")[1] for act in actions if "-" in act])
for gold in golds:
for ex in examples:
gold = ex.gold
for i, label in enumerate(gold.labels):
if label is not None and label not in label_set:
gold.labels[i] = label.split("||")[0]
return nlp.begin_training(lambda: golds_to_gold_tuples(docs, golds))
return nlp.begin_training(lambda: examples)
########################
@ -391,13 +394,17 @@ def main(ud_dir, parses_dir, config, corpus, limit=0):
Token.set_extension("begins_fused", default=False)
Token.set_extension("inside_fused", default=False)
Token.set_extension("get_conllu_lines", method=get_token_conllu)
Token.set_extension("begins_fused", default=False)
Token.set_extension("inside_fused", default=False)
paths = TreebankPaths(ud_dir, corpus)
if not (parses_dir / corpus).exists():
(parses_dir / corpus).mkdir()
print("Train and evaluate", corpus, "using lang", paths.lang)
nlp = load_nlp(paths.lang, config)
docs, golds = read_data(
examples = read_data(
nlp,
paths.train.conllu.open(encoding="utf8"),
paths.train.text.open(encoding="utf8"),
@ -405,23 +412,18 @@ def main(ud_dir, parses_dir, config, corpus, limit=0):
limit=limit,
)
optimizer = initialize_pipeline(nlp, docs, golds, config)
optimizer = initialize_pipeline(nlp, examples, config)
for i in range(config.nr_epoch):
docs = [nlp.make_doc(doc.text) for doc in docs]
batches = minibatch_by_words(list(zip(docs, golds)), size=config.batch_size)
docs = [nlp.make_doc(example.doc.text) for example in examples]
batches = minibatch_by_words(examples, size=config.batch_size)
losses = {}
n_train_words = sum(len(doc) for doc in docs)
with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
for batch in batches:
batch_docs, batch_gold = zip(*batch)
pbar.update(sum(len(doc) for doc in batch_docs))
pbar.update(sum(len(ex.doc) for ex in batch))
nlp.update(
batch_docs,
batch_gold,
sgd=optimizer,
drop=config.dropout,
losses=losses,
examples=batch, sgd=optimizer, drop=config.dropout, losses=losses,
)
out_path = parses_dir / corpus / "epoch-{i}.conllu".format(i=i)

29
examples/training/ner_multitask_objective.py
View File

@ -31,14 +31,13 @@ random.seed(0)
PWD = os.path.dirname(__file__)
TRAIN_DATA = list(read_json_file(
os.path.join(PWD, "ner_example_data", "ner-sent-per-line.json")))
TRAIN_DATA = list(read_json_file(os.path.join(PWD, "training-data.json")))
def get_position_label(i, words, tags, heads, labels, ents):
def get_position_label(i, token_annotation):
"""Return labels indicating the position of the word in the document.
"""
if len(words) < 20:
if len(token_annotation.words) < 20:
return "short-doc"
elif i == 0:
return "first-word"
@ -46,7 +45,7 @@ def get_position_label(i, words, tags, heads, labels, ents):
return "early-word"
elif i < 20:
return "mid-word"
elif i == len(words) - 1:
elif i == len(token_annotation.words) - 1:
return "last-word"
else:
return "late-word"
@ -60,17 +59,17 @@ def main(n_iter=10):
print(nlp.pipeline)
print("Create data", len(TRAIN_DATA))
optimizer = nlp.begin_training(get_gold_tuples=lambda: TRAIN_DATA)
optimizer = nlp.begin_training(get_examples=lambda: TRAIN_DATA)
for itn in range(n_iter):
random.shuffle(TRAIN_DATA)
losses = {}
for text, annot_brackets in TRAIN_DATA:
for annotations, _ in annot_brackets:
doc = Doc(nlp.vocab, words=annotations[1])
gold = GoldParse.from_annot_tuples(doc, annotations)
for example in TRAIN_DATA:
for token_annotation in example.token_annotations:
doc = Doc(nlp.vocab, words=token_annotation.words)
gold = GoldParse.from_annotation(doc, example.doc_annotation, token_annotation)
nlp.update(
[doc], # batch of texts
[gold], # batch of annotations
examples=[(doc, gold)], # 1 example
drop=0.2, # dropout - make it harder to memorise data
sgd=optimizer, # callable to update weights
losses=losses,
@ -78,9 +77,9 @@ def main(n_iter=10):
print(losses.get("nn_labeller", 0.0), losses["ner"])
# test the trained model
for text, _ in TRAIN_DATA:
if text is not None:
doc = nlp(text)
for example in TRAIN_DATA:
if example.text is not None:
doc = nlp(example.text)
print("Entities", [(ent.text, ent.label_) for ent in doc.ents])
print("Tokens", [(t.text, t.ent_type_, t.ent_iob) for t in doc])

217
examples/training/pretrain_textcat.py
View File

@ -1,217 +0,0 @@
"""This script is experimental.
Try pre-training the CNN component of the text categorizer using a cheap
language modelling-like objective. Specifically, we load pretrained vectors
(from something like word2vec, GloVe, FastText etc), and use the CNN to
predict the tokens' pretrained vectors. This isn't as easy as it sounds:
we're not merely doing compression here, because heavy dropout is applied,
including over the input words. This means the model must often (50% of the time)
use the context in order to predict the word.
To evaluate the technique, we're pre-training with the 50k texts from the IMDB
corpus, and then training with only 100 labels. Note that it's a bit dirty to
pre-train with the development data, but also not *so* terrible: we're not using
the development labels, after all --- only the unlabelled text.
"""
import plac
import tqdm
import random
import spacy
import thinc.extra.datasets
from spacy.util import minibatch, use_gpu, compounding
from spacy._ml import Tok2Vec
from spacy.pipeline import TextCategorizer
import numpy
def load_texts(limit=0):
train, dev = thinc.extra.datasets.imdb()
train_texts, train_labels = zip(*train)
dev_texts, dev_labels = zip(*train)
train_texts = list(train_texts)
dev_texts = list(dev_texts)
random.shuffle(train_texts)
random.shuffle(dev_texts)
if limit >= 1:
return train_texts[:limit]
else:
return list(train_texts) + list(dev_texts)
def load_textcat_data(limit=0):
"""Load data from the IMDB dataset."""
# Partition off part of the train data for evaluation
train_data, eval_data = thinc.extra.datasets.imdb()
random.shuffle(train_data)
train_data = train_data[-limit:]
texts, labels = zip(*train_data)
eval_texts, eval_labels = zip(*eval_data)
cats = [{"POSITIVE": bool(y), "NEGATIVE": not bool(y)} for y in labels]
eval_cats = [{"POSITIVE": bool(y), "NEGATIVE": not bool(y)} for y in eval_labels]
return (texts, cats), (eval_texts, eval_cats)
def prefer_gpu():
used = spacy.util.use_gpu(0)
if used is None:
return False
else:
import cupy.random
cupy.random.seed(0)
return True
def build_textcat_model(tok2vec, nr_class, width):
from thinc.v2v import Model, Softmax, Maxout
from thinc.api import flatten_add_lengths, chain
from thinc.t2v import Pooling, sum_pool, mean_pool, max_pool
from thinc.misc import Residual, LayerNorm
from spacy._ml import logistic, zero_init
with Model.define_operators({">>": chain}):
model = (
tok2vec
>> flatten_add_lengths
>> Pooling(mean_pool)
>> Softmax(nr_class, width)
)
model.tok2vec = tok2vec
return model
def block_gradients(model):
from thinc.api import wrap
def forward(X, drop=0.0):
Y, _ = model.begin_update(X, drop=drop)
return Y, None
return wrap(forward, model)
def create_pipeline(width, embed_size, vectors_model):
print("Load vectors")
nlp = spacy.load(vectors_model)
print("Start training")
textcat = TextCategorizer(
nlp.vocab,
labels=["POSITIVE", "NEGATIVE"],
model=build_textcat_model(
Tok2Vec(width=width, embed_size=embed_size), 2, width
),
)
nlp.add_pipe(textcat)
return nlp
def train_tensorizer(nlp, texts, dropout, n_iter):
tensorizer = nlp.create_pipe("tensorizer")
nlp.add_pipe(tensorizer)
optimizer = nlp.begin_training()
for i in range(n_iter):
losses = {}
for i, batch in enumerate(minibatch(tqdm.tqdm(texts))):
docs = [nlp.make_doc(text) for text in batch]
tensorizer.update(docs, None, losses=losses, sgd=optimizer, drop=dropout)
print(losses)
return optimizer
def train_textcat(nlp, n_texts, n_iter=10):
textcat = nlp.get_pipe("textcat")
tok2vec_weights = textcat.model.tok2vec.to_bytes()
(train_texts, train_cats), (dev_texts, dev_cats) = load_textcat_data(limit=n_texts)
print(
"Using {} examples ({} training, {} evaluation)".format(
n_texts, len(train_texts), len(dev_texts)
)
)
train_data = list(zip(train_texts, [{"cats": cats} for cats in train_cats]))
# get names of other pipes to disable them during training
pipe_exceptions = ["textcat", "trf_wordpiecer", "trf_tok2vec"]
other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
with nlp.disable_pipes(*other_pipes): # only train textcat
optimizer = nlp.begin_training()
textcat.model.tok2vec.from_bytes(tok2vec_weights)
print("Training the model...")
print("{:^5}\t{:^5}\t{:^5}\t{:^5}".format("LOSS", "P", "R", "F"))
for i in range(n_iter):
losses = {"textcat": 0.0}
# batch up the examples using spaCy's minibatch
batches = minibatch(tqdm.tqdm(train_data), size=2)
for batch in batches:
texts, annotations = zip(*batch)
nlp.update(texts, annotations, sgd=optimizer, drop=0.2, losses=losses)
with textcat.model.use_params(optimizer.averages):
# evaluate on the dev data split off in load_data()
scores = evaluate_textcat(nlp.tokenizer, textcat, dev_texts, dev_cats)
print(
"{0:.3f}\t{1:.3f}\t{2:.3f}\t{3:.3f}".format( # print a simple table
losses["textcat"],
scores["textcat_p"],
scores["textcat_r"],
scores["textcat_f"],
)
)
def evaluate_textcat(tokenizer, textcat, texts, cats):
docs = (tokenizer(text) for text in texts)
tp = 1e-8
fp = 1e-8
tn = 1e-8
fn = 1e-8
for i, doc in enumerate(textcat.pipe(docs)):
gold = cats[i]
for label, score in doc.cats.items():
if label not in gold:
continue
if score >= 0.5 and gold[label] >= 0.5:
tp += 1.0
elif score >= 0.5 and gold[label] < 0.5:
fp += 1.0
elif score < 0.5 and gold[label] < 0.5:
tn += 1
elif score < 0.5 and gold[label] >= 0.5:
fn += 1
precision = tp / (tp + fp)
recall = tp / (tp + fn)
f_score = 2 * (precision * recall) / (precision + recall)
return {"textcat_p": precision, "textcat_r": recall, "textcat_f": f_score}
@plac.annotations(
width=("Width of CNN layers", "positional", None, int),
embed_size=("Embedding rows", "positional", None, int),
pretrain_iters=("Number of iterations to pretrain", "option", "pn", int),
train_iters=("Number of iterations to train", "option", "tn", int),
train_examples=("Number of labelled examples", "option", "eg", int),
vectors_model=("Name or path to vectors model to learn from"),
)
def main(
width,
embed_size,
vectors_model,
pretrain_iters=30,
train_iters=30,
train_examples=1000,
):
random.seed(0)
numpy.random.seed(0)
use_gpu = prefer_gpu()
print("Using GPU?", use_gpu)
nlp = create_pipeline(width, embed_size, vectors_model)
print("Load data")
texts = load_texts(limit=0)
print("Train tensorizer")
optimizer = train_tensorizer(nlp, texts, dropout=0.2, n_iter=pretrain_iters)
print("Train textcat")
train_textcat(nlp, train_examples, n_iter=train_iters)
if __name__ == "__main__":
plac.call(main)

12
examples/training/rehearsal.py
View File

@ -59,17 +59,14 @@ def main(model_name, unlabelled_loc):
# yet, but I'm getting weird results from Adam. Try commenting out the
# nlp.update(), and using Adam -- you'll find the models drift apart.
# I guess Adam is losing precision, introducing gradient noise?
optimizer.alpha = 0.1
optimizer.learn_rate = 0.1
optimizer.b1 = 0.0
optimizer.b2 = 0.0
# get names of other pipes to disable them during training
pipe_exceptions = ["ner", "trf_wordpiecer", "trf_tok2vec"]
other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
sizes = compounding(1.0, 4.0, 1.001)
with nlp.disable_pipes(*other_pipes) and warnings.catch_warnings():
with nlp.select_pipes(enable="ner") and warnings.catch_warnings():
# show warnings for misaligned entity spans once
warnings.filterwarnings("once", category=UserWarning, module='spacy')
warnings.filterwarnings("once", category=UserWarning, module="spacy")
for itn in range(n_iter):
random.shuffle(TRAIN_DATA)
@ -79,8 +76,7 @@ def main(model_name, unlabelled_loc):
# batch up the examples using spaCy's minibatch
raw_batches = minibatch(raw_docs, size=4)
for batch in minibatch(TRAIN_DATA, size=sizes):
docs, golds = zip(*batch)
nlp.update(docs, golds, sgd=optimizer, drop=dropout, losses=losses)
nlp.update(batch, sgd=optimizer, drop=dropout, losses=losses)
raw_batch = list(next(raw_batches))
nlp.rehearse(raw_batch, sgd=optimizer, losses=r_losses)
print("Losses", losses)

6
examples/training/textcat_example_data/textcatjsonl_to_trainjson.py
View File

@ -5,16 +5,17 @@ from spacy.gold import docs_to_json
import srsly
import sys
@plac.annotations(
model=("Model name. Defaults to 'en'.", "option", "m", str),
input_file=("Input file (jsonl)", "positional", None, Path),
output_dir=("Output directory", "positional", None, Path),
n_texts=("Number of texts to convert", "option", "t", int),
)
def convert(model='en', input_file=None, output_dir=None, n_texts=0):
def convert(model="en", input_file=None, output_dir=None, n_texts=0):
# Load model with tokenizer + sentencizer only
nlp = spacy.load(model)
nlp.disable_pipes(*nlp.pipe_names)
nlp.select_pipes(disable=nlp.pipe_names)
sentencizer = nlp.create_pipe("sentencizer")
nlp.add_pipe(sentencizer, first=True)
@ -49,5 +50,6 @@ def convert(model='en', input_file=None, output_dir=None, n_texts=0):
srsly.write_json(output_dir / input_file.with_suffix(".json"), [docs_to_json(docs)])
if __name__ == "__main__":
plac.call(convert)

31
examples/training/train_entity_linker.py
View File

@ -18,7 +18,6 @@ import random
from pathlib import Path
from spacy.vocab import Vocab
import spacy
from spacy.kb import KnowledgeBase
from spacy.pipeline import EntityRuler
@ -66,36 +65,38 @@ def main(kb_path, vocab_path=None, output_dir=None, n_iter=50):
vocab = Vocab().from_disk(vocab_path)
# create blank English model with correct vocab
nlp = spacy.blank("en", vocab=vocab)
nlp.vocab.vectors.name = "spacy_pretrained_vectors"
nlp.vocab.vectors.name = "nel_vectors"
print("Created blank 'en' model with vocab from '%s'" % vocab_path)
# Add a sentencizer component. Alternatively, add a dependency parser for higher accuracy.
nlp.add_pipe(nlp.create_pipe('sentencizer'))
nlp.add_pipe(nlp.create_pipe("sentencizer"))
# Add a custom component to recognize "Russ Cochran" as an entity for the example training data.
# Note that in a realistic application, an actual NER algorithm should be used instead.
ruler = EntityRuler(nlp)
patterns = [{"label": "PERSON", "pattern": [{"LOWER": "russ"}, {"LOWER": "cochran"}]}]
patterns = [
{"label": "PERSON", "pattern": [{"LOWER": "russ"}, {"LOWER": "cochran"}]}
]
ruler.add_patterns(patterns)
nlp.add_pipe(ruler)
# Create the Entity Linker component and add it to the pipeline.
if "entity_linker" not in nlp.pipe_names:
# use only the predicted EL score and not the prior probability (for demo purposes)
cfg = {"incl_prior": False}
entity_linker = nlp.create_pipe("entity_linker", cfg)
kb = KnowledgeBase(vocab=nlp.vocab)
kb.load_bulk(kb_path)
print("Loaded Knowledge Base from '%s'" % kb_path)
entity_linker.set_kb(kb)
# use only the predicted EL score and not the prior probability (for demo purposes)
cfg = {"kb": kb, "incl_prior": False}
entity_linker = nlp.create_pipe("entity_linker", cfg)
nlp.add_pipe(entity_linker, last=True)
# Convert the texts to docs to make sure we have doc.ents set for the training examples.
# Also ensure that the annotated examples correspond to known identifiers in the knowlege base.
# Also ensure that the annotated examples correspond to known identifiers in the knowledge base.
kb_ids = nlp.get_pipe("entity_linker").kb.get_entity_strings()
TRAIN_DOCS = []
for text, annotation in TRAIN_DATA:
with nlp.disable_pipes("entity_linker"):
with nlp.select_pipes(disable="entity_linker"):
doc = nlp(text)
annotation_clean = annotation
for offset, kb_id_dict in annotation["links"].items():
@ -110,22 +111,18 @@ def main(kb_path, vocab_path=None, output_dir=None, n_iter=50):
annotation_clean["links"][offset] = new_dict
TRAIN_DOCS.append((doc, annotation_clean))
# get names of other pipes to disable them during training
pipe_exceptions = ["entity_linker", "trf_wordpiecer", "trf_tok2vec"]
other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
with nlp.disable_pipes(*other_pipes): # only train entity linker
with nlp.select_pipes(enable="entity_linker"): # only train entity linker
# reset and initialize the weights randomly
optimizer = nlp.begin_training()
for itn in range(n_iter):
random.shuffle(TRAIN_DOCS)
losses = {}
# batch up the examples using spaCy's minibatch
batches = minibatch(TRAIN_DOCS, size=compounding(4.0, 32.0, 1.001))
for batch in batches:
texts, annotations = zip(*batch)
nlp.update(
texts, # batch of texts
annotations, # batch of annotations
batch,
drop=0.2, # dropout - make it harder to memorise data
losses=losses,
sgd=optimizer,

7
examples/training/train_intent_parser.py
View File

@ -124,9 +124,7 @@ def main(model=None, output_dir=None, n_iter=15):
for dep in annotations.get("deps", []):
parser.add_label(dep)
pipe_exceptions = ["parser", "trf_wordpiecer", "trf_tok2vec"]
other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
with nlp.disable_pipes(*other_pipes): # only train parser
with nlp.select_pipes(enable="parser"): # only train parser
optimizer = nlp.begin_training()
for itn in range(n_iter):
random.shuffle(TRAIN_DATA)
@ -134,8 +132,7 @@ def main(model=None, output_dir=None, n_iter=15):
# batch up the examples using spaCy's minibatch
batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
for batch in batches:
texts, annotations = zip(*batch)
nlp.update(texts, annotations, sgd=optimizer, losses=losses)
nlp.update(batch, sgd=optimizer, losses=losses)
print("Losses", losses)
# test the trained model

133
examples/training/train_morphologizer.py Normal file
View File

@ -0,0 +1,133 @@
#!/usr/bin/env python
# coding: utf8
"""
A simple example for training a morphologizer. For more details, see
the documentation:
* Training: https://spacy.io/usage/training
Compatible with: spaCy v3.0.0+
Last tested with: v3.0.0
"""
from __future__ import unicode_literals, print_function
import plac
import random
from pathlib import Path
import spacy
from spacy.util import minibatch, compounding
from spacy.morphology import Morphology
# Usually you'll read this in, of course. Data formats vary. Ensure your
# strings are unicode and that the number of tags assigned matches spaCy's
# tokenization. If not, you can always add a 'words' key to the annotations
# that specifies the gold-standard tokenization, e.g.:
# ("Eatblueham", {'words': ['Eat', 'blue', 'ham'], 'tags': ['V', 'J', 'N']})
TRAIN_DATA = [
(
"I like green eggs",
{
"morphs": [
"PronType=Prs|Person=1",
"VerbForm=Fin",
"Degree=Pos",
"Number=Plur",
],
"pos": ["PRON", "VERB", "ADJ", "NOUN"],
},
),
(
"Eat blue ham",
{
"morphs": ["VerbForm=Inf", "Degree=Pos", "Number=Sing"],
"pos": ["VERB", "ADJ", "NOUN"],
},
),
(
"She was blue",
{
"morphs": ["PronType=Prs|Person=3", "VerbForm=Fin", "Degree=Pos"],
"pos": ["PRON", "VERB", "ADJ"],
},
),
(
"He was blue today",
{
"morphs": ["PronType=Prs|Person=3", "VerbForm=Fin", "Degree=Pos", ""],
"pos": ["PRON", "VERB", "ADJ", "ADV"],
},
),
]
# The POS tags are optional, set `with_pos_tags = False` to omit them for
# this example:
with_pos_tags = True
if not with_pos_tags:
for i in range(len(TRAIN_DATA)):
del TRAIN_DATA[i][1]["pos"]
@plac.annotations(
lang=("ISO Code of language to use", "option", "l", str),
output_dir=("Optional output directory", "option", "o", Path),
n_iter=("Number of training iterations", "option", "n", int),
)
def main(lang="en", output_dir=None, n_iter=25):
"""Create a new model, set up the pipeline and train the tagger. In order to
train the tagger with a custom tag map, we're creating a new Language
instance with a custom vocab.
"""
nlp = spacy.blank(lang)
# add the tagger to the pipeline
# nlp.create_pipe works for built-ins that are registered with spaCy
morphologizer = nlp.create_pipe("morphologizer")
nlp.add_pipe(morphologizer)
# add labels
for _, annotations in TRAIN_DATA:
morph_labels = annotations.get("morphs")
pos_labels = annotations.get("pos", [""] * len(annotations.get("morphs")))
assert len(morph_labels) == len(pos_labels)
for morph, pos in zip(morph_labels, pos_labels):
morph_dict = Morphology.feats_to_dict(morph)
if pos:
morph_dict["POS"] = pos
morph = Morphology.dict_to_feats(morph_dict)
morphologizer.add_label(morph)
optimizer = nlp.begin_training()
for i in range(n_iter):
random.shuffle(TRAIN_DATA)
losses = {}
# batch up the examples using spaCy's minibatch
batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
for batch in batches:
nlp.update(batch, sgd=optimizer, losses=losses)
print("Losses", losses)
# test the trained model
test_text = "I like blue eggs"
doc = nlp(test_text)
print("Morphs", [(t.text, t.morph) for t in doc])
# save model to output directory
if output_dir is not None:
output_dir = Path(output_dir)
if not output_dir.exists():
output_dir.mkdir()
nlp.to_disk(output_dir)
print("Saved model to", output_dir)
# test the save model
print("Loading from", output_dir)
nlp2 = spacy.load(output_dir)
doc = nlp2(test_text)
print("Morphs", [(t.text, t.morph) for t in doc])
if __name__ == "__main__":
plac.call(main)
# Expected output:
# Morphs [('I', POS=PRON|Person=1|PronType=Prs), ('like', POS=VERB|VerbForm=Fin), ('blue', Degree=Pos|POS=ADJ), ('eggs', Number=Plur|POS=NOUN)]

24
examples/training/train_ner.py
View File

@ -43,41 +43,39 @@ def main(model=None, output_dir=None, n_iter=100):
# create the built-in pipeline components and add them to the pipeline
# nlp.create_pipe works for built-ins that are registered with spaCy
if "ner" not in nlp.pipe_names:
ner = nlp.create_pipe("ner")
if "simple_ner" not in nlp.pipe_names:
ner = nlp.create_pipe("simple_ner")
nlp.add_pipe(ner, last=True)
# otherwise, get it so we can add labels
else:
ner = nlp.get_pipe("ner")
ner = nlp.get_pipe("simple_ner")
# add labels
for _, annotations in TRAIN_DATA:
for ent in annotations.get("entities"):
print("Add label", ent[2])
ner.add_label(ent[2])
# get names of other pipes to disable them during training
pipe_exceptions = ["ner", "trf_wordpiecer", "trf_tok2vec"]
other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
# only train NER
with nlp.disable_pipes(*other_pipes) and warnings.catch_warnings():
with nlp.select_pipes(enable="ner") and warnings.catch_warnings():
# show warnings for misaligned entity spans once
warnings.filterwarnings("once", category=UserWarning, module='spacy')
warnings.filterwarnings("once", category=UserWarning, module="spacy")
# reset and initialize the weights randomly – but only if we're
# training a new model
if model is None:
nlp.begin_training()
print(
"Transitions", list(enumerate(nlp.get_pipe("simple_ner").get_tag_names()))
)
for itn in range(n_iter):
random.shuffle(TRAIN_DATA)
losses = {}
# batch up the examples using spaCy's minibatch
batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
for batch in batches:
texts, annotations = zip(*batch)
nlp.update(
texts, # batch of texts
annotations, # batch of annotations
drop=0.5, # dropout - make it harder to memorise data
batch,
drop=0.0, # dropout - make it harder to memorise data
losses=losses,
)
print("Losses", losses)

11
examples/training/train_new_entity_type.py
View File

@ -95,13 +95,9 @@ def main(model=None, new_model_name="animal", output_dir=None, n_iter=30):
else:
optimizer = nlp.resume_training()
move_names = list(ner.move_names)
# get names of other pipes to disable them during training
pipe_exceptions = ["ner", "trf_wordpiecer", "trf_tok2vec"]
other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
# only train NER
with nlp.disable_pipes(*other_pipes) and warnings.catch_warnings():
with nlp.select_pipes(enable="ner") and warnings.catch_warnings():
# show warnings for misaligned entity spans once
warnings.filterwarnings("once", category=UserWarning, module='spacy')
warnings.filterwarnings("once", category=UserWarning, module="spacy")
sizes = compounding(1.0, 4.0, 1.001)
# batch up the examples using spaCy's minibatch
@ -110,8 +106,7 @@ def main(model=None, new_model_name="animal", output_dir=None, n_iter=30):
batches = minibatch(TRAIN_DATA, size=sizes)
losses = {}
for batch in batches:
texts, annotations = zip(*batch)
nlp.update(texts, annotations, sgd=optimizer, drop=0.35, losses=losses)
nlp.update(batch, sgd=optimizer, drop=0.35, losses=losses)
print("Losses", losses)
# test the trained model

8
examples/training/train_parser.py
View File

@ -64,10 +64,7 @@ def main(model=None, output_dir=None, n_iter=15):
for dep in annotations.get("deps", []):
parser.add_label(dep)
# get names of other pipes to disable them during training
pipe_exceptions = ["parser", "trf_wordpiecer", "trf_tok2vec"]
other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
with nlp.disable_pipes(*other_pipes): # only train parser
with nlp.select_pipes(enable="parser"): # only train parser
optimizer = nlp.begin_training()
for itn in range(n_iter):
random.shuffle(TRAIN_DATA)
@ -75,8 +72,7 @@ def main(model=None, output_dir=None, n_iter=15):
# batch up the examples using spaCy's minibatch
batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
for batch in batches:
texts, annotations = zip(*batch)
nlp.update(texts, annotations, sgd=optimizer, losses=losses)
nlp.update(batch, sgd=optimizer, losses=losses)
print("Losses", losses)
# test the trained model

3
examples/training/train_tagger.py
View File

@ -65,8 +65,7 @@ def main(lang="en", output_dir=None, n_iter=25):
# batch up the examples using spaCy's minibatch
batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
for batch in batches:
texts, annotations = zip(*batch)
nlp.update(texts, annotations, sgd=optimizer, losses=losses)
nlp.update(batch, sgd=optimizer, losses=losses)
print("Losses", losses)
# test the trained model

120
examples/training/train_textcat.py
View File

@ -2,89 +2,87 @@
# coding: utf8
"""Train a convolutional neural network text classifier on the
IMDB dataset, using the TextCategorizer component. The dataset will be loaded
automatically via Thinc's built-in dataset loader. The model is added to
automatically via the package `ml_datasets`. The model is added to
spacy.pipeline, and predictions are available via `doc.cats`. For more details,
see the documentation:
* Training: https://spacy.io/usage/training
Compatible with: spaCy v2.0.0+
Compatible with: spaCy v3.0.0+
"""
from __future__ import unicode_literals, print_function
import plac
import random
from pathlib import Path
import thinc.extra.datasets
from ml_datasets import loaders
import spacy
from spacy import util
from spacy.util import minibatch, compounding
from spacy.gold import Example, GoldParse
@plac.annotations(
model=("Model name. Defaults to blank 'en' model.", "option", "m", str),
config_path=("Path to config file", "positional", None, Path),
output_dir=("Optional output directory", "option", "o", Path),
n_texts=("Number of texts to train from", "option", "t", int),
n_iter=("Number of training iterations", "option", "n", int),
init_tok2vec=("Pretrained tok2vec weights", "option", "t2v", Path),
dataset=("Dataset to train on (default: imdb)", "option", "d", str),
threshold=("Min. number of instances for a given label (default 20)", "option", "m", int)
)
def main(model=None, output_dir=None, n_iter=20, n_texts=2000, init_tok2vec=None):
def main(config_path, output_dir=None, n_iter=20, n_texts=2000, init_tok2vec=None, dataset="imdb", threshold=20):
if not config_path or not config_path.exists():
raise ValueError(f"Config file not found at {config_path}")
spacy.util.fix_random_seed()
if output_dir is not None:
output_dir = Path(output_dir)
if not output_dir.exists():
output_dir.mkdir()
if model is not None:
nlp = spacy.load(model) # load existing spaCy model
print("Loaded model '%s'" % model)
else:
nlp = spacy.blank("en") # create blank Language class
print("Created blank 'en' model")
print(f"Loading nlp model from {config_path}")
nlp_config = util.load_config(config_path, create_objects=False)["nlp"]
nlp = util.load_model_from_config(nlp_config)
# add the text classifier to the pipeline if it doesn't exist
# nlp.create_pipe works for built-ins that are registered with spaCy
# ensure the nlp object was defined with a textcat component
if "textcat" not in nlp.pipe_names:
textcat = nlp.create_pipe(
"textcat", config={"exclusive_classes": True, "architecture": "simple_cnn"}
)
nlp.add_pipe(textcat, last=True)
# otherwise, get it, so we can add labels to it
else:
textcat = nlp.get_pipe("textcat")
raise ValueError(f"The nlp definition in the config does not contain a textcat component")
# add label to text classifier
textcat.add_label("POSITIVE")
textcat.add_label("NEGATIVE")
textcat = nlp.get_pipe("textcat")
# load the IMDB dataset
print("Loading IMDB data...")
(train_texts, train_cats), (dev_texts, dev_cats) = load_data()
train_texts = train_texts[:n_texts]
train_cats = train_cats[:n_texts]
# load the dataset
print(f"Loading dataset {dataset} ...")
(train_texts, train_cats), (dev_texts, dev_cats) = load_data(dataset=dataset, threshold=threshold, limit=n_texts)
print(
"Using {} examples ({} training, {} evaluation)".format(
n_texts, len(train_texts), len(dev_texts)
)
)
train_data = list(zip(train_texts, [{"cats": cats} for cats in train_cats]))
train_examples = []
for text, cats in zip(train_texts, train_cats):
doc = nlp.make_doc(text)
gold = GoldParse(doc, cats=cats)
for cat in cats:
textcat.add_label(cat)
ex = Example.from_gold(gold, doc=doc)
train_examples.append(ex)
# get names of other pipes to disable them during training
pipe_exceptions = ["textcat", "trf_wordpiecer", "trf_tok2vec"]
other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
with nlp.disable_pipes(*other_pipes): # only train textcat
with nlp.select_pipes(enable="textcat"): # only train textcat
optimizer = nlp.begin_training()
if init_tok2vec is not None:
with init_tok2vec.open("rb") as file_:
textcat.model.tok2vec.from_bytes(file_.read())
textcat.model.get_ref("tok2vec").from_bytes(file_.read())
print("Training the model...")
print("{:^5}\t{:^5}\t{:^5}\t{:^5}".format("LOSS", "P", "R", "F"))
batch_sizes = compounding(4.0, 32.0, 1.001)
for i in range(n_iter):
losses = {}
# batch up the examples using spaCy's minibatch
random.shuffle(train_data)
batches = minibatch(train_data, size=batch_sizes)
random.shuffle(train_examples)
batches = minibatch(train_examples, size=batch_sizes)
for batch in batches:
texts, annotations = zip(*batch)
nlp.update(texts, annotations, sgd=optimizer, drop=0.2, losses=losses)
nlp.update(batch, sgd=optimizer, drop=0.2, losses=losses)
with textcat.model.use_params(optimizer.averages):
# evaluate on the dev data split off in load_data()
scores = evaluate(nlp.tokenizer, textcat, dev_texts, dev_cats)
@ -97,7 +95,7 @@ def main(model=None, output_dir=None, n_iter=20, n_texts=2000, init_tok2vec=None
)
)
# test the trained model
# test the trained model (only makes sense for sentiment analysis)
test_text = "This movie sucked"
doc = nlp(test_text)
print(test_text, doc.cats)
@ -114,14 +112,48 @@ def main(model=None, output_dir=None, n_iter=20, n_texts=2000, init_tok2vec=None
print(test_text, doc2.cats)
def load_data(limit=0, split=0.8):
"""Load data from the IMDB dataset."""
def load_data(dataset, threshold, limit=0, split=0.8):
"""Load data from the provided dataset."""
# Partition off part of the train data for evaluation
train_data, _ = thinc.extra.datasets.imdb()
data_loader = loaders.get(dataset)
train_data, _ = data_loader(limit=int(limit/split))
random.shuffle(train_data)
train_data = train_data[-limit:]
texts, labels = zip(*train_data)
cats = [{"POSITIVE": bool(y), "NEGATIVE": not bool(y)} for y in labels]
unique_labels = set()
for label_set in labels:
if isinstance(label_set, int) or isinstance(label_set, str):
unique_labels.add(label_set)
elif isinstance(label_set, list) or isinstance(label_set, set):
unique_labels.update(label_set)
unique_labels = sorted(unique_labels)
print(f"# of unique_labels: {len(unique_labels)}")
count_values_train = dict()
for text, annot_list in train_data:
if isinstance(annot_list, int) or isinstance(annot_list, str):
count_values_train[annot_list] = count_values_train.get(annot_list, 0) + 1
else:
for annot in annot_list:
count_values_train[annot] = count_values_train.get(annot, 0) + 1
for value, count in sorted(count_values_train.items(), key=lambda item: item[1]):
if count < threshold:
unique_labels.remove(value)
print(f"# of unique_labels after filtering with threshold {threshold}: {len(unique_labels)}")
if unique_labels == {0, 1}:
cats = [{"POSITIVE": bool(y), "NEGATIVE": not bool(y)} for y in labels]
else:
cats = []
for y in labels:
if isinstance(y, str) or isinstance(y, int):
cats.append({str(label): (label == y) for label in unique_labels})
elif isinstance(y, set):
cats.append({str(label): (label in y) for label in unique_labels})
else:
raise ValueError(f"Unrecognised type of labels: {type(y)}")
split = int(len(train_data) * split)
return (texts[:split], cats[:split]), (texts[split:], cats[split:])

19
examples/training/train_textcat_config.cfg Normal file
View File

@ -0,0 +1,19 @@
[nlp]
lang = "en"
[nlp.pipeline.textcat]
factory = "textcat"
[nlp.pipeline.textcat.model]
@architectures = "spacy.TextCatCNN.v1"
exclusive_classes = false
[nlp.pipeline.textcat.model.tok2vec]
@architectures = "spacy.HashEmbedCNN.v1"
pretrained_vectors = null
width = 96
depth = 4
embed_size = 2000
window_size = 1
maxout_pieces = 3
subword_features = true

9
fabfile.py vendored
View File

@ -1,9 +1,6 @@
# coding: utf-8
from __future__ import unicode_literals, print_function
import contextlib
from pathlib import Path
from fabric.api import local, lcd, env, settings, prefix
from fabric.api import local, lcd
from os import path, environ
import shutil
import sys
@ -82,9 +79,7 @@ def pex():
with virtualenv(VENV_DIR) as venv_local:
with lcd(path.dirname(__file__)):
sha = local("git rev-parse --short HEAD", capture=True)
venv_local(
"pex dist/*.whl -e spacy -o dist/spacy-%s.pex" % sha, direct=True
)
venv_local(f"pex dist/*.whl -e spacy -o dist/spacy-{sha}.pex", direct=True)
def clean():

3
pyproject.toml
View File

@ -6,6 +6,7 @@ requires = [
"cymem>=2.0.2,<2.1.0",
"preshed>=3.0.2,<3.1.0",
"murmurhash>=0.28.0,<1.1.0",
"thinc==7.4.1",
"thinc==8.0.0a9",
"blis>=0.4.0,<0.5.0"
]
build-backend = "setuptools.build_meta"

13
requirements.txt
View File

@ -1,20 +1,23 @@
# Our libraries
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
thinc==7.4.1
thinc==8.0.0a9
blis>=0.4.0,<0.5.0
ml_datasets>=0.1.1
murmurhash>=0.28.0,<1.1.0
wasabi>=0.4.0,<1.1.0
srsly>=1.0.2,<1.1.0
srsly>=2.0.0,<3.0.0
catalogue>=0.0.7,<1.1.0
# Third party dependencies
numpy>=1.15.0
requests>=2.13.0,<3.0.0
plac>=0.9.6,<1.2.0
pathlib==1.0.1; python_version < "3.4"
tqdm>=4.38.0,<5.0.0
# Optional dependencies
jsonschema>=2.6.0,<3.1.0
pydantic>=1.3.0,<2.0.0
# Official Python utilities
setuptools
packaging
importlib_metadata>=0.20; python_version < "3.8"
# Development dependencies
cython>=0.25
pytest>=4.6.5

20
setup.cfg
View File

@ -16,10 +16,7 @@ classifiers =
Operating System :: MacOS :: MacOS X
Operating System :: Microsoft :: Windows
Programming Language :: Cython
Programming Language :: Python :: 2
Programming Language :: Python :: 2.7
Programming Language :: Python :: 3
Programming Language :: Python :: 3.5
Programming Language :: Python :: 3.6
Programming Language :: Python :: 3.7
Programming Language :: Python :: 3.8
@ -30,32 +27,37 @@ zip_safe = false
include_package_data = true
scripts =
bin/spacy
python_requires = >=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*
python_requires = >=3.6
setup_requires =
wheel
cython>=0.25
numpy>=1.15.0
# We also need our Cython packages here to compile against
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
murmurhash>=0.28.0,<1.1.0
thinc==7.4.1
thinc==8.0.0a9
install_requires =
# Our libraries
murmurhash>=0.28.0,<1.1.0
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
thinc==7.4.1
thinc==8.0.0a9
blis>=0.4.0,<0.5.0
wasabi>=0.4.0,<1.1.0
srsly>=1.0.2,<1.1.0
srsly>=2.0.0,<3.0.0
catalogue>=0.0.7,<1.1.0
ml_datasets>=0.1.1
# Third-party dependencies
tqdm>=4.38.0,<5.0.0
setuptools
numpy>=1.15.0
plac>=0.9.6,<1.2.0
requests>=2.13.0,<3.0.0
pathlib==1.0.1; python_version < "3.4"
pydantic>=1.3.0,<2.0.0
# Official Python utilities
setuptools
packaging
importlib_metadata>=0.20; python_version < "3.8"
[options.extras_require]
lookups =

180
setup.py
View File

@ -1,35 +1,27 @@
#!/usr/bin/env python
from __future__ import print_function
import io
import os
import subprocess
import sys
import contextlib
import platform
from distutils.command.build_ext import build_ext
from distutils.sysconfig import get_python_inc
import distutils.util
from distutils import ccompiler, msvccompiler
from setuptools import Extension, setup, find_packages
import numpy
from pathlib import Path
import shutil
from Cython.Build import cythonize
from Cython.Compiler import Options
def is_new_osx():
"""Check whether we're on OSX >= 10.10"""
name = distutils.util.get_platform()
if sys.platform != "darwin":
return False
elif name.startswith("macosx-10"):
minor_version = int(name.split("-")[1].split(".")[1])
if minor_version >= 7:
return True
else:
return False
else:
return False
ROOT = Path(__file__).parent
PACKAGE_ROOT = ROOT / "spacy"
# Preserve `__doc__` on functions and classes
# http://docs.cython.org/en/latest/src/userguide/source_files_and_compilation.html#compiler-options
Options.docstrings = True
PACKAGES = find_packages()
MOD_NAMES = [
"spacy.parts_of_speech",
"spacy.strings",
@ -62,16 +54,38 @@ MOD_NAMES = [
"spacy.symbols",
"spacy.vectors",
]
COMPILE_OPTIONS = {
"msvc": ["/Ox", "/EHsc"],
"mingw32": ["-O2", "-Wno-strict-prototypes", "-Wno-unused-function"],
"other": ["-O2", "-Wno-strict-prototypes", "-Wno-unused-function"],
}
LINK_OPTIONS = {"msvc": [], "mingw32": [], "other": []}
COMPILER_DIRECTIVES = {
"language_level": -3,
"embedsignature": True,
"annotation_typing": False,
}
# Files to copy into the package that are otherwise not included
COPY_FILES = {
ROOT / "setup.cfg": PACKAGE_ROOT / "tests" / "package",
ROOT / "pyproject.toml": PACKAGE_ROOT / "tests" / "package",
ROOT / "requirements.txt": PACKAGE_ROOT / "tests" / "package",
}
def is_new_osx():
"""Check whether we're on OSX >= 10.7"""
name = distutils.util.get_platform()
if sys.platform != "darwin":
return False
mac_ver = platform.mac_ver()[0]
if mac_ver.startswith("10"):
minor_version = int(mac_ver.split('.')[1])
if minor_version >= 7:
return True
else:
return False
return False
if is_new_osx():
@ -104,95 +118,53 @@ class build_ext_subclass(build_ext, build_ext_options):
build_ext.build_extensions(self)
def generate_cython(root, source):
print("Cythonizing sources")
p = subprocess.call(
[sys.executable, os.path.join(root, "bin", "cythonize.py"), source],
env=os.environ,
)
if p != 0:
raise RuntimeError("Running cythonize failed")
def is_source_release(path):
return os.path.exists(os.path.join(path, "PKG-INFO"))
def clean(path):
for name in MOD_NAMES:
name = name.replace(".", "/")
for ext in [".so", ".html", ".cpp", ".c"]:
file_path = os.path.join(path, name + ext)
if os.path.exists(file_path):
os.unlink(file_path)
@contextlib.contextmanager
def chdir(new_dir):
old_dir = os.getcwd()
try:
os.chdir(new_dir)
sys.path.insert(0, new_dir)
yield
finally:
del sys.path[0]
os.chdir(old_dir)
for path in path.glob("**/*"):
if path.is_file() and path.suffix in (".so", ".cpp"):
print(f"Deleting {path.name}")
path.unlink()
def setup_package():
root = os.path.abspath(os.path.dirname(__file__))
if len(sys.argv) > 1 and sys.argv[1] == "clean":
return clean(root)
return clean(PACKAGE_ROOT)
with chdir(root):
with io.open(os.path.join(root, "spacy", "about.py"), encoding="utf8") as f:
about = {}
exec(f.read(), about)
with (PACKAGE_ROOT / "about.py").open("r") as f:
about = {}
exec(f.read(), about)
include_dirs = [
get_python_inc(plat_specific=True),
os.path.join(root, "include"),
]
for copy_file, target_dir in COPY_FILES.items():
if copy_file.exists():
shutil.copy(str(copy_file), str(target_dir))
print(f"Copied {copy_file} -> {target_dir}")
if (
ccompiler.new_compiler().compiler_type == "msvc"
and msvccompiler.get_build_version() == 9
):
include_dirs.append(os.path.join(root, "include", "msvc9"))
include_dirs = [
get_python_inc(plat_specific=True),
numpy.get_include(),
str(ROOT / "include"),
]
if (
ccompiler.new_compiler().compiler_type == "msvc"
and msvccompiler.get_build_version() == 9
):
include_dirs.append(str(ROOT / "include" / "msvc9"))
ext_modules = []
for name in MOD_NAMES:
mod_path = name.replace(".", "/") + ".pyx"
ext = Extension(name, [mod_path], language="c++")
ext_modules.append(ext)
print("Cythonizing sources")
ext_modules = cythonize(ext_modules, compiler_directives=COMPILER_DIRECTIVES)
ext_modules = []
for mod_name in MOD_NAMES:
mod_path = mod_name.replace(".", "/") + ".cpp"
extra_link_args = []
# ???
# Imported from patch from @mikepb
# See Issue #267. Running blind here...
if sys.platform == "darwin":
dylib_path = [".." for _ in range(mod_name.count("."))]
dylib_path = "/".join(dylib_path)
dylib_path = "@loader_path/%s/spacy/platform/darwin/lib" % dylib_path
extra_link_args.append("-Wl,-rpath,%s" % dylib_path)
ext_modules.append(
Extension(
mod_name,
[mod_path],
language="c++",
include_dirs=include_dirs,
extra_link_args=extra_link_args,
)
)
if not is_source_release(root):
generate_cython(root, "spacy")
setup(
name="spacy",
packages=PACKAGES,
version=about["__version__"],
ext_modules=ext_modules,
cmdclass={"build_ext": build_ext_subclass},
)
setup(
name="spacy",
packages=PACKAGES,
version=about["__version__"],
ext_modules=ext_modules,
cmdclass={"build_ext": build_ext_subclass},
include_dirs=include_dirs,
package_data={"": ["*.pyx", "*.pxd", "*.pxi", "*.cpp"]},
)
if __name__ == "__main__":

7
spacy/__init__.py
View File

@ -1,5 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
import warnings
import sys
@ -7,7 +5,7 @@ warnings.filterwarnings("ignore", message="numpy.dtype size changed")
warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
# These are imported as part of the API
from thinc.neural.util import prefer_gpu, require_gpu
from thinc.api import prefer_gpu, require_gpu
from . import pipeline
from .cli.info import info as cli_info
@ -23,6 +21,9 @@ if sys.maxunicode == 65535:
raise SystemError(Errors.E130)
config = registry
def load(name, **overrides):
depr_path = overrides.get("path")
if depr_path not in (True, False, None):

17
spacy/__main__.py
View File

@ -1,21 +1,16 @@
# coding: utf8
from __future__ import print_function
# NB! This breaks in plac on Python 2!!
# from __future__ import unicode_literals
if __name__ == "__main__":
import plac
import sys
from wasabi import msg
from spacy.cli import download, link, info, package, train, pretrain, convert
from spacy.cli import download, link, info, package, pretrain, convert
from spacy.cli import init_model, profile, evaluate, validate, debug_data
from spacy.cli import train_cli
commands = {
"download": download,
"link": link,
"info": info,
"train": train,
"train": train_cli,
"pretrain": pretrain,
"debug-data": debug_data,
"evaluate": evaluate,
@ -28,9 +23,9 @@ if __name__ == "__main__":
if len(sys.argv) == 1:
msg.info("Available commands", ", ".join(commands), exits=1)
command = sys.argv.pop(1)
sys.argv[0] = "spacy %s" % command
sys.argv[0] = f"spacy {command}"
if command in commands:
plac.call(commands[command], sys.argv[1:])
else:
available = "Available: {}".format(", ".join(commands))
msg.fail("Unknown command: {}".format(command), available, exits=1)
available = f"Available: {', '.join(commands)}"
msg.fail(f"Unknown command: {command}", available, exits=1)

988
spacy/_ml.py
View File

@ -1,988 +0,0 @@
# coding: utf8
from __future__ import unicode_literals
import numpy
import warnings
from thinc.v2v import Model, Maxout, Softmax, Affine, ReLu
from thinc.t2t import ExtractWindow, ParametricAttention
from thinc.t2v import Pooling, sum_pool, mean_pool
from thinc.i2v import HashEmbed
from thinc.misc import Residual, FeatureExtracter
from thinc.misc import LayerNorm as LN
from thinc.api import add, layerize, chain, clone, concatenate, with_flatten
from thinc.api import with_getitem, flatten_add_lengths
from thinc.api import uniqued, wrap, noop
from thinc.linear.linear import LinearModel
from thinc.neural.ops import NumpyOps, CupyOps
from thinc.neural.util import get_array_module, copy_array
from thinc.neural.optimizers import Adam
from thinc import describe
from thinc.describe import Dimension, Synapses, Biases, Gradient
from thinc.neural._classes.affine import _set_dimensions_if_needed
import thinc.extra.load_nlp
from .attrs import ID, ORTH, LOWER, NORM, PREFIX, SUFFIX, SHAPE
from .errors import Errors, Warnings
from . import util
from . import ml as new_ml
from .ml import _legacy_tok2vec
VECTORS_KEY = "spacy_pretrained_vectors"
# Backwards compatibility with <2.2.2
USE_MODEL_REGISTRY_TOK2VEC = False
def cosine(vec1, vec2):
xp = get_array_module(vec1)
norm1 = xp.linalg.norm(vec1)
norm2 = xp.linalg.norm(vec2)
if norm1 == 0.0 or norm2 == 0.0:
return 0
else:
return vec1.dot(vec2) / (norm1 * norm2)
def create_default_optimizer(ops, **cfg):
learn_rate = util.env_opt("learn_rate", 0.001)
beta1 = util.env_opt("optimizer_B1", 0.9)
beta2 = util.env_opt("optimizer_B2", 0.999)
eps = util.env_opt("optimizer_eps", 1e-8)
L2 = util.env_opt("L2_penalty", 1e-6)
max_grad_norm = util.env_opt("grad_norm_clip", 1.0)
optimizer = Adam(ops, learn_rate, L2=L2, beta1=beta1, beta2=beta2, eps=eps)
optimizer.max_grad_norm = max_grad_norm
optimizer.device = ops.device
return optimizer
@layerize
def _flatten_add_lengths(seqs, pad=0, drop=0.0):
ops = Model.ops
lengths = ops.asarray([len(seq) for seq in seqs], dtype="i")
def finish_update(d_X, sgd=None):
return ops.unflatten(d_X, lengths, pad=pad)
X = ops.flatten(seqs, pad=pad)
return (X, lengths), finish_update
def _zero_init(model):
def _zero_init_impl(self, *args, **kwargs):
self.W.fill(0)
model.on_init_hooks.append(_zero_init_impl)
if model.W is not None:
model.W.fill(0.0)
return model
def with_cpu(ops, model):
"""Wrap a model that should run on CPU, transferring inputs and outputs
as necessary."""
model.to_cpu()
def with_cpu_forward(inputs, drop=0.0):
cpu_outputs, backprop = model.begin_update(_to_cpu(inputs), drop=drop)
gpu_outputs = _to_device(ops, cpu_outputs)
def with_cpu_backprop(d_outputs, sgd=None):
cpu_d_outputs = _to_cpu(d_outputs)
return backprop(cpu_d_outputs, sgd=sgd)
return gpu_outputs, with_cpu_backprop
return wrap(with_cpu_forward, model)
def _to_cpu(X):
if isinstance(X, numpy.ndarray):
return X
elif isinstance(X, tuple):
return tuple([_to_cpu(x) for x in X])
elif isinstance(X, list):
return [_to_cpu(x) for x in X]
elif hasattr(X, "get"):
return X.get()
else:
return X
def _to_device(ops, X):
if isinstance(X, tuple):
return tuple([_to_device(ops, x) for x in X])
elif isinstance(X, list):
return [_to_device(ops, x) for x in X]
else:
return ops.asarray(X)
class extract_ngrams(Model):
def __init__(self, ngram_size, attr=LOWER):
Model.__init__(self)
self.ngram_size = ngram_size
self.attr = attr
def begin_update(self, docs, drop=0.0):
batch_keys = []
batch_vals = []
for doc in docs:
unigrams = doc.to_array([self.attr])
ngrams = [unigrams]
for n in range(2, self.ngram_size + 1):
ngrams.append(self.ops.ngrams(n, unigrams))
keys = self.ops.xp.concatenate(ngrams)
keys, vals = self.ops.xp.unique(keys, return_counts=True)
batch_keys.append(keys)
batch_vals.append(vals)
# The dtype here matches what thinc is expecting -- which differs per
# platform (by int definition). This should be fixed once the problem
# is fixed on Thinc's side.
lengths = self.ops.asarray(
[arr.shape[0] for arr in batch_keys], dtype=numpy.int_
)
batch_keys = self.ops.xp.concatenate(batch_keys)
batch_vals = self.ops.asarray(self.ops.xp.concatenate(batch_vals), dtype="f")
return (batch_keys, batch_vals, lengths), None
@describe.on_data(
_set_dimensions_if_needed, lambda model, X, y: model.init_weights(model)
)
@describe.attributes(
nI=Dimension("Input size"),
nF=Dimension("Number of features"),
nO=Dimension("Output size"),
nP=Dimension("Maxout pieces"),
W=Synapses("Weights matrix", lambda obj: (obj.nF, obj.nO, obj.nP, obj.nI)),
b=Biases("Bias vector", lambda obj: (obj.nO, obj.nP)),
pad=Synapses(
"Pad",
lambda obj: (1, obj.nF, obj.nO, obj.nP),
lambda M, ops: ops.normal_init(M, 1.0),
),
d_W=Gradient("W"),
d_pad=Gradient("pad"),
d_b=Gradient("b"),
)
class PrecomputableAffine(Model):
def __init__(self, nO=None, nI=None, nF=None, nP=None, **kwargs):
Model.__init__(self, **kwargs)
self.nO = nO
self.nP = nP
self.nI = nI
self.nF = nF
def begin_update(self, X, drop=0.0):
Yf = self.ops.gemm(
X, self.W.reshape((self.nF * self.nO * self.nP, self.nI)), trans2=True
)
Yf = Yf.reshape((Yf.shape[0], self.nF, self.nO, self.nP))
Yf = self._add_padding(Yf)
def backward(dY_ids, sgd=None):
dY, ids = dY_ids
dY, ids = self._backprop_padding(dY, ids)
Xf = X[ids]
Xf = Xf.reshape((Xf.shape[0], self.nF * self.nI))
self.d_b += dY.sum(axis=0)
dY = dY.reshape((dY.shape[0], self.nO * self.nP))
Wopfi = self.W.transpose((1, 2, 0, 3))
Wopfi = self.ops.xp.ascontiguousarray(Wopfi)
Wopfi = Wopfi.reshape((self.nO * self.nP, self.nF * self.nI))
dXf = self.ops.gemm(dY.reshape((dY.shape[0], self.nO * self.nP)), Wopfi)
# Reuse the buffer
dWopfi = Wopfi
dWopfi.fill(0.0)
self.ops.gemm(dY, Xf, out=dWopfi, trans1=True)
dWopfi = dWopfi.reshape((self.nO, self.nP, self.nF, self.nI))
# (o, p, f, i) --> (f, o, p, i)
self.d_W += dWopfi.transpose((2, 0, 1, 3))
if sgd is not None:
sgd(self._mem.weights, self._mem.gradient, key=self.id)
return dXf.reshape((dXf.shape[0], self.nF, self.nI))
return Yf, backward
def _add_padding(self, Yf):
Yf_padded = self.ops.xp.vstack((self.pad, Yf))
return Yf_padded
def _backprop_padding(self, dY, ids):
# (1, nF, nO, nP) += (nN, nF, nO, nP) where IDs (nN, nF) < 0
mask = ids < 0.0
mask = mask.sum(axis=1)
d_pad = dY * mask.reshape((ids.shape[0], 1, 1))
self.d_pad += d_pad.sum(axis=0)
return dY, ids
@staticmethod
def init_weights(model):
"""This is like the 'layer sequential unit variance', but instead
of taking the actual inputs, we randomly generate whitened data.
Why's this all so complicated? We have a huge number of inputs,
and the maxout unit makes guessing the dynamics tricky. Instead
we set the maxout weights to values that empirically result in
whitened outputs given whitened inputs.
"""
if (model.W ** 2).sum() != 0.0:
return
ops = model.ops
xp = ops.xp
ops.normal_init(model.W, model.nF * model.nI, inplace=True)
ids = ops.allocate((5000, model.nF), dtype="f")
ids += xp.random.uniform(0, 1000, ids.shape)
ids = ops.asarray(ids, dtype="i")
tokvecs = ops.allocate((5000, model.nI), dtype="f")
tokvecs += xp.random.normal(loc=0.0, scale=1.0, size=tokvecs.size).reshape(
tokvecs.shape
)
def predict(ids, tokvecs):
# nS ids. nW tokvecs. Exclude the padding array.
hiddens = model(tokvecs[:-1]) # (nW, f, o, p)
vectors = model.ops.allocate((ids.shape[0], model.nO * model.nP), dtype="f")
# need nS vectors
hiddens = hiddens.reshape(
(hiddens.shape[0] * model.nF, model.nO * model.nP)
)
model.ops.scatter_add(vectors, ids.flatten(), hiddens)
vectors = vectors.reshape((vectors.shape[0], model.nO, model.nP))
vectors += model.b
vectors = model.ops.asarray(vectors)
if model.nP >= 2:
return model.ops.maxout(vectors)[0]
else:
return vectors * (vectors >= 0)
tol_var = 0.01
tol_mean = 0.01
t_max = 10
t_i = 0
for t_i in range(t_max):
acts1 = predict(ids, tokvecs)
var = model.ops.xp.var(acts1)
mean = model.ops.xp.mean(acts1)
if abs(var - 1.0) >= tol_var:
model.W /= model.ops.xp.sqrt(var)
elif abs(mean) >= tol_mean:
model.b -= mean
else:
break
def link_vectors_to_models(vocab, skip_rank=False):
vectors = vocab.vectors
if vectors.name is None:
vectors.name = VECTORS_KEY
if vectors.data.size != 0:
warnings.warn(Warnings.W020.format(shape=vectors.data.shape))
ops = Model.ops
if not skip_rank:
for word in vocab:
if word.orth in vectors.key2row:
word.rank = vectors.key2row[word.orth]
else:
word.rank = util.OOV_RANK
data = ops.asarray(vectors.data)
# Set an entry here, so that vectors are accessed by StaticVectors
# (unideal, I know)
key = (ops.device, vectors.name)
if key in thinc.extra.load_nlp.VECTORS:
if thinc.extra.load_nlp.VECTORS[key].shape != data.shape:
# This is a hack to avoid the problem in #3853.
old_name = vectors.name
new_name = vectors.name + "_%d" % data.shape[0]
warnings.warn(Warnings.W019.format(old=old_name, new=new_name))
vectors.name = new_name
key = (ops.device, vectors.name)
thinc.extra.load_nlp.VECTORS[key] = data
def PyTorchBiLSTM(nO, nI, depth, dropout=0.2):
import torch.nn
from thinc.api import with_square_sequences
from thinc.extra.wrappers import PyTorchWrapperRNN
if depth == 0:
return layerize(noop())
model = torch.nn.LSTM(nI, nO // 2, depth, bidirectional=True, dropout=dropout)
return with_square_sequences(PyTorchWrapperRNN(model))
def Tok2Vec(width, embed_size, **kwargs):
if not USE_MODEL_REGISTRY_TOK2VEC:
# Preserve prior tok2vec for backwards compat, in v2.2.2
return _legacy_tok2vec.Tok2Vec(width, embed_size, **kwargs)
pretrained_vectors = kwargs.get("pretrained_vectors", None)
cnn_maxout_pieces = kwargs.get("cnn_maxout_pieces", 3)
subword_features = kwargs.get("subword_features", True)
char_embed = kwargs.get("char_embed", False)
conv_depth = kwargs.get("conv_depth", 4)
bilstm_depth = kwargs.get("bilstm_depth", 0)
conv_window = kwargs.get("conv_window", 1)
cols = ["ID", "NORM", "PREFIX", "SUFFIX", "SHAPE", "ORTH"]
doc2feats_cfg = {"arch": "spacy.Doc2Feats.v1", "config": {"columns": cols}}
if char_embed:
embed_cfg = {
"arch": "spacy.CharacterEmbed.v1",
"config": {
"width": 64,
"chars": 6,
"@mix": {
"arch": "spacy.LayerNormalizedMaxout.v1",
"config": {"width": width, "pieces": 3},
},
"@embed_features": None,
},
}
else:
embed_cfg = {
"arch": "spacy.MultiHashEmbed.v1",
"config": {
"width": width,
"rows": embed_size,
"columns": cols,
"use_subwords": subword_features,
"@pretrained_vectors": None,
"@mix": {
"arch": "spacy.LayerNormalizedMaxout.v1",
"config": {"width": width, "pieces": 3},
},
},
}
if pretrained_vectors:
embed_cfg["config"]["@pretrained_vectors"] = {
"arch": "spacy.PretrainedVectors.v1",
"config": {
"vectors_name": pretrained_vectors,
"width": width,
"column": cols.index("ID"),
},
}
if cnn_maxout_pieces >= 2:
cnn_cfg = {
"arch": "spacy.MaxoutWindowEncoder.v1",
"config": {
"width": width,
"window_size": conv_window,
"pieces": cnn_maxout_pieces,
"depth": conv_depth,
},
}
else:
cnn_cfg = {
"arch": "spacy.MishWindowEncoder.v1",
"config": {"width": width, "window_size": conv_window, "depth": conv_depth},
}
bilstm_cfg = {
"arch": "spacy.TorchBiLSTMEncoder.v1",
"config": {"width": width, "depth": bilstm_depth},
}
if conv_depth == 0 and bilstm_depth == 0:
encode_cfg = {}
elif conv_depth >= 1 and bilstm_depth >= 1:
encode_cfg = {
"arch": "thinc.FeedForward.v1",
"config": {"children": [cnn_cfg, bilstm_cfg]},
}
elif conv_depth >= 1:
encode_cfg = cnn_cfg
else:
encode_cfg = bilstm_cfg
config = {"@doc2feats": doc2feats_cfg, "@embed": embed_cfg, "@encode": encode_cfg}
return new_ml.Tok2Vec(config)
def reapply(layer, n_times):
def reapply_fwd(X, drop=0.0):
backprops = []
for i in range(n_times):
Y, backprop = layer.begin_update(X, drop=drop)
X = Y
backprops.append(backprop)
def reapply_bwd(dY, sgd=None):
dX = None
for backprop in reversed(backprops):
dY = backprop(dY, sgd=sgd)
if dX is None:
dX = dY
else:
dX += dY
return dX
return Y, reapply_bwd
return wrap(reapply_fwd, layer)
def asarray(ops, dtype):
def forward(X, drop=0.0):
return ops.asarray(X, dtype=dtype), None
return layerize(forward)
def _divide_array(X, size):
parts = []
index = 0
while index < len(X):
parts.append(X[index : index + size])
index += size
return parts
def get_col(idx):
if idx < 0:
raise IndexError(Errors.E066.format(value=idx))
def forward(X, drop=0.0):
if isinstance(X, numpy.ndarray):
ops = NumpyOps()
else:
ops = CupyOps()
output = ops.xp.ascontiguousarray(X[:, idx], dtype=X.dtype)
def backward(y, sgd=None):
dX = ops.allocate(X.shape)
dX[:, idx] += y
return dX
return output, backward
return layerize(forward)
def doc2feats(cols=None):
if cols is None:
cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
def forward(docs, drop=0.0):
feats = []
for doc in docs:
feats.append(doc.to_array(cols))
return feats, None
model = layerize(forward)
model.cols = cols
return model
def print_shape(prefix):
def forward(X, drop=0.0):
return X, lambda dX, **kwargs: dX
return layerize(forward)
@layerize
def get_token_vectors(tokens_attrs_vectors, drop=0.0):
tokens, attrs, vectors = tokens_attrs_vectors
def backward(d_output, sgd=None):
return (tokens, d_output)
return vectors, backward
@layerize
def logistic(X, drop=0.0):
xp = get_array_module(X)
if not isinstance(X, xp.ndarray):
X = xp.asarray(X)
# Clip to range (-10, 10)
X = xp.minimum(X, 10.0, X)
X = xp.maximum(X, -10.0, X)
Y = 1.0 / (1.0 + xp.exp(-X))
def logistic_bwd(dY, sgd=None):
dX = dY * (Y * (1 - Y))
return dX
return Y, logistic_bwd
def zero_init(model):
def _zero_init_impl(self, X, y):
self.W.fill(0)
model.on_data_hooks.append(_zero_init_impl)
return model
def getitem(i):
def getitem_fwd(X, drop=0.0):
return X[i], None
return layerize(getitem_fwd)
@describe.attributes(
W=Synapses("Weights matrix", lambda obj: (obj.nO, obj.nI), lambda W, ops: None)
)
class MultiSoftmax(Affine):
"""Neural network layer that predicts several multi-class attributes at once.
For instance, we might predict one class with 6 variables, and another with 5.
We predict the 11 neurons required for this, and then softmax them such
that columns 0-6 make a probability distribution and coumns 6-11 make another.
"""
name = "multisoftmax"
def __init__(self, out_sizes, nI=None, **kwargs):
Model.__init__(self, **kwargs)
self.out_sizes = out_sizes
self.nO = sum(out_sizes)
self.nI = nI
def predict(self, input__BI):
output__BO = self.ops.affine(self.W, self.b, input__BI)
i = 0
for out_size in self.out_sizes:
self.ops.softmax(output__BO[:, i : i + out_size], inplace=True)
i += out_size
return output__BO
def begin_update(self, input__BI, drop=0.0):
output__BO = self.predict(input__BI)
def finish_update(grad__BO, sgd=None):
self.d_W += self.ops.gemm(grad__BO, input__BI, trans1=True)
self.d_b += grad__BO.sum(axis=0)
grad__BI = self.ops.gemm(grad__BO, self.W)
if sgd is not None:
sgd(self._mem.weights, self._mem.gradient, key=self.id)
return grad__BI
return output__BO, finish_update
def build_tagger_model(nr_class, **cfg):
embed_size = util.env_opt("embed_size", 2000)
if "token_vector_width" in cfg:
token_vector_width = cfg["token_vector_width"]
else:
token_vector_width = util.env_opt("token_vector_width", 96)
pretrained_vectors = cfg.get("pretrained_vectors")
subword_features = cfg.get("subword_features", True)
with Model.define_operators({">>": chain, "+": add}):
if "tok2vec" in cfg:
tok2vec = cfg["tok2vec"]
else:
tok2vec = Tok2Vec(
token_vector_width,
embed_size,
subword_features=subword_features,
pretrained_vectors=pretrained_vectors,
)
softmax = with_flatten(Softmax(nr_class, token_vector_width))
model = tok2vec >> softmax
model.nI = None
model.tok2vec = tok2vec
model.softmax = softmax
return model
def build_morphologizer_model(class_nums, **cfg):
embed_size = util.env_opt("embed_size", 7000)
if "token_vector_width" in cfg:
token_vector_width = cfg["token_vector_width"]
else:
token_vector_width = util.env_opt("token_vector_width", 128)
pretrained_vectors = cfg.get("pretrained_vectors")
char_embed = cfg.get("char_embed", True)
with Model.define_operators({">>": chain, "+": add, "**": clone}):
if "tok2vec" in cfg:
tok2vec = cfg["tok2vec"]
else:
tok2vec = Tok2Vec(
token_vector_width,
embed_size,
char_embed=char_embed,
pretrained_vectors=pretrained_vectors,
)
softmax = with_flatten(MultiSoftmax(class_nums, token_vector_width))
softmax.out_sizes = class_nums
model = tok2vec >> softmax
model.nI = None
model.tok2vec = tok2vec
model.softmax = softmax
return model
@layerize
def SpacyVectors(docs, drop=0.0):
batch = []
for doc in docs:
indices = numpy.zeros((len(doc),), dtype="i")
for i, word in enumerate(doc):
if word.orth in doc.vocab.vectors.key2row:
indices[i] = doc.vocab.vectors.key2row[word.orth]
else:
indices[i] = 0
vectors = doc.vocab.vectors.data[indices]
batch.append(vectors)
return batch, None
def build_text_classifier(nr_class, width=64, **cfg):
depth = cfg.get("depth", 2)
nr_vector = cfg.get("nr_vector", 5000)
pretrained_dims = cfg.get("pretrained_dims", 0)
with Model.define_operators({">>": chain, "+": add, "|": concatenate, "**": clone}):
if cfg.get("low_data") and pretrained_dims:
model = (
SpacyVectors
>> flatten_add_lengths
>> with_getitem(0, Affine(width, pretrained_dims))
>> ParametricAttention(width)
>> Pooling(sum_pool)
>> Residual(ReLu(width, width)) ** 2
>> zero_init(Affine(nr_class, width, drop_factor=0.0))
>> logistic
)
return model
lower = HashEmbed(width, nr_vector, column=1)
prefix = HashEmbed(width // 2, nr_vector, column=2)
suffix = HashEmbed(width // 2, nr_vector, column=3)
shape = HashEmbed(width // 2, nr_vector, column=4)
trained_vectors = FeatureExtracter(
[ORTH, LOWER, PREFIX, SUFFIX, SHAPE, ID]
) >> with_flatten(
uniqued(
(lower | prefix | suffix | shape)
>> LN(Maxout(width, width + (width // 2) * 3)),
column=0,
)
)
if pretrained_dims:
static_vectors = SpacyVectors >> with_flatten(
Affine(width, pretrained_dims)
)
# TODO Make concatenate support lists
vectors = concatenate_lists(trained_vectors, static_vectors)
vectors_width = width * 2
else:
vectors = trained_vectors
vectors_width = width
static_vectors = None
tok2vec = vectors >> with_flatten(
LN(Maxout(width, vectors_width))
>> Residual((ExtractWindow(nW=1) >> LN(Maxout(width, width * 3)))) ** depth,
pad=depth,
)
cnn_model = (
tok2vec
>> flatten_add_lengths
>> ParametricAttention(width)
>> Pooling(sum_pool)
>> Residual(zero_init(Maxout(width, width)))
>> zero_init(Affine(nr_class, width, drop_factor=0.0))
)
linear_model = build_bow_text_classifier(
nr_class,
ngram_size=cfg.get("ngram_size", 1),
exclusive_classes=cfg.get("exclusive_classes", False),
)
if cfg.get("exclusive_classes", False):
output_layer = Softmax(nr_class, nr_class * 2)
else:
output_layer = (
zero_init(Affine(nr_class, nr_class * 2, drop_factor=0.0)) >> logistic
)
model = (linear_model | cnn_model) >> output_layer
model.tok2vec = chain(tok2vec, flatten)
model.nO = nr_class
model.lsuv = False
return model
def build_bow_text_classifier(
nr_class, ngram_size=1, exclusive_classes=False, no_output_layer=False, **cfg
):
with Model.define_operators({">>": chain}):
model = with_cpu(
Model.ops, extract_ngrams(ngram_size, attr=ORTH) >> LinearModel(nr_class)
)
if not no_output_layer:
model = model >> (cpu_softmax if exclusive_classes else logistic)
model.nO = nr_class
return model
@layerize
def cpu_softmax(X, drop=0.0):
ops = NumpyOps()
def cpu_softmax_backward(dY, sgd=None):
return dY
return ops.softmax(X), cpu_softmax_backward
def build_simple_cnn_text_classifier(tok2vec, nr_class, exclusive_classes=False, **cfg):
"""
Build a simple CNN text classifier, given a token-to-vector model as inputs.
If exclusive_classes=True, a softmax non-linearity is applied, so that the
outputs sum to 1. If exclusive_classes=False, a logistic non-linearity
is applied instead, so that outputs are in the range [0, 1].
"""
with Model.define_operators({">>": chain}):
if exclusive_classes:
output_layer = Softmax(nr_class, tok2vec.nO)
else:
output_layer = (
zero_init(Affine(nr_class, tok2vec.nO, drop_factor=0.0)) >> logistic
)
model = tok2vec >> flatten_add_lengths >> Pooling(mean_pool) >> output_layer
model.tok2vec = chain(tok2vec, flatten)
model.nO = nr_class
return model
def build_nel_encoder(embed_width, hidden_width, ner_types, **cfg):
if "entity_width" not in cfg:
raise ValueError(Errors.E144.format(param="entity_width"))
conv_depth = cfg.get("conv_depth", 2)
cnn_maxout_pieces = cfg.get("cnn_maxout_pieces", 3)
pretrained_vectors = cfg.get("pretrained_vectors", None)
context_width = cfg.get("entity_width")
with Model.define_operators({">>": chain, "**": clone}):
# context encoder
tok2vec = Tok2Vec(
width=hidden_width,
embed_size=embed_width,
pretrained_vectors=pretrained_vectors,
cnn_maxout_pieces=cnn_maxout_pieces,
subword_features=True,
conv_depth=conv_depth,
bilstm_depth=0,
)
model = (
tok2vec
>> flatten_add_lengths
>> Pooling(mean_pool)
>> Residual(zero_init(Maxout(hidden_width, hidden_width)))
>> zero_init(Affine(context_width, hidden_width, drop_factor=0.0))
)
model.tok2vec = tok2vec
model.nO = context_width
return model
@layerize
def flatten(seqs, drop=0.0):
ops = Model.ops
lengths = ops.asarray([len(seq) for seq in seqs], dtype="i")
def finish_update(d_X, sgd=None):
return ops.unflatten(d_X, lengths, pad=0)
X = ops.flatten(seqs, pad=0)
return X, finish_update
def concatenate_lists(*layers, **kwargs): # pragma: no cover
"""Compose two or more models `f`, `g`, etc, such that their outputs are
concatenated, i.e. `concatenate(f, g)(x)` computes `hstack(f(x), g(x))`
"""
if not layers:
return noop()
drop_factor = kwargs.get("drop_factor", 1.0)
ops = layers[0].ops
layers = [chain(layer, flatten) for layer in layers]
concat = concatenate(*layers)
def concatenate_lists_fwd(Xs, drop=0.0):
if drop is not None:
drop *= drop_factor
lengths = ops.asarray([len(X) for X in Xs], dtype="i")
flat_y, bp_flat_y = concat.begin_update(Xs, drop=drop)
ys = ops.unflatten(flat_y, lengths)
def concatenate_lists_bwd(d_ys, sgd=None):
return bp_flat_y(ops.flatten(d_ys), sgd=sgd)
return ys, concatenate_lists_bwd
model = wrap(concatenate_lists_fwd, concat)
return model
def masked_language_model(vocab, model, mask_prob=0.15):
"""Convert a model into a BERT-style masked language model"""
random_words = _RandomWords(vocab)
def mlm_forward(docs, drop=0.0):
mask, docs = _apply_mask(docs, random_words, mask_prob=mask_prob)
mask = model.ops.asarray(mask).reshape((mask.shape[0], 1))
output, backprop = model.begin_update(docs, drop=drop)
def mlm_backward(d_output, sgd=None):
d_output *= 1 - mask
return backprop(d_output, sgd=sgd)
return output, mlm_backward
return wrap(mlm_forward, model)
class _RandomWords(object):
def __init__(self, vocab):
self.words = [lex.text for lex in vocab if lex.prob != 0.0]
self.probs = [lex.prob for lex in vocab if lex.prob != 0.0]
self.words = self.words[:10000]
self.probs = self.probs[:10000]
self.probs = numpy.exp(numpy.array(self.probs, dtype="f"))
self.probs /= self.probs.sum()
self._cache = []
def next(self):
if not self._cache:
self._cache.extend(
numpy.random.choice(len(self.words), 10000, p=self.probs)
)
index = self._cache.pop()
return self.words[index]
def _apply_mask(docs, random_words, mask_prob=0.15):
# This needs to be here to avoid circular imports
from .tokens.doc import Doc
N = sum(len(doc) for doc in docs)
mask = numpy.random.uniform(0.0, 1.0, (N,))
mask = mask >= mask_prob
i = 0
masked_docs = []
for doc in docs:
words = []
for token in doc:
if not mask[i]:
word = _replace_word(token.text, random_words)
else:
word = token.text
words.append(word)
i += 1
spaces = [bool(w.whitespace_) for w in doc]
# NB: If you change this implementation to instead modify
# the docs in place, take care that the IDs reflect the original
# words. Currently we use the original docs to make the vectors
# for the target, so we don't lose the original tokens. But if
# you modified the docs in place here, you would.
masked_docs.append(Doc(doc.vocab, words=words, spaces=spaces))
return mask, masked_docs
def _replace_word(word, random_words, mask="[MASK]"):
roll = numpy.random.random()
if roll < 0.8:
return mask
elif roll < 0.9:
return random_words.next()
else:
return word
def _uniform_init(lo, hi):
def wrapped(W, ops):
copy_array(W, ops.xp.random.uniform(lo, hi, W.shape))
return wrapped
@describe.attributes(
nM=Dimension("Vector dimensions"),
nC=Dimension("Number of characters per word"),
vectors=Synapses(
"Embed matrix", lambda obj: (obj.nC, obj.nV, obj.nM), _uniform_init(-0.1, 0.1)
),
d_vectors=Gradient("vectors"),
)
class CharacterEmbed(Model):
def __init__(self, nM=None, nC=None, **kwargs):
Model.__init__(self, **kwargs)
self.nM = nM
self.nC = nC
@property
def nO(self):
return self.nM * self.nC
@property
def nV(self):
return 256
def begin_update(self, docs, drop=0.0):
if not docs:
return []
ids = []
output = []
weights = self.vectors
# This assists in indexing; it's like looping over this dimension.
# Still consider this weird witch craft...But thanks to Mark Neumann
# for the tip.
nCv = self.ops.xp.arange(self.nC)
for doc in docs:
doc_ids = doc.to_utf8_array(nr_char=self.nC)
doc_vectors = self.ops.allocate((len(doc), self.nC, self.nM))
# Let's say I have a 2d array of indices, and a 3d table of data. What numpy
# incantation do I chant to get
# output[i, j, k] == data[j, ids[i, j], k]?
doc_vectors[:, nCv] = weights[nCv, doc_ids[:, nCv]]
output.append(doc_vectors.reshape((len(doc), self.nO)))
ids.append(doc_ids)
def backprop_character_embed(d_vectors, sgd=None):
gradient = self.d_vectors
for doc_ids, d_doc_vectors in zip(ids, d_vectors):
d_doc_vectors = d_doc_vectors.reshape((len(doc_ids), self.nC, self.nM))
gradient[nCv, doc_ids[:, nCv]] += d_doc_vectors[:, nCv]
if sgd is not None:
sgd(self._mem.weights, self._mem.gradient, key=self.id)
return None
return output, backprop_character_embed
def get_cossim_loss(yh, y, ignore_zeros=False):
xp = get_array_module(yh)
# Find the zero vectors
if ignore_zeros:
zero_indices = xp.abs(y).sum(axis=1) == 0
# Add a small constant to avoid 0 vectors
yh = yh + 1e-8
y = y + 1e-8
# https://math.stackexchange.com/questions/1923613/partial-derivative-of-cosine-similarity
norm_yh = xp.linalg.norm(yh, axis=1, keepdims=True)
norm_y = xp.linalg.norm(y, axis=1, keepdims=True)
mul_norms = norm_yh * norm_y
cosine = (yh * y).sum(axis=1, keepdims=True) / mul_norms
d_yh = (y / mul_norms) - (cosine * (yh / norm_yh ** 2))
losses = xp.abs(cosine - 1)
if ignore_zeros:
# If the target was a zero vector, don't count it in the loss.
d_yh[zero_indices] = 0
losses[zero_indices] = 0
loss = losses.sum()
return loss, -d_yh

2
spacy/about.py
View File

@ -1,6 +1,6 @@
# fmt: off
__title__ = "spacy"
__version__ = "2.3.0"
__version__ = "3.0.0.dev9"
__release__ = True
__download_url__ = "https://github.com/explosion/spacy-models/releases/download"
__compatibility__ = "https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json"

1
spacy/attrs.pxd
View File

@ -91,6 +91,7 @@ cdef enum attr_id_t:
LANG
ENT_KB_ID = symbols.ENT_KB_ID
MORPH
ENT_ID = symbols.ENT_ID
IDX

4
spacy/attrs.pyx
View File

@ -1,6 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
IDS = {
"": NULL_ATTR,
@ -92,6 +89,7 @@ IDS = {
"SPACY": SPACY,
"PROB": PROB,
"LANG": LANG,
"MORPH": MORPH,
"IDX": IDX
}

12
spacy/cli/__init__.py
View File

@ -1,12 +1,20 @@
from wasabi import msg
from .download import download # noqa: F401
from .info import info # noqa: F401
from .link import link # noqa: F401
from .package import package # noqa: F401
from .profile import profile # noqa: F401
from .train import train # noqa: F401
from .train_from_config import train_cli # noqa: F401
from .pretrain import pretrain # noqa: F401
from .debug_data import debug_data # noqa: F401
from .evaluate import evaluate # noqa: F401
from .convert import convert # noqa: F401
from .init_model import init_model # noqa: F401
from .validate import validate # noqa: F401
def link(*args, **kwargs):
msg.warn(
"As of spaCy v3.0, model symlinks are deprecated. You can load models "
"using their full names or from a directory path."
)

220
spacy/cli/_schemas.py
View File

@ -1,220 +0,0 @@
# coding: utf-8
from __future__ import unicode_literals
# NB: This schema describes the new format of the training data, see #2928
TRAINING_SCHEMA = {
"$schema": "http://json-schema.org/draft-06/schema",
"title": "Training data for spaCy models",
"type": "array",
"items": {
"type": "object",
"properties": {
"text": {
"title": "The text of the training example",
"type": "string",
"minLength": 1,
},
"ents": {
"title": "Named entity spans in the text",
"type": "array",
"items": {
"type": "object",
"properties": {
"start": {
"title": "Start character offset of the span",
"type": "integer",
"minimum": 0,
},
"end": {
"title": "End character offset of the span",
"type": "integer",
"minimum": 0,
},
"label": {
"title": "Entity label",
"type": "string",
"minLength": 1,
"pattern": "^[A-Z0-9]*$",
},
},
"required": ["start", "end", "label"],
},
},
"sents": {
"title": "Sentence spans in the text",
"type": "array",
"items": {
"type": "object",
"properties": {
"start": {
"title": "Start character offset of the span",
"type": "integer",
"minimum": 0,
},
"end": {
"title": "End character offset of the span",
"type": "integer",
"minimum": 0,
},
},
"required": ["start", "end"],
},
},
"cats": {
"title": "Text categories for the text classifier",
"type": "object",
"patternProperties": {
"*": {
"title": "A text category",
"oneOf": [
{"type": "boolean"},
{"type": "number", "minimum": 0},
],
}
},
"propertyNames": {"pattern": "^[A-Z0-9]*$", "minLength": 1},
},
"tokens": {
"title": "The tokens in the text",
"type": "array",
"items": {
"type": "object",
"minProperties": 1,
"properties": {
"id": {
"title": "Token ID, usually token index",
"type": "integer",
"minimum": 0,
},
"start": {
"title": "Start character offset of the token",
"type": "integer",
"minimum": 0,
},
"end": {
"title": "End character offset of the token",
"type": "integer",
"minimum": 0,
},
"pos": {
"title": "Coarse-grained part-of-speech tag",
"type": "string",
"minLength": 1,
},
"tag": {
"title": "Fine-grained part-of-speech tag",
"type": "string",
"minLength": 1,
},
"dep": {
"title": "Dependency label",
"type": "string",
"minLength": 1,
},
"head": {
"title": "Index of the token's head",
"type": "integer",
"minimum": 0,
},
},
"required": ["start", "end"],
},
},
"_": {"title": "Custom user space", "type": "object"},
},
"required": ["text"],
},
}
META_SCHEMA = {
"$schema": "http://json-schema.org/draft-06/schema",
"type": "object",
"properties": {
"lang": {
"title": "Two-letter language code, e.g. 'en'",
"type": "string",
"minLength": 2,
"maxLength": 2,
"pattern": "^[a-z]*$",
},
"name": {
"title": "Model name",
"type": "string",
"minLength": 1,
"pattern": "^[a-z_]*$",
},
"version": {
"title": "Model version",
"type": "string",
"minLength": 1,
"pattern": "^[0-9a-z.-]*$",
},
"spacy_version": {
"title": "Compatible spaCy version identifier",
"type": "string",
"minLength": 1,
"pattern": "^[0-9a-z.-><=]*$",
},
"parent_package": {
"title": "Name of parent spaCy package, e.g. spacy or spacy-nightly",
"type": "string",
"minLength": 1,
"default": "spacy",
},
"pipeline": {
"title": "Names of pipeline components",
"type": "array",
"items": {"type": "string", "minLength": 1},
},
"description": {"title": "Model description", "type": "string"},
"license": {"title": "Model license", "type": "string"},
"author": {"title": "Model author name", "type": "string"},
"email": {"title": "Model author email", "type": "string", "format": "email"},
"url": {"title": "Model author URL", "type": "string", "format": "uri"},
"sources": {
"title": "Training data sources",
"type": "array",
"items": {"type": "string"},
},
"vectors": {
"title": "Included word vectors",
"type": "object",
"properties": {
"keys": {
"title": "Number of unique keys",
"type": "integer",
"minimum": 0,
},
"vectors": {
"title": "Number of unique vectors",
"type": "integer",
"minimum": 0,
},
"width": {
"title": "Number of dimensions",
"type": "integer",
"minimum": 0,
},
},
},
"accuracy": {
"title": "Accuracy numbers",
"type": "object",
"patternProperties": {"*": {"type": "number", "minimum": 0.0}},
},
"speed": {
"title": "Speed evaluation numbers",
"type": "object",
"patternProperties": {
"*": {
"oneOf": [
{"type": "number", "minimum": 0.0},
{"type": "integer", "minimum": 0},
]
}
},
},
},
"required": ["lang", "name", "version"],
}

60
spacy/cli/convert.py
View File

@ -1,7 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
import plac
from pathlib import Path
from wasabi import Printer
import srsly
@ -29,27 +25,20 @@ FILE_TYPES = ("json", "jsonl", "msg")
FILE_TYPES_STDOUT = ("json", "jsonl")
@plac.annotations(
input_file=("Input file", "positional", None, str),
output_dir=("Output directory. '-' for stdout.", "positional", None, str),
file_type=("Type of data to produce: {}".format(FILE_TYPES), "option", "t", str),
n_sents=("Number of sentences per doc (0 to disable)", "option", "n", int),
seg_sents=("Segment sentences (for -c ner)", "flag", "s"),
model=("Model for sentence segmentation (for -s)", "option", "b", str),
converter=("Converter: {}".format(tuple(CONVERTERS.keys())), "option", "c", str),
lang=("Language (if tokenizer required)", "option", "l", str),
morphology=("Enable appending morphology to tags", "flag", "m", bool),
)
def convert(
input_file,
output_dir="-",
file_type="json",
n_sents=1,
seg_sents=False,
model=None,
morphology=False,
converter="auto",
lang=None,
# fmt: off
input_file: ("Input file", "positional", None, str),
output_dir: ("Output directory. '-' for stdout.", "positional", None, str) = "-",
file_type: (f"Type of data to produce: {FILE_TYPES}", "option", "t", str, FILE_TYPES) = "json",
n_sents: ("Number of sentences per doc (0 to disable)", "option", "n", int) = 1,
seg_sents: ("Segment sentences (for -c ner)", "flag", "s") = False,
model: ("Model for sentence segmentation (for -s)", "option", "b", str) = None,
morphology: ("Enable appending morphology to tags", "flag", "m", bool) = False,
merge_subtokens: ("Merge CoNLL-U subtokens", "flag", "T", bool) = False,
converter: (f"Converter: {tuple(CONVERTERS.keys())}", "option", "c", str) = "auto",
ner_map_path: ("NER tag mapping (as JSON-encoded dict of entity types)", "option", "N", Path) = None,
lang: ("Language (if tokenizer required)", "option", "l", str) = None,
# fmt: on
):
"""
Convert files into JSON format for use with train command and other
@ -60,16 +49,10 @@ def convert(
no_print = output_dir == "-"
msg = Printer(no_print=no_print)
input_path = Path(input_file)
if file_type not in FILE_TYPES:
msg.fail(
"Unknown file type: '{}'".format(file_type),
"Supported file types: '{}'".format(", ".join(FILE_TYPES)),
exits=1,
)
if file_type not in FILE_TYPES_STDOUT and output_dir == "-":
# TODO: support msgpack via stdout in srsly?
msg.fail(
"Can't write .{} data to stdout.".format(file_type),
f"Can't write .{file_type} data to stdout",
"Please specify an output directory.",
exits=1,
)
@ -93,21 +76,26 @@ def convert(
"Can't automatically detect NER format. Conversion may not succeed. See https://spacy.io/api/cli#convert"
)
if converter not in CONVERTERS:
msg.fail("Can't find converter for {}".format(converter), exits=1)
msg.fail(f"Can't find converter for {converter}", exits=1)
ner_map = None
if ner_map_path is not None:
ner_map = srsly.read_json(ner_map_path)
# Use converter function to convert data
func = CONVERTERS[converter]
data = func(
input_data,
n_sents=n_sents,
seg_sents=seg_sents,
use_morphology=morphology,
append_morphology=morphology,
merge_subtokens=merge_subtokens,
lang=lang,
model=model,
no_print=no_print,
ner_map=ner_map,
)
if output_dir != "-":
# Export data to a file
suffix = ".{}".format(file_type)
suffix = f".{file_type}"
output_file = Path(output_dir) / Path(input_path.parts[-1]).with_suffix(suffix)
if file_type == "json":
srsly.write_json(output_file, data)
@ -115,9 +103,7 @@ def convert(
srsly.write_jsonl(output_file, data)
elif file_type == "msg":
srsly.write_msgpack(output_file, data)
msg.good(
"Generated output file ({} documents): {}".format(len(data), output_file)
)
msg.good(f"Generated output file ({len(data)} documents): {output_file}")
else:
# Print to stdout
if file_type == "json":

13
spacy/cli/converters/conll_ner2json.py
View File

@ -1,6 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
from wasabi import Printer
from ...gold import iob_to_biluo
@ -64,9 +61,9 @@ def conll_ner2json(
# sentence segmentation required for document segmentation
if n_sents > 0 and not seg_sents:
msg.warn(
"No sentence boundaries found to use with option `-n {}`. "
"Use `-s` to automatically segment sentences or `-n 0` "
"to disable.".format(n_sents)
f"No sentence boundaries found to use with option `-n {n_sents}`. "
f"Use `-s` to automatically segment sentences or `-n 0` "
f"to disable."
)
else:
n_sents_info(msg, n_sents)
@ -129,7 +126,7 @@ def segment_sents_and_docs(doc, n_sents, doc_delimiter, model=None, msg=None):
if model:
nlp = load_model(model)
if "parser" in nlp.pipe_names:
msg.info("Segmenting sentences with parser from model '{}'.".format(model))
msg.info(f"Segmenting sentences with parser from model '{model}'.")
sentencizer = nlp.get_pipe("parser")
if not sentencizer:
msg.info(
@ -166,7 +163,7 @@ def segment_docs(input_data, n_sents, doc_delimiter):
def n_sents_info(msg, n_sents):
msg.info("Grouping every {} sentences into a document.".format(n_sents))
msg.info(f"Grouping every {n_sents} sentences into a document.")
if n_sents == 1:
msg.warn(
"To generate better training data, you may want to group "

392
spacy/cli/converters/conllu2json.py
View File

@ -1,141 +1,349 @@
# coding: utf8
from __future__ import unicode_literals
import re
from ...gold import iob_to_biluo
from ...gold import Example
from ...gold import iob_to_biluo, spans_from_biluo_tags, biluo_tags_from_offsets
from ...language import Language
from ...tokens import Doc, Token
from .conll_ner2json import n_sents_info
from wasabi import Printer
def conllu2json(input_data, n_sents=10, use_morphology=False, lang=None, **_):
def conllu2json(
input_data,
n_sents=10,
append_morphology=False,
lang=None,
ner_map=None,
merge_subtokens=False,
no_print=False,
**_
):
"""
Convert conllu files into JSON format for use with train cli.
use_morphology parameter enables appending morphology to tags, which is
append_morphology parameter enables appending morphology to tags, which is
useful for languages such as Spanish, where UD tags are not so rich.
Extract NER tags if available and convert them so that they follow
BILUO and the Wikipedia scheme
"""
# by @dvsrepo, via #11 explosion/spacy-dev-resources
# by @katarkor
MISC_NER_PATTERN = "^((?:name|NE)=)?([BILU])-([A-Z_]+)|O$"
msg = Printer(no_print=no_print)
n_sents_info(msg, n_sents)
docs = []
raw = ""
sentences = []
conll_tuples = read_conllx(input_data, use_morphology=use_morphology)
checked_for_ner = False
has_ner_tags = False
for i, (raw_text, tokens) in enumerate(conll_tuples):
sentence, brackets = tokens[0]
if not checked_for_ner:
has_ner_tags = is_ner(sentence[5][0])
checked_for_ner = True
sentences.append(generate_sentence(sentence, has_ner_tags))
conll_data = read_conllx(
input_data,
append_morphology=append_morphology,
ner_tag_pattern=MISC_NER_PATTERN,
ner_map=ner_map,
merge_subtokens=merge_subtokens,
)
has_ner_tags = has_ner(input_data, MISC_NER_PATTERN)
for i, example in enumerate(conll_data):
raw += example.text
sentences.append(
generate_sentence(
example.token_annotation,
has_ner_tags,
MISC_NER_PATTERN,
ner_map=ner_map,
)
)
# Real-sized documents could be extracted using the comments on the
# conluu document
# conllu document
if len(sentences) % n_sents == 0:
doc = create_doc(sentences, i)
doc = create_json_doc(raw, sentences, i)
docs.append(doc)
raw = ""
sentences = []
if sentences:
doc = create_doc(sentences, i)
doc = create_json_doc(raw, sentences, i)
docs.append(doc)
return docs
def is_ner(tag):
def has_ner(input_data, ner_tag_pattern):
"""
Check the 10th column of the first token to determine if the file contains
NER tags
Check the MISC column for NER tags.
"""
tag_match = re.match("([A-Z_]+)-([A-Z_]+)", tag)
if tag_match:
return True
elif tag == "O":
return True
else:
return False
def read_conllx(input_data, use_morphology=False, n=0):
i = 0
for sent in input_data.strip().split("\n\n"):
lines = sent.strip().split("\n")
if lines:
while lines[0].startswith("#"):
lines.pop(0)
tokens = []
for line in lines:
parts = line.split("\t")
id_, word, lemma, pos, tag, morph, head, dep, _1, iob = parts
if "-" in id_ or "." in id_:
continue
try:
id_ = int(id_) - 1
head = (int(head) - 1) if head not in ["0", "_"] else id_
dep = "ROOT" if dep == "root" else dep
tag = pos if tag == "_" else tag
tag = tag + "__" + morph if use_morphology else tag
iob = iob if iob else "O"
tokens.append((id_, word, tag, head, dep, iob))
except: # noqa: E722
print(line)
raise
tuples = [list(t) for t in zip(*tokens)]
yield (None, [[tuples, []]])
i += 1
if n >= 1 and i >= n:
id_, word, lemma, pos, tag, morph, head, dep, _1, misc = parts
for misc_part in misc.split("|"):
if re.match(ner_tag_pattern, misc_part):
return True
return False
def read_conllx(
input_data,
append_morphology=False,
merge_subtokens=False,
ner_tag_pattern="",
ner_map=None,
):
""" Yield examples, one for each sentence """
vocab = Language.Defaults.create_vocab() # need vocab to make a minimal Doc
for sent in input_data.strip().split("\n\n"):
lines = sent.strip().split("\n")
if lines:
while lines[0].startswith("#"):
lines.pop(0)
example = example_from_conllu_sentence(
vocab,
lines,
ner_tag_pattern,
merge_subtokens=merge_subtokens,
append_morphology=append_morphology,
ner_map=ner_map,
)
yield example
def get_entities(lines, tag_pattern, ner_map=None):
"""Find entities in the MISC column according to the pattern and map to
final entity type with `ner_map` if mapping present. Entity tag is 'O' if
the pattern is not matched.
lines (str): CONLL-U lines for one sentences
tag_pattern (str): Regex pattern for entity tag
ner_map (dict): Map old NER tag names to new ones, '' maps to O.
RETURNS (list): List of BILUO entity tags
"""
miscs = []
for line in lines:
parts = line.split("\t")
id_, word, lemma, pos, tag, morph, head, dep, _1, misc = parts
if "-" in id_ or "." in id_:
continue
miscs.append(misc)
iob = []
for misc in miscs:
iob_tag = "O"
for misc_part in misc.split("|"):
tag_match = re.match(tag_pattern, misc_part)
if tag_match:
prefix = tag_match.group(2)
suffix = tag_match.group(3)
if prefix and suffix:
iob_tag = prefix + "-" + suffix
if ner_map:
suffix = ner_map.get(suffix, suffix)
if suffix == "":
iob_tag = "O"
else:
iob_tag = prefix + "-" + suffix
break
iob.append(iob_tag)
return iob_to_biluo(iob)
def simplify_tags(iob):
"""
Simplify tags obtained from the dataset in order to follow Wikipedia
scheme (PER, LOC, ORG, MISC). 'PER', 'LOC' and 'ORG' keep their tags, while
'GPE_LOC' is simplified to 'LOC', 'GPE_ORG' to 'ORG' and all remaining tags to
'MISC'.
"""
new_iob = []
for tag in iob:
tag_match = re.match("([A-Z_]+)-([A-Z_]+)", tag)
if tag_match:
prefix = tag_match.group(1)
suffix = tag_match.group(2)
if suffix == "GPE_LOC":
suffix = "LOC"
elif suffix == "GPE_ORG":
suffix = "ORG"
elif suffix != "PER" and suffix != "LOC" and suffix != "ORG":
suffix = "MISC"
tag = prefix + "-" + suffix
new_iob.append(tag)
return new_iob
def generate_sentence(sent, has_ner_tags):
(id_, word, tag, head, dep, iob) = sent
def generate_sentence(token_annotation, has_ner_tags, tag_pattern, ner_map=None):
sentence = {}
tokens = []
if has_ner_tags:
iob = simplify_tags(iob)
biluo = iob_to_biluo(iob)
for i, id in enumerate(id_):
for i, id_ in enumerate(token_annotation.ids):
token = {}
token["id"] = id
token["orth"] = word[i]
token["tag"] = tag[i]
token["head"] = head[i] - id
token["dep"] = dep[i]
token["id"] = id_
token["orth"] = token_annotation.get_word(i)
token["tag"] = token_annotation.get_tag(i)
token["pos"] = token_annotation.get_pos(i)
token["lemma"] = token_annotation.get_lemma(i)
token["morph"] = token_annotation.get_morph(i)
token["head"] = token_annotation.get_head(i) - id_
token["dep"] = token_annotation.get_dep(i)
if has_ner_tags:
token["ner"] = biluo[i]
token["ner"] = token_annotation.get_entity(i)
tokens.append(token)
sentence["tokens"] = tokens
return sentence
def create_doc(sentences, id):
def create_json_doc(raw, sentences, id_):
doc = {}
paragraph = {}
doc["id"] = id
doc["id"] = id_
doc["paragraphs"] = []
paragraph["raw"] = raw.strip()
paragraph["sentences"] = sentences
doc["paragraphs"].append(paragraph)
return doc
def example_from_conllu_sentence(
vocab,
lines,
ner_tag_pattern,
merge_subtokens=False,
append_morphology=False,
ner_map=None,
):
"""Create an Example from the lines for one CoNLL-U sentence, merging
subtokens and appending morphology to tags if required.
lines (str): The non-comment lines for a CoNLL-U sentence
ner_tag_pattern (str): The regex pattern for matching NER in MISC col
RETURNS (Example): An example containing the annotation
"""
# create a Doc with each subtoken as its own token
# if merging subtokens, each subtoken orth is the merged subtoken form
if not Token.has_extension("merged_orth"):
Token.set_extension("merged_orth", default="")
if not Token.has_extension("merged_lemma"):
Token.set_extension("merged_lemma", default="")
if not Token.has_extension("merged_morph"):
Token.set_extension("merged_morph", default="")
if not Token.has_extension("merged_spaceafter"):
Token.set_extension("merged_spaceafter", default="")
words, spaces, tags, poses, morphs, lemmas = [], [], [], [], [], []
heads, deps = [], []
subtok_word = ""
in_subtok = False
for i in range(len(lines)):
line = lines[i]
parts = line.split("\t")
id_, word, lemma, pos, tag, morph, head, dep, _1, misc = parts
if "." in id_:
continue
if "-" in id_:
in_subtok = True
if "-" in id_:
in_subtok = True
subtok_word = word
subtok_start, subtok_end = id_.split("-")
subtok_spaceafter = "SpaceAfter=No" not in misc
continue
if merge_subtokens and in_subtok:
words.append(subtok_word)
else:
words.append(word)
if in_subtok:
if id_ == subtok_end:
spaces.append(subtok_spaceafter)
else:
spaces.append(False)
elif "SpaceAfter=No" in misc:
spaces.append(False)
else:
spaces.append(True)
if in_subtok and id_ == subtok_end:
subtok_word = ""
in_subtok = False
id_ = int(id_) - 1
head = (int(head) - 1) if head not in ("0", "_") else id_
tag = pos if tag == "_" else tag
morph = morph if morph != "_" else ""
dep = "ROOT" if dep == "root" else dep
lemmas.append(lemma)
poses.append(pos)
tags.append(tag)
morphs.append(morph)
heads.append(head)
deps.append(dep)
doc = Doc(vocab, words=words, spaces=spaces)
for i in range(len(doc)):
doc[i].tag_ = tags[i]
doc[i].pos_ = poses[i]
doc[i].dep_ = deps[i]
doc[i].lemma_ = lemmas[i]
doc[i].head = doc[heads[i]]
doc[i]._.merged_orth = words[i]
doc[i]._.merged_morph = morphs[i]
doc[i]._.merged_lemma = lemmas[i]
doc[i]._.merged_spaceafter = spaces[i]
ents = get_entities(lines, ner_tag_pattern, ner_map)
doc.ents = spans_from_biluo_tags(doc, ents)
doc.is_parsed = True
doc.is_tagged = True
if merge_subtokens:
doc = merge_conllu_subtokens(lines, doc)
# create Example from custom Doc annotation
ids, words, tags, heads, deps = [], [], [], [], []
pos, lemmas, morphs, spaces = [], [], [], []
for i, t in enumerate(doc):
ids.append(i)
words.append(t._.merged_orth)
if append_morphology and t._.merged_morph:
tags.append(t.tag_ + "__" + t._.merged_morph)
else:
tags.append(t.tag_)
pos.append(t.pos_)
morphs.append(t._.merged_morph)
lemmas.append(t._.merged_lemma)
heads.append(t.head.i)
deps.append(t.dep_)
spaces.append(t._.merged_spaceafter)
ent_offsets = [(e.start_char, e.end_char, e.label_) for e in doc.ents]
ents = biluo_tags_from_offsets(doc, ent_offsets)
raw = ""
for word, space in zip(words, spaces):
raw += word
if space:
raw += " "
example = Example(doc=raw)
example.set_token_annotation(
ids=ids,
words=words,
tags=tags,
pos=pos,
morphs=morphs,
lemmas=lemmas,
heads=heads,
deps=deps,
entities=ents,
)
return example
def merge_conllu_subtokens(lines, doc):
# identify and process all subtoken spans to prepare attrs for merging
subtok_spans = []
for line in lines:
parts = line.split("\t")
id_, word, lemma, pos, tag, morph, head, dep, _1, misc = parts
if "-" in id_:
subtok_start, subtok_end = id_.split("-")
subtok_span = doc[int(subtok_start) - 1 : int(subtok_end)]
subtok_spans.append(subtok_span)
# create merged tag, morph, and lemma values
tags = []
morphs = {}
lemmas = []
for token in subtok_span:
tags.append(token.tag_)
lemmas.append(token.lemma_)
if token._.merged_morph:
for feature in token._.merged_morph.split("|"):
field, values = feature.split("=", 1)
if field not in morphs:
morphs[field] = set()
for value in values.split(","):
morphs[field].add(value)
# create merged features for each morph field
for field, values in morphs.items():
morphs[field] = field + "=" + ",".join(sorted(values))
# set the same attrs on all subtok tokens so that whatever head the
# retokenizer chooses, the final attrs are available on that token
for token in subtok_span:
token._.merged_orth = token.orth_
token._.merged_lemma = " ".join(lemmas)
token.tag_ = "_".join(tags)
token._.merged_morph = "|".join(sorted(morphs.values()))
token._.merged_spaceafter = (
True if subtok_span[-1].whitespace_ else False
)
with doc.retokenize() as retokenizer:
for span in subtok_spans:
retokenizer.merge(span)
return doc

3
spacy/cli/converters/iob2json.py
View File

@ -1,6 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
from wasabi import Printer
from ...gold import iob_to_biluo

3
spacy/cli/converters/jsonl2json.py
View File

@ -1,6 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
import srsly
from ...gold import docs_to_json

344
spacy/cli/debug_data.py
View File

@ -1,9 +1,5 @@
# coding: utf8
from __future__ import unicode_literals, print_function
from pathlib import Path
from collections import Counter
import plac
import sys
import srsly
from wasabi import Printer, MESSAGES
@ -22,29 +18,18 @@ BLANK_MODEL_MIN_THRESHOLD = 100
BLANK_MODEL_THRESHOLD = 2000
@plac.annotations(
# fmt: off
lang=("model language", "positional", None, str),
train_path=("location of JSON-formatted training data", "positional", None, Path),
dev_path=("location of JSON-formatted development data", "positional", None, Path),
tag_map_path=("Location of JSON-formatted tag map", "option", "tm", Path),
base_model=("name of model to update (optional)", "option", "b", str),
pipeline=("Comma-separated names of pipeline components to train", "option", "p", str),
ignore_warnings=("Ignore warnings, only show stats and errors", "flag", "IW", bool),
verbose=("Print additional information and explanations", "flag", "V", bool),
no_format=("Don't pretty-print the results", "flag", "NF", bool),
# fmt: on
)
def debug_data(
lang,
train_path,
dev_path,
tag_map_path=None,
base_model=None,
pipeline="tagger,parser,ner",
ignore_warnings=False,
verbose=False,
no_format=False,
# fmt: off
lang: ("Model language", "positional", None, str),
train_path: ("Location of JSON-formatted training data", "positional", None, Path),
dev_path: ("Location of JSON-formatted development data", "positional", None, Path),
tag_map_path: ("Location of JSON-formatted tag map", "option", "tm", Path) = None,
base_model: ("Name of model to update (optional)", "option", "b", str) = None,
pipeline: ("Comma-separated names of pipeline components to train", "option", "p", str) = "tagger,parser,ner",
ignore_warnings: ("Ignore warnings, only show stats and errors", "flag", "IW", bool) = False,
verbose: ("Print additional information and explanations", "flag", "V", bool) = False,
no_format: ("Don't pretty-print the results", "flag", "NF", bool) = False,
# fmt: on
):
"""
Analyze, debug and validate your training and development data, get useful
@ -85,20 +70,16 @@ def debug_data(
with msg.loading("Loading corpus..."):
corpus = GoldCorpus(train_path, dev_path)
try:
train_docs = list(corpus.train_docs(nlp))
train_docs_unpreprocessed = list(
corpus.train_docs_without_preprocessing(nlp)
train_dataset = list(corpus.train_dataset(nlp))
train_dataset_unpreprocessed = list(
corpus.train_dataset_without_preprocessing(nlp)
)
except ValueError as e:
loading_train_error_message = "Training data cannot be loaded: {}".format(
str(e)
)
loading_train_error_message = f"Training data cannot be loaded: {e}"
try:
dev_docs = list(corpus.dev_docs(nlp))
dev_dataset = list(corpus.dev_dataset(nlp))
except ValueError as e:
loading_dev_error_message = "Development data cannot be loaded: {}".format(
str(e)
)
loading_dev_error_message = f"Development data cannot be loaded: {e}"
if loading_train_error_message or loading_dev_error_message:
if loading_train_error_message:
msg.fail(loading_train_error_message)
@ -107,82 +88,68 @@ def debug_data(
sys.exit(1)
msg.good("Corpus is loadable")
# Create all gold data here to avoid iterating over the train_docs constantly
gold_train_data = _compile_gold(train_docs, pipeline, nlp)
# Create all gold data here to avoid iterating over the train_dataset constantly
gold_train_data = _compile_gold(train_dataset, pipeline, nlp)
gold_train_unpreprocessed_data = _compile_gold(
train_docs_unpreprocessed, pipeline, nlp
train_dataset_unpreprocessed, pipeline
)
gold_dev_data = _compile_gold(dev_docs, pipeline, nlp)
gold_dev_data = _compile_gold(dev_dataset, pipeline, nlp)
train_texts = gold_train_data["texts"]
dev_texts = gold_dev_data["texts"]
msg.divider("Training stats")
msg.text("Training pipeline: {}".format(", ".join(pipeline)))
msg.text(f"Training pipeline: {', '.join(pipeline)}")
for pipe in [p for p in pipeline if p not in nlp.factories]:
msg.fail("Pipeline component '{}' not available in factories".format(pipe))
msg.fail(f"Pipeline component '{pipe}' not available in factories")
if base_model:
msg.text("Starting with base model '{}'".format(base_model))
msg.text(f"Starting with base model '{base_model}'")
else:
msg.text("Starting with blank model '{}'".format(lang))
msg.text("{} training docs".format(len(train_docs)))
msg.text("{} evaluation docs".format(len(dev_docs)))
msg.text(f"Starting with blank model '{lang}'")
msg.text(f"{len(train_dataset)} training docs")
msg.text(f"{len(dev_dataset)} evaluation docs")
if not len(dev_docs):
if not len(gold_dev_data):
msg.fail("No evaluation docs")
overlap = len(train_texts.intersection(dev_texts))
if overlap:
msg.warn("{} training examples also in evaluation data".format(overlap))
msg.warn(f"{overlap} training examples also in evaluation data")
else:
msg.good("No overlap between training and evaluation data")
if not base_model and len(train_docs) < BLANK_MODEL_THRESHOLD:
text = "Low number of examples to train from a blank model ({})".format(
len(train_docs)
if not base_model and len(train_dataset) < BLANK_MODEL_THRESHOLD:
text = (
f"Low number of examples to train from a blank model ({len(train_dataset)})"
)
if len(train_docs) < BLANK_MODEL_MIN_THRESHOLD:
if len(train_dataset) < BLANK_MODEL_MIN_THRESHOLD:
msg.fail(text)
else:
msg.warn(text)
msg.text(
"It's recommended to use at least {} examples (minimum {})".format(
BLANK_MODEL_THRESHOLD, BLANK_MODEL_MIN_THRESHOLD
),
f"It's recommended to use at least {BLANK_MODEL_THRESHOLD} examples "
f"(minimum {BLANK_MODEL_MIN_THRESHOLD})",
show=verbose,
)
msg.divider("Vocab & Vectors")
n_words = gold_train_data["n_words"]
msg.info(
"{} total {} in the data ({} unique)".format(
n_words, "word" if n_words == 1 else "words", len(gold_train_data["words"])
)
f"{n_words} total word(s) in the data ({len(gold_train_data['words'])} unique)"
)
if gold_train_data["n_misaligned_words"] > 0:
msg.warn(
"{} misaligned tokens in the training data".format(
gold_train_data["n_misaligned_words"]
)
)
n_misaligned = gold_train_data["n_misaligned_words"]
msg.warn(f"{n_misaligned} misaligned tokens in the training data")
if gold_dev_data["n_misaligned_words"] > 0:
msg.warn(
"{} misaligned tokens in the dev data".format(
gold_dev_data["n_misaligned_words"]
)
)
n_misaligned = gold_dev_data["n_misaligned_words"]
msg.warn(f"{n_misaligned} misaligned tokens in the dev data")
most_common_words = gold_train_data["words"].most_common(10)
msg.text(
"10 most common words: {}".format(
_format_labels(most_common_words, counts=True)
),
f"10 most common words: {_format_labels(most_common_words, counts=True)}",
show=verbose,
)
if len(nlp.vocab.vectors):
msg.info(
"{} vectors ({} unique keys, {} dimensions)".format(
len(nlp.vocab.vectors),
nlp.vocab.vectors.n_keys,
nlp.vocab.vectors_length,
)
f"{len(nlp.vocab.vectors)} vectors ({nlp.vocab.vectors.n_keys} "
f"unique keys, {nlp.vocab.vectors_length} dimensions)"
)
n_missing_vectors = sum(gold_train_data["words_missing_vectors"].values())
msg.warn(
@ -205,7 +172,7 @@ def debug_data(
if "ner" in pipeline:
# Get all unique NER labels present in the data
labels = set(
label for label in gold_train_data["ner"] if label not in ("O", "-")
label for label in gold_train_data["ner"] if label not in ("O", "-", None)
)
label_counts = gold_train_data["ner"]
model_labels = _get_labels_from_model(nlp, "ner")
@ -218,19 +185,10 @@ def debug_data(
msg.divider("Named Entity Recognition")
msg.info(
"{} new {}, {} existing {}".format(
len(new_labels),
"label" if len(new_labels) == 1 else "labels",
len(existing_labels),
"label" if len(existing_labels) == 1 else "labels",
)
f"{len(new_labels)} new label(s), {len(existing_labels)} existing label(s)"
)
missing_values = label_counts["-"]
msg.text(
"{} missing {} (tokens with '-' label)".format(
missing_values, "value" if missing_values == 1 else "values"
)
)
msg.text(f"{missing_values} missing value(s) (tokens with '-' label)")
for label in new_labels:
if len(label) == 0:
msg.fail("Empty label found in new labels")
@ -241,43 +199,28 @@ def debug_data(
if label != "-"
]
labels_with_counts = _format_labels(labels_with_counts, counts=True)
msg.text("New: {}".format(labels_with_counts), show=verbose)
msg.text(f"New: {labels_with_counts}", show=verbose)
if existing_labels:
msg.text(
"Existing: {}".format(_format_labels(existing_labels)), show=verbose
)
msg.text(f"Existing: {_format_labels(existing_labels)}", show=verbose)
if gold_train_data["ws_ents"]:
msg.fail(
"{} invalid whitespace entity span(s)".format(
gold_train_data["ws_ents"]
)
)
msg.fail(f"{gold_train_data['ws_ents']} invalid whitespace entity spans")
has_ws_ents_error = True
if gold_train_data["punct_ents"]:
msg.warn(
"{} entity span(s) with punctuation".format(
gold_train_data["punct_ents"]
)
)
msg.warn(f"{gold_train_data['punct_ents']} entity span(s) with punctuation")
has_punct_ents_warning = True
for label in new_labels:
if label_counts[label] <= NEW_LABEL_THRESHOLD:
msg.warn(
"Low number of examples for new label '{}' ({})".format(
label, label_counts[label]
)
f"Low number of examples for new label '{label}' ({label_counts[label]})"
)
has_low_data_warning = True
with msg.loading("Analyzing label distribution..."):
neg_docs = _get_examples_without_label(train_docs, label)
neg_docs = _get_examples_without_label(train_dataset, label)
if neg_docs == 0:
msg.warn(
"No examples for texts WITHOUT new label '{}'".format(label)
)
msg.warn(f"No examples for texts WITHOUT new label '{label}'")
has_no_neg_warning = True
if not has_low_data_warning:
@ -291,8 +234,8 @@ def debug_data(
if has_low_data_warning:
msg.text(
"To train a new entity type, your data should include at "
"least {} instances of the new label".format(NEW_LABEL_THRESHOLD),
f"To train a new entity type, your data should include at "
f"least {NEW_LABEL_THRESHOLD} instances of the new label",
show=verbose,
)
if has_no_neg_warning:
@ -321,27 +264,21 @@ def debug_data(
new_labels = [l for l in labels if l not in model_labels]
existing_labels = [l for l in labels if l in model_labels]
msg.info(
"Text Classification: {} new label(s), {} existing label(s)".format(
len(new_labels), len(existing_labels)
)
f"Text Classification: {len(new_labels)} new label(s), "
f"{len(existing_labels)} existing label(s)"
)
if new_labels:
labels_with_counts = _format_labels(
gold_train_data["cats"].most_common(), counts=True
)
msg.text("New: {}".format(labels_with_counts), show=verbose)
msg.text(f"New: {labels_with_counts}", show=verbose)
if existing_labels:
msg.text(
"Existing: {}".format(_format_labels(existing_labels)), show=verbose
)
msg.text(f"Existing: {_format_labels(existing_labels)}", show=verbose)
if set(gold_train_data["cats"]) != set(gold_dev_data["cats"]):
msg.fail(
"The train and dev labels are not the same. "
"Train labels: {}. "
"Dev labels: {}.".format(
_format_labels(gold_train_data["cats"]),
_format_labels(gold_dev_data["cats"]),
)
f"The train and dev labels are not the same. "
f"Train labels: {_format_labels(gold_train_data['cats'])}. "
f"Dev labels: {_format_labels(gold_dev_data['cats'])}."
)
if gold_train_data["n_cats_multilabel"] > 0:
msg.info(
@ -371,27 +308,16 @@ def debug_data(
msg.divider("Part-of-speech Tagging")
labels = [label for label in gold_train_data["tags"]]
tag_map = nlp.vocab.morphology.tag_map
msg.info(
"{} {} in data ({} {} in tag map)".format(
len(labels),
"label" if len(labels) == 1 else "labels",
len(tag_map),
"label" if len(tag_map) == 1 else "labels",
)
)
msg.info(f"{len(labels)} label(s) in data ({len(tag_map)} label(s) in tag map)")
labels_with_counts = _format_labels(
gold_train_data["tags"].most_common(), counts=True
)
msg.text(labels_with_counts, show=verbose)
non_tagmap = [l for l in labels if l not in tag_map]
if not non_tagmap:
msg.good("All labels present in tag map for language '{}'".format(nlp.lang))
msg.good(f"All labels present in tag map for language '{nlp.lang}'")
for label in non_tagmap:
msg.fail(
"Label '{}' not found in tag map for language '{}'".format(
label, nlp.lang
)
)
msg.fail(f"Label '{label}' not found in tag map for language '{nlp.lang}'")
if "parser" in pipeline:
has_low_data_warning = False
@ -399,21 +325,18 @@ def debug_data(
# profile sentence length
msg.info(
"Found {} sentence{} with an average length of {:.1f} words.".format(
gold_train_data["n_sents"],
"s" if len(train_docs) > 1 else "",
gold_train_data["n_words"] / gold_train_data["n_sents"],
)
f"Found {gold_train_data['n_sents']} sentence(s) with an average "
f"length of {gold_train_data['n_words'] / gold_train_data['n_sents']:.1f} words."
)
# check for documents with multiple sentences
sents_per_doc = gold_train_data["n_sents"] / len(gold_train_data["texts"])
if sents_per_doc < 1.1:
msg.warn(
"The training data contains {:.2f} sentences per "
"document. When there are very few documents containing more "
"than one sentence, the parser will not learn how to segment "
"longer texts into sentences.".format(sents_per_doc)
f"The training data contains {sents_per_doc:.2f} sentences per "
f"document. When there are very few documents containing more "
f"than one sentence, the parser will not learn how to segment "
f"longer texts into sentences."
)
# profile labels
@ -424,32 +347,13 @@ def debug_data(
labels_dev = [label for label in gold_dev_data["deps"]]
if gold_train_unpreprocessed_data["n_nonproj"] > 0:
msg.info(
"Found {} nonprojective train sentence{}".format(
gold_train_unpreprocessed_data["n_nonproj"],
"s" if gold_train_unpreprocessed_data["n_nonproj"] > 1 else "",
)
)
n_nonproj = gold_train_unpreprocessed_data["n_nonproj"]
msg.info(f"Found {n_nonproj} nonprojective train sentence(s)")
if gold_dev_data["n_nonproj"] > 0:
msg.info(
"Found {} nonprojective dev sentence{}".format(
gold_dev_data["n_nonproj"],
"s" if gold_dev_data["n_nonproj"] > 1 else "",
)
)
msg.info(
"{} {} in train data".format(
len(labels_train_unpreprocessed),
"label" if len(labels_train) == 1 else "labels",
)
)
msg.info(
"{} {} in projectivized train data".format(
len(labels_train), "label" if len(labels_train) == 1 else "labels"
)
)
n_nonproj = gold_dev_data["n_nonproj"]
msg.info(f"Found {n_nonproj} nonprojective dev sentence(s)")
msg.info(f"{labels_train_unpreprocessed} label(s) in train data")
msg.info(f"{len(labels_train)} label(s) in projectivized train data")
labels_with_counts = _format_labels(
gold_train_unpreprocessed_data["deps"].most_common(), counts=True
)
@ -459,9 +363,8 @@ def debug_data(
for label in gold_train_unpreprocessed_data["deps"]:
if gold_train_unpreprocessed_data["deps"][label] <= DEP_LABEL_THRESHOLD:
msg.warn(
"Low number of examples for label '{}' ({})".format(
label, gold_train_unpreprocessed_data["deps"][label]
)
f"Low number of examples for label '{label}' "
f"({gold_train_unpreprocessed_data['deps'][label]})"
)
has_low_data_warning = True
@ -470,22 +373,19 @@ def debug_data(
for label in gold_train_data["deps"]:
if gold_train_data["deps"][label] <= DEP_LABEL_THRESHOLD and "||" in label:
rare_projectivized_labels.append(
"{}: {}".format(label, str(gold_train_data["deps"][label]))
f"{label}: {gold_train_data['deps'][label]}"
)
if len(rare_projectivized_labels) > 0:
msg.warn(
"Low number of examples for {} label{} in the "
"projectivized dependency trees used for training. You may "
"want to projectivize labels such as punct before "
"training in order to improve parser performance.".format(
len(rare_projectivized_labels),
"s" if len(rare_projectivized_labels) > 1 else "",
)
f"Low number of examples for {len(rare_projectivized_labels)} "
"label(s) in the projectivized dependency trees used for "
"training. You may want to projectivize labels such as punct "
"before training in order to improve parser performance."
)
msg.warn(
"Projectivized labels with low numbers of examples: "
"{}".format("\n".join(rare_projectivized_labels)),
f"Projectivized labels with low numbers of examples: ",
", ".join(rare_projectivized_labels),
show=verbose,
)
has_low_data_warning = True
@ -493,50 +393,44 @@ def debug_data(
# labels only in train
if set(labels_train) - set(labels_dev):
msg.warn(
"The following labels were found only in the train data: "
"{}".format(", ".join(set(labels_train) - set(labels_dev))),
"The following labels were found only in the train data:",
", ".join(set(labels_train) - set(labels_dev)),
show=verbose,
)
# labels only in dev
if set(labels_dev) - set(labels_train):
msg.warn(
"The following labels were found only in the dev data: "
+ ", ".join(set(labels_dev) - set(labels_train)),
"The following labels were found only in the dev data:",
", ".join(set(labels_dev) - set(labels_train)),
show=verbose,
)
if has_low_data_warning:
msg.text(
"To train a parser, your data should include at "
"least {} instances of each label.".format(DEP_LABEL_THRESHOLD),
f"To train a parser, your data should include at "
f"least {DEP_LABEL_THRESHOLD} instances of each label.",
show=verbose,
)
# multiple root labels
if len(gold_train_unpreprocessed_data["roots"]) > 1:
msg.warn(
"Multiple root labels ({}) ".format(
", ".join(gold_train_unpreprocessed_data["roots"])
)
+ "found in training data. spaCy's parser uses a single root "
"label ROOT so this distinction will not be available."
f"Multiple root labels "
f"({', '.join(gold_train_unpreprocessed_data['roots'])}) "
f"found in training data. spaCy's parser uses a single root "
f"label ROOT so this distinction will not be available."
)
# these should not happen, but just in case
if gold_train_data["n_nonproj"] > 0:
msg.fail(
"Found {} nonprojective projectivized train sentence{}".format(
gold_train_data["n_nonproj"],
"s" if gold_train_data["n_nonproj"] > 1 else "",
)
f"Found {gold_train_data['n_nonproj']} nonprojective "
f"projectivized train sentence(s)"
)
if gold_train_data["n_cycles"] > 0:
msg.fail(
"Found {} projectivized train sentence{} with cycles".format(
gold_train_data["n_cycles"],
"s" if gold_train_data["n_cycles"] > 1 else "",
)
f"Found {gold_train_data['n_cycles']} projectivized train sentence(s) with cycles"
)
msg.divider("Summary")
@ -544,42 +438,34 @@ def debug_data(
warn_counts = msg.counts[MESSAGES.WARN]
fail_counts = msg.counts[MESSAGES.FAIL]
if good_counts:
msg.good(
"{} {} passed".format(
good_counts, "check" if good_counts == 1 else "checks"
)
)
msg.good(f"{good_counts} {'check' if good_counts == 1 else 'checks'} passed")
if warn_counts:
msg.warn(
"{} {}".format(warn_counts, "warning" if warn_counts == 1 else "warnings")
)
if fail_counts:
msg.fail("{} {}".format(fail_counts, "error" if fail_counts == 1 else "errors"))
msg.warn(f"{warn_counts} {'warning' if warn_counts == 1 else 'warnings'}")
if fail_counts:
msg.fail(f"{fail_counts} {'error' if fail_counts == 1 else 'errors'}")
sys.exit(1)
def _load_file(file_path, msg):
file_name = file_path.parts[-1]
if file_path.suffix == ".json":
with msg.loading("Loading {}...".format(file_name)):
with msg.loading(f"Loading {file_name}..."):
data = srsly.read_json(file_path)
msg.good("Loaded {}".format(file_name))
msg.good(f"Loaded {file_name}")
return data
elif file_path.suffix == ".jsonl":
with msg.loading("Loading {}...".format(file_name)):
with msg.loading(f"Loading {file_name}..."):
data = srsly.read_jsonl(file_path)
msg.good("Loaded {}".format(file_name))
msg.good(f"Loaded {file_name}")
return data
msg.fail(
"Can't load file extension {}".format(file_path.suffix),
f"Can't load file extension {file_path.suffix}",
"Expected .json or .jsonl",
exits=1,
)
def _compile_gold(train_docs, pipeline, nlp):
def _compile_gold(examples, pipeline, nlp):
data = {
"ner": Counter(),
"cats": Counter(),
@ -598,7 +484,9 @@ def _compile_gold(train_docs, pipeline, nlp):
"n_cats_multilabel": 0,
"texts": set(),
}
for doc, gold in train_docs:
for example in examples:
gold = example.gold
doc = example.doc
valid_words = [x for x in gold.words if x is not None]
data["words"].update(valid_words)
data["n_words"] += len(valid_words)
@ -651,17 +539,17 @@ def _compile_gold(train_docs, pipeline, nlp):
def _format_labels(labels, counts=False):
if counts:
return ", ".join(["'{}' ({})".format(l, c) for l, c in labels])
return ", ".join(["'{}'".format(l) for l in labels])
return ", ".join([f"'{l}' ({c})" for l, c in labels])
return ", ".join([f"'{l}'" for l in labels])
def _get_examples_without_label(data, label):
count = 0
for doc, gold in data:
for ex in data:
labels = [
label.split("-")[1]
for label in gold.ner
if label is not None and label not in ("O", "-")
for label in ex.gold.ner
if label not in ("O", "-", None)
]
if label not in labels:
count += 1

85
spacy/cli/download.py
View File

@ -1,30 +1,24 @@
# coding: utf8
from __future__ import unicode_literals
import plac
import requests
import os
import subprocess
import sys
from wasabi import msg
from .link import link
from ..util import get_package_path
from .. import about
from ..util import is_package, get_base_version
@plac.annotations(
model=("Model to download (shortcut or name)", "positional", None, str),
direct=("Force direct download of name + version", "flag", "d", bool),
pip_args=("Additional arguments to be passed to `pip install` on model install"),
)
def download(model, direct=False, *pip_args):
def download(
model: ("Model to download (shortcut or name)", "positional", None, str),
direct: ("Force direct download of name + version", "flag", "d", bool) = False,
*pip_args: ("Additional arguments to be passed to `pip install` on model install"),
):
"""
Download compatible model from default download path using pip. Model
can be shortcut, model name or, if --direct flag is set, full model name
with version. For direct downloads, the compatibility check will be skipped.
Download compatible model from default download path using pip. If --direct
flag is set, the command expects the full model name with version.
For direct downloads, the compatibility check will be skipped.
"""
if not require_package("spacy") and "--no-deps" not in pip_args:
if not is_package("spacy") and "--no-deps" not in pip_args:
msg.warn(
"Skipping model package dependencies and setting `--no-deps`. "
"You don't seem to have the spaCy package itself installed "
@ -50,77 +44,38 @@ def download(model, direct=False, *pip_args):
sys.exit(dl)
msg.good(
"Download and installation successful",
"You can now load the model via spacy.load('{}')".format(model_name),
f"You can now load the model via spacy.load('{model_name}')",
)
# Only create symlink if the model is installed via a shortcut like 'en'.
# There's no real advantage over an additional symlink for en_core_web_sm
# and if anything, it's more error prone and causes more confusion.
if model in shortcuts:
try:
# Get package path here because link uses
# pip.get_installed_distributions() to check if model is a
# package, which fails if model was just installed via
# subprocess
package_path = get_package_path(model_name)
link(model_name, model, force=True, model_path=package_path)
except: # noqa: E722
# Dirty, but since spacy.download and the auto-linking is
# mostly a convenience wrapper, it's best to show a success
# message and loading instructions, even if linking fails.
msg.warn(
"Download successful but linking failed",
"Creating a shortcut link for '{}' didn't work (maybe you "
"don't have admin permissions?), but you can still load "
"the model via its full package name: "
"nlp = spacy.load('{}')".format(model, model_name),
)
# If a model is downloaded and then loaded within the same process, our
# is_package check currently fails, because pkg_resources.working_set
# is not refreshed automatically (see #3923). We're trying to work
# around this here be requiring the package explicitly.
require_package(model_name)
def require_package(name):
try:
import pkg_resources
pkg_resources.working_set.require(name)
return True
except: # noqa: E722
return False
def get_json(url, desc):
r = requests.get(url)
if r.status_code != 200:
msg.fail(
"Server error ({})".format(r.status_code),
"Couldn't fetch {}. Please find a model for your spaCy "
"installation (v{}), and download it manually. For more "
"details, see the documentation: "
"https://spacy.io/usage/models".format(desc, about.__version__),
f"Server error ({r.status_code})",
f"Couldn't fetch {desc}. Please find a model for your spaCy "
f"installation (v{about.__version__}), and download it manually. "
f"For more details, see the documentation: "
f"https://spacy.io/usage/models",
exits=1,
)
return r.json()
def get_compatibility():
version = about.__version__
version = version.rsplit(".dev", 1)[0]
version = get_base_version(about.__version__)
comp_table = get_json(about.__compatibility__, "compatibility table")
comp = comp_table["spacy"]
if version not in comp:
msg.fail("No compatible models found for v{} of spaCy".format(version), exits=1)
msg.fail(f"No compatible models found for v{version} of spaCy", exits=1)
return comp[version]
def get_version(model, comp):
model = model.rsplit(".dev", 1)[0]
model = get_base_version(model)
if model not in comp:
msg.fail(
"No compatible model found for '{}' "
"(spaCy v{}).".format(model, about.__version__),
f"No compatible model found for '{model}' (spaCy v{about.__version__})",
exits=1,
)
return comp[model][0]

65
spacy/cli/evaluate.py
View File

@ -1,7 +1,3 @@
# coding: utf8
from __future__ import unicode_literals, division, print_function
import plac
from timeit import default_timer as timer
from wasabi import msg
@ -10,23 +6,16 @@ from .. import util
from .. import displacy
@plac.annotations(
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", "dp", str),
displacy_limit=("Limit of parses to render as HTML", "option", "dl", int),
return_scores=("Return dict containing model scores", "flag", "R", bool),
)
def evaluate(
model,
data_path,
gpu_id=-1,
gold_preproc=False,
displacy_path=None,
displacy_limit=25,
return_scores=False,
# fmt: off
model: ("Model name or path", "positional", None, str),
data_path: ("Location of JSON-formatted evaluation data", "positional", None, str),
gpu_id: ("Use GPU", "option", "g", int) = -1,
gold_preproc: ("Use gold preprocessing", "flag", "G", bool) = False,
displacy_path: ("Directory to output rendered parses as HTML", "option", "dp", str) = None,
displacy_limit: ("Limit of parses to render as HTML", "option", "dl", int) = 25,
return_scores: ("Return dict containing model scores", "flag", "R", bool) = False,
# fmt: on
):
"""
Evaluate a model. To render a sample of parses in a HTML file, set an
@ -47,28 +36,34 @@ def evaluate(
nlp = util.get_lang_class(model.replace("blank:", ""))()
else:
nlp = util.load_model(model)
dev_docs = list(corpus.dev_docs(nlp, gold_preproc=gold_preproc))
dev_dataset = list(corpus.dev_dataset(nlp, gold_preproc=gold_preproc))
begin = timer()
scorer = nlp.evaluate(dev_docs, verbose=False)
scorer = nlp.evaluate(dev_dataset, verbose=False)
end = timer()
nwords = sum(len(doc_gold[0]) for doc_gold in dev_docs)
nwords = sum(len(ex.doc) for ex in dev_dataset)
results = {
"Time": "%.2f s" % (end - begin),
"Time": f"{end - begin:.2f} s",
"Words": nwords,
"Words/s": "%.0f" % (nwords / (end - begin)),
"TOK": "%.2f" % scorer.token_acc,
"POS": "%.2f" % scorer.tags_acc,
"UAS": "%.2f" % scorer.uas,
"LAS": "%.2f" % scorer.las,
"NER P": "%.2f" % scorer.ents_p,
"NER R": "%.2f" % scorer.ents_r,
"NER F": "%.2f" % scorer.ents_f,
"Textcat": "%.2f" % scorer.textcat_score,
"Words/s": f"{nwords / (end - begin):.0f}",
"TOK": f"{scorer.token_acc:.2f}",
"TAG": f"{scorer.tags_acc:.2f}",
"POS": f"{scorer.pos_acc:.2f}",
"MORPH": f"{scorer.morphs_acc:.2f}",
"UAS": f"{scorer.uas:.2f}",
"LAS": f"{scorer.las:.2f}",
"NER P": f"{scorer.ents_p:.2f}",
"NER R": f"{scorer.ents_r:.2f}",
"NER F": f"{scorer.ents_f:.2f}",
"Textcat AUC": f"{scorer.textcat_auc:.2f}",
"Textcat F": f"{scorer.textcat_f:.2f}",
"Sent P": f"{scorer.sent_p:.2f}",
"Sent R": f"{scorer.sent_r:.2f}",
"Sent F": f"{scorer.sent_f:.2f}",
}
msg.table(results, title="Results")
if displacy_path:
docs, golds = zip(*dev_docs)
docs = [ex.doc for ex in dev_dataset]
render_deps = "parser" in nlp.meta.get("pipeline", [])
render_ents = "ner" in nlp.meta.get("pipeline", [])
render_parses(
@ -79,7 +74,7 @@ def evaluate(
deps=render_deps,
ents=render_ents,
)
msg.good("Generated {} parses as HTML".format(displacy_limit), displacy_path)
msg.good(f"Generated {displacy_limit} parses as HTML", displacy_path)
if return_scores:
return scorer.scores

61
spacy/cli/info.py
View File

@ -1,44 +1,39 @@
# coding: utf8
from __future__ import unicode_literals
import plac
import platform
from pathlib import Path
from wasabi import msg
import srsly
from ..compat import path2str, basestring_, unicode_
from .validate import get_model_pkgs
from .. import util
from .. import about
@plac.annotations(
model=("Optional shortcut link of model", "positional", None, str),
markdown=("Generate Markdown for GitHub issues", "flag", "md", str),
silent=("Don't print anything (just return)", "flag", "s"),
)
def info(model=None, markdown=False, silent=False):
def info(
model: ("Optional model name", "positional", None, str) = None,
markdown: ("Generate Markdown for GitHub issues", "flag", "md", str) = False,
silent: ("Don't print anything (just return)", "flag", "s") = False,
):
"""
Print info about spaCy installation. If a model shortcut link is
speficied as an argument, print model information. Flag --markdown
prints details in Markdown for easy copy-pasting to GitHub issues.
Print info about spaCy installation. If a model is speficied as an argument,
print model information. Flag --markdown prints details in Markdown for easy
copy-pasting to GitHub issues.
"""
if model:
if util.is_package(model):
model_path = util.get_package_path(model)
else:
model_path = util.get_data_path() / model
model_path = model
meta_path = model_path / "meta.json"
if not meta_path.is_file():
msg.fail("Can't find model meta.json", meta_path, exits=1)
meta = srsly.read_json(meta_path)
if model_path.resolve() != model_path:
meta["link"] = path2str(model_path)
meta["source"] = path2str(model_path.resolve())
meta["link"] = str(model_path)
meta["source"] = str(model_path.resolve())
else:
meta["source"] = path2str(model_path)
meta["source"] = str(model_path)
if not silent:
title = "Info about model '{}'".format(model)
title = f"Info about model '{model}'"
model_meta = {
k: v for k, v in meta.items() if k not in ("accuracy", "speed")
}
@ -47,12 +42,15 @@ def info(model=None, markdown=False, silent=False):
else:
msg.table(model_meta, title=title)
return meta
all_models, _ = get_model_pkgs()
data = {
"spaCy version": about.__version__,
"Location": path2str(Path(__file__).parent.parent),
"Location": str(Path(__file__).parent.parent),
"Platform": platform.platform(),
"Python version": platform.python_version(),
"Models": list_models(),
"Models": ", ".join(
f"{m['name']} ({m['version']})" for m in all_models.values()
),
}
if not silent:
title = "Info about spaCy"
@ -63,30 +61,17 @@ def info(model=None, markdown=False, silent=False):
return data
def list_models():
def exclude_dir(dir_name):
# exclude common cache directories and hidden directories
exclude = ("cache", "pycache", "__pycache__")
return dir_name in exclude or dir_name.startswith(".")
data_path = util.get_data_path()
if data_path:
models = [f.parts[-1] for f in data_path.iterdir() if f.is_dir()]
return ", ".join([m for m in models if not exclude_dir(m)])
return "-"
def print_markdown(data, title=None):
"""Print data in GitHub-flavoured Markdown format for issues etc.
data (dict or list of tuples): Label/value pairs.
title (unicode or None): Title, will be rendered as headline 2.
title (str / None): Title, will be rendered as headline 2.
"""
markdown = []
for key, value in data.items():
if isinstance(value, basestring_) and Path(value).exists():
if isinstance(value, str) and Path(value).exists():
continue
markdown.append("* **{}:** {}".format(key, unicode_(value)))
markdown.append(f"* **{key}:** {value}")
if title:
print("\n## {}".format(title))
print(f"\n## {title}")
print("\n{}\n".format("\n".join(markdown)))

68
spacy/cli/init_model.py
View File

@ -1,7 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
import plac
import math
from tqdm import tqdm
import numpy
@ -20,7 +16,6 @@ from ..errors import Errors, Warnings
from ..util import ensure_path, get_lang_class, load_model, OOV_RANK
from ..lookups import Lookups
try:
import ftfy
except ImportError:
@ -30,43 +25,21 @@ except ImportError:
DEFAULT_OOV_PROB = -20
@plac.annotations(
lang=("Model language", "positional", None, str),
output_dir=("Model output directory", "positional", None, Path),
freqs_loc=("Location of words frequencies file", "option", "f", Path),
jsonl_loc=("Location of JSONL-formatted attributes file", "option", "j", Path),
clusters_loc=("Optional location of brown clusters data", "option", "c", str),
vectors_loc=("Optional vectors file in Word2Vec format", "option", "v", str),
truncate_vectors=(
"Optional number of vectors to truncate to when reading in vectors file",
"option",
"t",
int,
),
prune_vectors=("Optional number of vectors to prune to", "option", "V", int),
vectors_name=(
"Optional name for the word vectors, e.g. en_core_web_lg.vectors",
"option",
"vn",
str,
),
model_name=("Optional name for the model meta", "option", "mn", str),
omit_extra_lookups=("Don't include extra lookups in model", "flag", "OEL", bool),
base_model=("Base model (for languages with custom tokenizers)", "option", "b", str),
)
def init_model(
lang,
output_dir,
freqs_loc=None,
clusters_loc=None,
jsonl_loc=None,
vectors_loc=None,
truncate_vectors=0,
prune_vectors=-1,
vectors_name=None,
model_name=None,
omit_extra_lookups=False,
base_model=None,
# fmt: off
lang: ("Model language", "positional", None, str),
output_dir: ("Model output directory", "positional", None, Path),
freqs_loc: ("Location of words frequencies file", "option", "f", Path) = None,
clusters_loc: ("Optional location of brown clusters data", "option", "c", str) = None,
jsonl_loc: ("Location of JSONL-formatted attributes file", "option", "j", Path) = None,
vectors_loc: ("Optional vectors file in Word2Vec format", "option", "v", str) = None,
prune_vectors: ("Optional number of vectors to prune to", "option", "V", int) = -1,
truncate_vectors: ("Optional number of vectors to truncate to when reading in vectors file", "option", "t", int) = 0,
vectors_name: ("Optional name for the word vectors, e.g. en_core_web_lg.vectors", "option", "vn", str) = None,
model_name: ("Optional name for the model meta", "option", "mn", str) = None,
omit_extra_lookups: ("Don't include extra lookups in model", "flag", "OEL", bool) = False,
base_model: ("Base model (for languages with custom tokenizers)", "option", "b", str) = None
# fmt: on
):
"""
Create a new model from raw data, like word frequencies, Brown clusters
@ -114,8 +87,7 @@ def init_model(
vec_added = len(nlp.vocab.vectors)
lex_added = len(nlp.vocab)
msg.good(
"Sucessfully compiled vocab",
"{} entries, {} vectors".format(lex_added, vec_added),
"Sucessfully compiled vocab", f"{lex_added} entries, {vec_added} vectors",
)
if not output_dir.exists():
output_dir.mkdir()
@ -203,9 +175,9 @@ def add_vectors(nlp, vectors_loc, truncate_vectors, prune_vectors, name=None):
nlp.vocab.vectors.add(lex.orth, row=lex.rank)
else:
if vectors_loc:
with msg.loading("Reading vectors from {}".format(vectors_loc)):
vectors_data, vector_keys = read_vectors(vectors_loc, truncate_vectors)
msg.good("Loaded vectors from {}".format(vectors_loc))
with msg.loading(f"Reading vectors from {vectors_loc}"):
vectors_data, vector_keys = read_vectors(vectors_loc)
msg.good(f"Loaded vectors from {vectors_loc}")
else:
vectors_data, vector_keys = (None, None)
if vector_keys is not None:
@ -215,7 +187,7 @@ def add_vectors(nlp, vectors_loc, truncate_vectors, prune_vectors, name=None):
if vectors_data is not None:
nlp.vocab.vectors = Vectors(data=vectors_data, keys=vector_keys)
if name is None:
nlp.vocab.vectors.name = "%s_model.vectors" % nlp.meta["lang"]
nlp.vocab.vectors.name = f"{nlp.meta['lang']}_model.vectors"
else:
nlp.vocab.vectors.name = name
nlp.meta["vectors"]["name"] = nlp.vocab.vectors.name
@ -265,7 +237,7 @@ def read_freqs(freqs_loc, max_length=100, min_doc_freq=5, min_freq=50):
word = literal_eval(key)
except SyntaxError:
# Take odd strings literally.
word = literal_eval("'%s'" % key)
word = literal_eval(f"'{key}'")
smooth_count = counts.smoother(int(freq))
probs[word] = math.log(smooth_count) - log_total
oov_prob = math.log(counts.smoother(0)) - log_total

77
spacy/cli/link.py
View File

@ -1,77 +0,0 @@
# coding: utf8
from __future__ import unicode_literals
import plac
from pathlib import Path
from wasabi import msg
from ..compat import symlink_to, path2str
from .. import util
@plac.annotations(
origin=("package name or local path to model", "positional", None, str),
link_name=("name of shortuct link to create", "positional", None, str),
force=("force overwriting of existing link", "flag", "f", bool),
)
def link(origin, link_name, force=False, model_path=None):
"""
Create a symlink for models within the spacy/data directory. Accepts
either the name of a pip package, or the local path to the model data
directory. Linking models allows loading them via spacy.load(link_name).
"""
if util.is_package(origin):
model_path = util.get_package_path(origin)
else:
model_path = Path(origin) if model_path is None else Path(model_path)
if not model_path.exists():
msg.fail(
"Can't locate model data",
"The data should be located in {}".format(path2str(model_path)),
exits=1,
)
data_path = util.get_data_path()
if not data_path or not data_path.exists():
spacy_loc = Path(__file__).parent.parent
msg.fail(
"Can't find the spaCy data path to create model symlink",
"Make sure a directory `/data` exists within your spaCy "
"installation and try again. The data directory should be located "
"here:".format(path=spacy_loc),
exits=1,
)
link_path = util.get_data_path() / link_name
if link_path.is_symlink() and not force:
msg.fail(
"Link '{}' already exists".format(link_name),
"To overwrite an existing link, use the --force flag",
exits=1,
)
elif link_path.is_symlink(): # does a symlink exist?
# NB: It's important to check for is_symlink here and not for exists,
# because invalid/outdated symlinks would return False otherwise.
link_path.unlink()
elif link_path.exists(): # does it exist otherwise?
# NB: Check this last because valid symlinks also "exist".
msg.fail(
"Can't overwrite symlink '{}'".format(link_name),
"This can happen if your data directory contains a directory or "
"file of the same name.",
exits=1,
)
details = "%s --> %s" % (path2str(model_path), path2str(link_path))
try:
symlink_to(link_path, model_path)
except: # noqa: E722
# This is quite dirty, but just making sure other errors are caught.
msg.fail(
"Couldn't link model to '{}'".format(link_name),
"Creating a symlink in spacy/data failed. Make sure you have the "
"required permissions and try re-running the command as admin, or "
"use a virtualenv. You can still import the model as a module and "
"call its load() method, or create the symlink manually.",
)
msg.text(details)
raise
msg.good("Linking successful", details)
msg.text("You can now load the model via spacy.load('{}')".format(link_name))

44
spacy/cli/package.py
View File

@ -1,25 +1,21 @@
# coding: utf8
from __future__ import unicode_literals
import plac
import shutil
from pathlib import Path
from wasabi import msg, get_raw_input
import srsly
from ..compat import path2str
from .. import util
from .. import about
@plac.annotations(
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", "flag", "c", bool),
force=("Force overwriting existing model in output directory", "flag", "f", bool),
)
def package(input_dir, output_dir, meta_path=None, create_meta=False, force=False):
def package(
# fmt: off
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) = None,
create_meta: ("Create meta.json, even if one exists", "flag", "c", bool) = False,
force: ("Force overwriting existing model in output directory", "flag", "f", bool) = False,
# fmt: on
):
"""
Generate Python package for model data, including meta and required
installation files. A new directory will be created in the specified
@ -47,7 +43,7 @@ def package(input_dir, output_dir, meta_path=None, create_meta=False, force=Fals
for key in ("lang", "name", "version"):
if key not in meta or meta[key] == "":
msg.fail(
"No '{}' setting found in meta.json".format(key),
f"No '{key}' setting found in meta.json",
"This setting is required to build your package.",
exits=1,
)
@ -58,22 +54,21 @@ def package(input_dir, output_dir, meta_path=None, create_meta=False, force=Fals
if package_path.exists():
if force:
shutil.rmtree(path2str(package_path))
shutil.rmtree(str(package_path))
else:
msg.fail(
"Package directory already exists",
"Please delete the directory and try again, or use the "
"`--force` flag to overwrite existing "
"directories.".format(path=path2str(package_path)),
"`--force` flag to overwrite existing directories.",
exits=1,
)
Path.mkdir(package_path, parents=True)
shutil.copytree(path2str(input_path), path2str(package_path / model_name_v))
shutil.copytree(str(input_path), str(package_path / model_name_v))
create_file(main_path / "meta.json", srsly.json_dumps(meta, indent=2))
create_file(main_path / "setup.py", TEMPLATE_SETUP)
create_file(main_path / "MANIFEST.in", TEMPLATE_MANIFEST)
create_file(package_path / "__init__.py", TEMPLATE_INIT)
msg.good("Successfully created package '{}'".format(model_name_v), main_path)
msg.good(f"Successfully created package '{model_name_v}'", main_path)
msg.text("To build the package, run `python setup.py sdist` in this directory.")
@ -88,14 +83,14 @@ def generate_meta(model_path, existing_meta, msg):
("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")),
("license", "License", meta.get("license", "MIT")),
]
nlp = util.load_model_from_path(Path(model_path))
meta["spacy_version"] = util.get_model_version_range(about.__version__)
meta["pipeline"] = nlp.pipe_names
meta["vectors"] = {
"width": nlp.vocab.vectors_length,
@ -118,9 +113,6 @@ def generate_meta(model_path, existing_meta, msg):
TEMPLATE_SETUP = """
#!/usr/bin/env python
# coding: utf8
from __future__ import unicode_literals
import io
import json
from os import path, walk
@ -176,6 +168,7 @@ def setup_package():
package_data={model_name: list_files(model_dir)},
install_requires=list_requirements(meta),
zip_safe=False,
entry_points={'spacy_models': ['{m} = {m}'.format(m=model_name)]}
)
@ -190,9 +183,6 @@ include meta.json
TEMPLATE_INIT = """
# coding: utf8
from __future__ import unicode_literals
from pathlib import Path
from spacy.util import load_model_from_init_py, get_model_meta

288
spacy/cli/pretrain.py
View File

@ -1,107 +1,41 @@
# coding: utf8
from __future__ import print_function, unicode_literals
import plac
import random
import numpy
import time
import re
from collections import Counter
import plac
from pathlib import Path
from thinc.v2v import Affine, Maxout
from thinc.misc import LayerNorm as LN
from thinc.neural.util import prefer_gpu
from thinc.api import Linear, Maxout, chain, list2array, use_pytorch_for_gpu_memory
from wasabi import msg
import srsly
from ..errors import Errors
from ..ml.models.multi_task import build_masked_language_model
from ..tokens import Doc
from ..attrs import ID, HEAD
from .._ml import Tok2Vec, flatten, chain, create_default_optimizer
from .._ml import masked_language_model, get_cossim_loss
from .. import util
from .train import _load_pretrained_tok2vec
from ..gold import Example
@plac.annotations(
texts_loc=(
"Path to JSONL file with raw texts to learn from, with text provided as the key 'text' or tokens as the "
"key 'tokens'",
"positional",
None,
str,
),
vectors_model=("Name or path to spaCy model with vectors to learn from"),
output_dir=("Directory to write models to on each epoch", "positional", None, str),
width=("Width of CNN layers", "option", "cw", int),
conv_depth=("Depth of CNN layers", "option", "cd", int),
cnn_window=("Window size for CNN layers", "option", "cW", int),
cnn_pieces=("Maxout size for CNN layers. 1 for Mish", "option", "cP", int),
use_chars=("Whether to use character-based embedding", "flag", "chr", bool),
sa_depth=("Depth of self-attention layers", "option", "sa", int),
bilstm_depth=("Depth of BiLSTM layers (requires PyTorch)", "option", "lstm", int),
embed_rows=("Number of embedding rows", "option", "er", int),
loss_func=(
"Loss function to use for the objective. Either 'L2' or 'cosine'",
"option",
"L",
str,
),
use_vectors=("Whether to use the static vectors as input features", "flag", "uv"),
dropout=("Dropout rate", "option", "d", float),
batch_size=("Number of words per training batch", "option", "bs", int),
max_length=(
"Max words per example. Longer examples are discarded",
"option",
"xw",
int,
),
min_length=(
"Min words per example. Shorter examples are discarded",
"option",
"nw",
int,
),
seed=("Seed for random number generators", "option", "s", int),
n_iter=("Number of iterations to pretrain", "option", "i", int),
n_save_every=("Save model every X batches.", "option", "se", int),
init_tok2vec=(
"Path to pretrained weights for the token-to-vector parts of the models. See 'spacy pretrain'. Experimental.",
"option",
"t2v",
Path,
),
epoch_start=(
"The epoch to start counting at. Only relevant when using '--init-tok2vec' and the given weight file has been "
"renamed. Prevents unintended overwriting of existing weight files.",
"option",
"es",
int,
),
# fmt: off
texts_loc=("Path to JSONL file with raw texts to learn from, with text provided as the key 'text' or tokens as the key 'tokens'", "positional", None, str),
vectors_model=("Name or path to spaCy model with vectors to learn from", "positional", None, str),
output_dir=("Directory to write models to on each epoch", "positional", None, Path),
config_path=("Path to config file", "positional", None, Path),
use_gpu=("Use GPU", "option", "g", int),
resume_path=("Path to pretrained weights from which to resume pretraining", "option", "r", Path),
epoch_resume=("The epoch to resume counting from when using '--resume_path'. Prevents unintended overwriting of existing weight files.", "option", "er", int),
# fmt: on
)
def pretrain(
texts_loc,
vectors_model,
config_path,
output_dir,
width=96,
conv_depth=4,
bilstm_depth=0,
cnn_pieces=3,
sa_depth=0,
use_chars=False,
cnn_window=1,
embed_rows=2000,
loss_func="cosine",
use_vectors=False,
dropout=0.2,
n_iter=1000,
batch_size=3000,
max_length=500,
min_length=5,
seed=0,
n_save_every=None,
init_tok2vec=None,
epoch_start=None,
use_gpu=-1,
resume_path=None,
epoch_resume=None,
):
"""
Pre-train the 'token-to-vector' (tok2vec) layer of pipeline components,
@ -115,34 +49,46 @@ def pretrain(
However, it's still quite experimental, so your mileage may vary.
To load the weights back in during 'spacy train', you need to ensure
all settings are the same between pretraining and training. The API and
errors around this need some improvement.
all settings are the same between pretraining and training. Ideally,
this is done by using the same config file for both commands.
"""
config = dict(locals())
for key in config:
if isinstance(config[key], Path):
config[key] = str(config[key])
util.fix_random_seed(seed)
if not config_path or not config_path.exists():
msg.fail("Config file not found", config_path, exits=1)
has_gpu = prefer_gpu()
if has_gpu:
import torch
if use_gpu >= 0:
msg.info("Using GPU")
util.use_gpu(use_gpu)
else:
msg.info("Using CPU")
torch.set_default_tensor_type("torch.cuda.FloatTensor")
msg.info("Using GPU" if has_gpu else "Not using GPU")
msg.info(f"Loading config from: {config_path}")
config = util.load_config(config_path, create_objects=False)
util.fix_random_seed(config["pretraining"]["seed"])
if config["pretraining"]["use_pytorch_for_gpu_memory"]:
use_pytorch_for_gpu_memory()
output_dir = Path(output_dir)
if output_dir.exists() and [p for p in output_dir.iterdir()]:
msg.warn(
"Output directory is not empty",
"It is better to use an empty directory or refer to a new output path, "
"then the new directory will be created for you.",
)
if resume_path:
msg.warn(
"Output directory is not empty. ",
"If you're resuming a run from a previous model in this directory, "
"the old models for the consecutive epochs will be overwritten "
"with the new ones.",
)
else:
msg.warn(
"Output directory is not empty. ",
"It is better to use an empty directory or refer to a new output path, "
"then the new directory will be created for you.",
)
if not output_dir.exists():
output_dir.mkdir()
msg.good("Created output directory: {}".format(output_dir))
msg.good(f"Created output directory: {output_dir}")
srsly.write_json(output_dir / "config.json", config)
msg.good("Saved settings to config.json")
msg.good("Saved config file in the output directory")
config = util.load_config(config_path, create_objects=True)
pretrain_config = config["pretraining"]
# Load texts from file or stdin
if texts_loc != "-": # reading from a file
@ -156,64 +102,58 @@ def pretrain(
msg.good("Loaded input texts")
random.shuffle(texts)
else: # reading from stdin
msg.text("Reading input text from stdin...")
msg.info("Reading input text from stdin...")
texts = srsly.read_jsonl("-")
with msg.loading("Loading model '{}'...".format(vectors_model)):
with msg.loading(f"Loading model '{vectors_model}'..."):
nlp = util.load_model(vectors_model)
msg.good("Loaded model '{}'".format(vectors_model))
pretrained_vectors = None if not use_vectors else nlp.vocab.vectors.name
model = create_pretraining_model(
nlp,
Tok2Vec(
width,
embed_rows,
conv_depth=conv_depth,
pretrained_vectors=pretrained_vectors,
bilstm_depth=bilstm_depth, # Requires PyTorch. Experimental.
subword_features=not use_chars, # Set to False for Chinese etc
cnn_maxout_pieces=cnn_pieces, # If set to 1, use Mish activation.
),
)
# Load in pretrained weights
if init_tok2vec is not None:
components = _load_pretrained_tok2vec(nlp, init_tok2vec)
msg.text("Loaded pretrained tok2vec for: {}".format(components))
msg.good(f"Loaded model '{vectors_model}'")
tok2vec_path = pretrain_config["tok2vec_model"]
tok2vec = config
for subpath in tok2vec_path.split("."):
tok2vec = tok2vec.get(subpath)
model = create_pretraining_model(nlp, tok2vec)
optimizer = pretrain_config["optimizer"]
# Load in pretrained weights to resume from
if resume_path is not None:
msg.info(f"Resume training tok2vec from: {resume_path}")
with resume_path.open("rb") as file_:
weights_data = file_.read()
model.get_ref("tok2vec").from_bytes(weights_data)
# Parse the epoch number from the given weight file
model_name = re.search(r"model\d+\.bin", str(init_tok2vec))
model_name = re.search(r"model\d+\.bin", str(resume_path))
if model_name:
# Default weight file name so read epoch_start from it by cutting off 'model' and '.bin'
epoch_start = int(model_name.group(0)[5:][:-4]) + 1
epoch_resume = int(model_name.group(0)[5:][:-4]) + 1
msg.info(f"Resuming from epoch: {epoch_resume}")
else:
if not epoch_start:
if not epoch_resume:
msg.fail(
"You have to use the '--epoch-start' argument when using a renamed weight file for "
"'--init-tok2vec'",
"You have to use the --epoch-resume setting when using a renamed weight file for --resume-path",
exits=True,
)
elif epoch_start < 0:
elif epoch_resume < 0:
msg.fail(
"The argument '--epoch-start' has to be greater or equal to 0. '%d' is invalid"
% epoch_start,
f"The argument --epoch-resume has to be greater or equal to 0. {epoch_resume} is invalid",
exits=True,
)
else:
msg.info(f"Resuming from epoch: {epoch_resume}")
else:
# Without '--init-tok2vec' the '--epoch-start' argument is ignored
epoch_start = 0
# Without '--resume-path' the '--epoch-resume' argument is ignored
epoch_resume = 0
optimizer = create_default_optimizer(model.ops)
tracker = ProgressTracker(frequency=10000)
msg.divider("Pre-training tok2vec layer - starting at epoch %d" % epoch_start)
msg.divider(f"Pre-training tok2vec layer - starting at epoch {epoch_resume}")
row_settings = {"widths": (3, 10, 10, 6, 4), "aligns": ("r", "r", "r", "r", "r")}
msg.row(("#", "# Words", "Total Loss", "Loss", "w/s"), **row_settings)
def _save_model(epoch, is_temp=False):
is_temp_str = ".temp" if is_temp else ""
with model.use_params(optimizer.averages):
with (output_dir / ("model%d%s.bin" % (epoch, is_temp_str))).open(
"wb"
) as file_:
file_.write(model.tok2vec.to_bytes())
with (output_dir / f"model{epoch}{is_temp_str}.bin").open("wb") as file_:
file_.write(model.get_ref("tok2vec").to_bytes())
log = {
"nr_word": tracker.nr_word,
"loss": tracker.loss,
@ -224,26 +164,27 @@ def pretrain(
file_.write(srsly.json_dumps(log) + "\n")
skip_counter = 0
for epoch in range(epoch_start, n_iter + epoch_start):
for batch_id, batch in enumerate(
util.minibatch_by_words(((text, None) for text in texts), size=batch_size)
):
loss_func = pretrain_config["loss_func"]
for epoch in range(epoch_resume, pretrain_config["max_epochs"]):
examples = [Example(doc=text) for text in texts]
batches = util.minibatch_by_words(examples, size=pretrain_config["batch_size"])
for batch_id, batch in enumerate(batches):
docs, count = make_docs(
nlp,
[text for (text, _) in batch],
max_length=max_length,
min_length=min_length,
[ex.doc for ex in batch],
max_length=pretrain_config["max_length"],
min_length=pretrain_config["min_length"],
)
skip_counter += count
loss = make_update(
model, docs, optimizer, objective=loss_func, drop=dropout
)
loss = make_update(model, docs, optimizer, distance=loss_func)
progress = tracker.update(epoch, loss, docs)
if progress:
msg.row(progress, **row_settings)
if texts_loc == "-" and tracker.words_per_epoch[epoch] >= 10 ** 7:
break
if n_save_every and (batch_id % n_save_every == 0):
if pretrain_config["n_save_every"] and (
batch_id % pretrain_config["n_save_every"] == 0
):
_save_model(epoch, is_temp=True)
_save_model(epoch)
tracker.epoch_loss = 0.0
@ -251,21 +192,21 @@ def pretrain(
# Reshuffle the texts if texts were loaded from a file
random.shuffle(texts)
if skip_counter > 0:
msg.warn("Skipped {count} empty values".format(count=str(skip_counter)))
msg.warn(f"Skipped {skip_counter} empty values")
msg.good("Successfully finished pretrain")
def make_update(model, docs, optimizer, drop=0.0, objective="L2"):
def make_update(model, docs, optimizer, distance):
"""Perform an update over a single batch of documents.
docs (iterable): A batch of `Doc` objects.
drop (float): The dropout rate.
optimizer (callable): An optimizer.
RETURNS loss: A float for the loss.
"""
predictions, backprop = model.begin_update(docs, drop=drop)
loss, gradients = get_vectors_loss(model.ops, docs, predictions, objective)
backprop(gradients, sgd=optimizer)
predictions, backprop = model.begin_update(docs)
loss, gradients = get_vectors_loss(model.ops, docs, predictions, distance)
backprop(gradients)
model.finish_update(optimizer)
# Don't want to return a cupy object here
# The gradients are modified in-place by the BERT MLM,
# so we get an accurate loss
@ -297,12 +238,12 @@ def make_docs(nlp, batch, min_length, max_length):
heads = numpy.asarray(heads, dtype="uint64")
heads = heads.reshape((len(doc), 1))
doc = doc.from_array([HEAD], heads)
if len(doc) >= min_length and len(doc) < max_length:
if min_length <= len(doc) < max_length:
docs.append(doc)
return docs, skip_count
def get_vectors_loss(ops, docs, prediction, objective="L2"):
def get_vectors_loss(ops, docs, prediction, distance):
"""Compute a mean-squared error loss between the documents' vectors and
the prediction.
@ -316,13 +257,7 @@ def get_vectors_loss(ops, docs, prediction, objective="L2"):
# and look them up all at once. This prevents data copying.
ids = ops.flatten([doc.to_array(ID).ravel() for doc in docs])
target = docs[0].vocab.vectors.data[ids]
if objective == "L2":
d_target = prediction - target
loss = (d_target ** 2).sum()
elif objective == "cosine":
loss, d_target = get_cossim_loss(prediction, target)
else:
raise ValueError(Errors.E142.format(loss_func=objective))
d_target, loss = distance(prediction, target)
return loss, d_target
@ -331,22 +266,21 @@ def create_pretraining_model(nlp, tok2vec):
the tok2vec input model. The tok2vec input model needs to be a model that
takes a batch of Doc objects (as a list), and returns a list of arrays.
Each array in the output needs to have one row per token in the doc.
The actual tok2vec layer is stored as a reference, and only this bit will be
serialized to file and read back in when calling the 'train' command.
"""
output_size = nlp.vocab.vectors.data.shape[1]
output_layer = chain(
LN(Maxout(300, pieces=3)), Affine(output_size, drop_factor=0.0)
Maxout(nO=300, nP=3, normalize=True, dropout=0.0), Linear(output_size)
)
# This is annoying, but the parser etc have the flatten step after
# the tok2vec. To load the weights in cleanly, we need to match
# the shape of the models' components exactly. So what we cann
# "tok2vec" has to be the same set of processes as what the components do.
tok2vec = chain(tok2vec, flatten)
model = chain(tok2vec, output_layer)
model = masked_language_model(nlp.vocab, model)
model.tok2vec = tok2vec
model.output_layer = output_layer
model.begin_training([nlp.make_doc("Give it a doc to infer shapes")])
return model
model = chain(tok2vec, list2array())
model = chain(model, output_layer)
model.initialize(X=[nlp.make_doc("Give it a doc to infer shapes")])
mlm_model = build_masked_language_model(nlp.vocab, model)
mlm_model.set_ref("tok2vec", tok2vec)
mlm_model.set_ref("output_layer", output_layer)
mlm_model.initialize(X=[nlp.make_doc("Give it a doc to infer shapes")])
return mlm_model
class ProgressTracker(object):

29
spacy/cli/profile.py
View File

@ -1,7 +1,3 @@
# coding: utf8
from __future__ import unicode_literals, division, print_function
import plac
import tqdm
from pathlib import Path
import srsly
@ -9,18 +5,19 @@ import cProfile
import pstats
import sys
import itertools
import thinc.extra.datasets
import ml_datasets
from wasabi import msg
from ..util import load_model
@plac.annotations(
model=("Model to load", "positional", None, str),
inputs=("Location of input file. '-' for stdin.", "positional", None, str),
n_texts=("Maximum number of texts to use if available", "option", "n", int),
)
def profile(model, inputs=None, n_texts=10000):
def profile(
# fmt: off
model: ("Model to load", "positional", None, str),
inputs: ("Location of input file. '-' for stdin.", "positional", None, str) = None,
n_texts: ("Maximum number of texts to use if available", "option", "n", int) = 10000,
# fmt: on
):
"""
Profile a spaCy pipeline, to find out which functions take the most time.
Input should be formatted as one JSON object per line with a key "text".
@ -32,13 +29,13 @@ def profile(model, inputs=None, n_texts=10000):
if inputs is None:
n_inputs = 25000
with msg.loading("Loading IMDB dataset via Thinc..."):
imdb_train, _ = thinc.extra.datasets.imdb()
imdb_train, _ = ml_datasets.imdb()
inputs, _ = zip(*imdb_train)
msg.info("Loaded IMDB dataset and using {} examples".format(n_inputs))
msg.info(f"Loaded IMDB dataset and using {n_inputs} examples")
inputs = inputs[:n_inputs]
with msg.loading("Loading model '{}'...".format(model)):
with msg.loading(f"Loading model '{model}'..."):
nlp = load_model(model)
msg.good("Loaded model '{}'".format(model))
msg.good(f"Loaded model '{model}'")
texts = list(itertools.islice(inputs, n_texts))
cProfile.runctx("parse_texts(nlp, texts)", globals(), locals(), "Profile.prof")
s = pstats.Stats("Profile.prof")
@ -60,7 +57,7 @@ def _read_inputs(loc, msg):
input_path = Path(loc)
if not input_path.exists() or not input_path.is_file():
msg.fail("Not a valid input data file", loc, exits=1)
msg.info("Using data from {}".format(input_path.parts[-1]))
msg.info(f"Using data from {input_path.parts[-1]}")
file_ = input_path.open()
for line in file_:
data = srsly.json_loads(line)

770
spacy/cli/train.py
View File

@ -1,770 +0,0 @@
# coding: utf8
from __future__ import unicode_literals, division, print_function
import plac
import os
import tqdm
from pathlib import Path
from thinc.neural._classes.model import Model
from timeit import default_timer as timer
import shutil
import srsly
from wasabi import msg
import contextlib
import random
from .._ml import create_default_optimizer
from ..util import use_gpu as set_gpu
from ..errors import Errors
from ..gold import GoldCorpus
from ..compat import path2str
from ..lookups import Lookups
from .. import util
from .. import about
@plac.annotations(
# fmt: off
lang=("Model language", "positional", None, str),
output_path=("Output directory to store model in", "positional", None, Path),
train_path=("Location of JSON-formatted training data", "positional", None, Path),
dev_path=("Location of JSON-formatted development data", "positional", None, Path),
raw_text=("Path to jsonl file with unlabelled text documents.", "option", "rt", Path),
base_model=("Name of model to update (optional)", "option", "b", str),
pipeline=("Comma-separated names of pipeline components", "option", "p", str),
replace_components=("Replace components from base model", "flag", "R", bool),
vectors=("Model to load vectors from", "option", "v", str),
width=("Width of CNN layers of Tok2Vec component", "option", "cw", int),
conv_depth=("Depth of CNN layers of Tok2Vec component", "option", "cd", int),
cnn_window=("Window size for CNN layers of Tok2Vec component", "option", "cW", int),
cnn_pieces=("Maxout size for CNN layers of Tok2Vec component. 1 for Mish", "option", "cP", int),
use_chars=("Whether to use character-based embedding of Tok2Vec component", "flag", "chr", bool),
bilstm_depth=("Depth of BiLSTM layers of Tok2Vec component (requires PyTorch)", "option", "lstm", int),
embed_rows=("Number of embedding rows of Tok2Vec component", "option", "er", int),
n_iter=("Number of iterations", "option", "n", int),
n_early_stopping=("Maximum number of training epochs without dev accuracy improvement", "option", "ne", int),
n_examples=("Number of examples", "option", "ns", int),
use_gpu=("Use GPU", "option", "g", int),
version=("Model version", "option", "V", str),
meta_path=("Optional path to meta.json to use as base.", "option", "m", Path),
init_tok2vec=("Path to pretrained weights for the token-to-vector parts of the models. See 'spacy pretrain'. Experimental.", "option", "t2v", Path),
parser_multitasks=("Side objectives for parser CNN, e.g. 'dep' or 'dep,tag'", "option", "pt", str),
entity_multitasks=("Side objectives for NER CNN, e.g. 'dep' or 'dep,tag'", "option", "et", str),
noise_level=("Amount of corruption for data augmentation", "option", "nl", float),
orth_variant_level=("Amount of orthography variation for data augmentation", "option", "ovl", float),
eval_beam_widths=("Beam widths to evaluate, e.g. 4,8", "option", "bw", str),
gold_preproc=("Use gold preprocessing", "flag", "G", bool),
learn_tokens=("Make parser learn gold-standard tokenization", "flag", "T", bool),
textcat_multilabel=("Textcat classes aren't mutually exclusive (multilabel)", "flag", "TML", bool),
textcat_arch=("Textcat model architecture", "option", "ta", str),
textcat_positive_label=("Textcat positive label for binary classes with two labels", "option", "tpl", str),
tag_map_path=("Location of JSON-formatted tag map", "option", "tm", Path),
omit_extra_lookups=("Don't include extra lookups in model", "flag", "OEL", bool),
verbose=("Display more information for debug", "flag", "VV", bool),
debug=("Run data diagnostics before training", "flag", "D", bool),
# fmt: on
)
def train(
lang,
output_path,
train_path,
dev_path,
raw_text=None,
base_model=None,
pipeline="tagger,parser,ner",
replace_components=False,
vectors=None,
width=96,
conv_depth=4,
cnn_window=1,
cnn_pieces=3,
use_chars=False,
bilstm_depth=0,
embed_rows=2000,
n_iter=30,
n_early_stopping=None,
n_examples=0,
use_gpu=-1,
version="0.0.0",
meta_path=None,
init_tok2vec=None,
parser_multitasks="",
entity_multitasks="",
noise_level=0.0,
orth_variant_level=0.0,
eval_beam_widths="",
gold_preproc=False,
learn_tokens=False,
textcat_multilabel=False,
textcat_arch="bow",
textcat_positive_label=None,
tag_map_path=None,
omit_extra_lookups=False,
verbose=False,
debug=False,
):
"""
Train or update a spaCy model. Requires data to be formatted in spaCy's
JSON format. To convert data from other formats, use the `spacy convert`
command.
"""
util.fix_random_seed()
util.set_env_log(verbose)
# Make sure all files and paths exists if they are needed
train_path = util.ensure_path(train_path)
dev_path = util.ensure_path(dev_path)
meta_path = util.ensure_path(meta_path)
output_path = util.ensure_path(output_path)
if raw_text is not None:
raw_text = list(srsly.read_jsonl(raw_text))
if not train_path or not train_path.exists():
msg.fail("Training data not found", train_path, exits=1)
if not dev_path or not dev_path.exists():
msg.fail("Development data not found", dev_path, exits=1)
if meta_path is not None and not meta_path.exists():
msg.fail("Can't find model meta.json", meta_path, exits=1)
meta = srsly.read_json(meta_path) if meta_path else {}
if output_path.exists() and [p for p in output_path.iterdir() if p.is_dir()]:
msg.warn(
"Output directory is not empty",
"This can lead to unintended side effects when saving the model. "
"Please use an empty directory or a different path instead. If "
"the specified output path doesn't exist, the directory will be "
"created for you.",
)
if not output_path.exists():
output_path.mkdir()
msg.good("Created output directory: {}".format(output_path))
tag_map = {}
if tag_map_path is not None:
tag_map = srsly.read_json(tag_map_path)
# Take dropout and batch size as generators of values -- dropout
# starts high and decays sharply, to force the optimizer to explore.
# Batch size starts at 1 and grows, so that we make updates quickly
# at the beginning of training.
dropout_rates = util.decaying(
util.env_opt("dropout_from", 0.2),
util.env_opt("dropout_to", 0.2),
util.env_opt("dropout_decay", 0.0),
)
batch_sizes = util.compounding(
util.env_opt("batch_from", 100.0),
util.env_opt("batch_to", 1000.0),
util.env_opt("batch_compound", 1.001),
)
if not eval_beam_widths:
eval_beam_widths = [1]
else:
eval_beam_widths = [int(bw) for bw in eval_beam_widths.split(",")]
if 1 not in eval_beam_widths:
eval_beam_widths.append(1)
eval_beam_widths.sort()
has_beam_widths = eval_beam_widths != [1]
# Set up the base model and pipeline. If a base model is specified, load
# the model and make sure the pipeline matches the pipeline setting. If
# training starts from a blank model, intitalize the language class.
pipeline = [p.strip() for p in pipeline.split(",")]
disabled_pipes = None
pipes_added = False
msg.text("Training pipeline: {}".format(pipeline))
if use_gpu >= 0:
activated_gpu = None
try:
activated_gpu = set_gpu(use_gpu)
except Exception as e:
msg.warn("Exception: {}".format(e))
if activated_gpu is not None:
msg.text("Using GPU: {}".format(use_gpu))
else:
msg.warn("Unable to activate GPU: {}".format(use_gpu))
msg.text("Using CPU only")
use_gpu = -1
base_components = []
if base_model:
msg.text("Starting with base model '{}'".format(base_model))
nlp = util.load_model(base_model)
if nlp.lang != lang:
msg.fail(
"Model language ('{}') doesn't match language specified as "
"`lang` argument ('{}') ".format(nlp.lang, lang),
exits=1,
)
for pipe in pipeline:
pipe_cfg = {}
if pipe == "parser":
pipe_cfg = {"learn_tokens": learn_tokens}
elif pipe == "textcat":
pipe_cfg = {
"exclusive_classes": not textcat_multilabel,
"architecture": textcat_arch,
"positive_label": textcat_positive_label,
}
if pipe not in nlp.pipe_names:
msg.text("Adding component to base model '{}'".format(pipe))
nlp.add_pipe(nlp.create_pipe(pipe, config=pipe_cfg))
pipes_added = True
elif replace_components:
msg.text("Replacing component from base model '{}'".format(pipe))
nlp.replace_pipe(pipe, nlp.create_pipe(pipe, config=pipe_cfg))
pipes_added = True
else:
if pipe == "textcat":
textcat_cfg = nlp.get_pipe("textcat").cfg
base_cfg = {
"exclusive_classes": textcat_cfg["exclusive_classes"],
"architecture": textcat_cfg["architecture"],
"positive_label": textcat_cfg["positive_label"],
}
if base_cfg != pipe_cfg:
msg.fail(
"The base textcat model configuration does"
"not match the provided training options. "
"Existing cfg: {}, provided cfg: {}".format(
base_cfg, pipe_cfg
),
exits=1,
)
msg.text("Extending component from base model '{}'".format(pipe))
base_components.append(pipe)
disabled_pipes = nlp.disable_pipes(
[p for p in nlp.pipe_names if p not in pipeline]
)
else:
msg.text("Starting with blank model '{}'".format(lang))
lang_cls = util.get_lang_class(lang)
nlp = lang_cls()
for pipe in pipeline:
if pipe == "parser":
pipe_cfg = {"learn_tokens": learn_tokens}
elif pipe == "textcat":
pipe_cfg = {
"exclusive_classes": not textcat_multilabel,
"architecture": textcat_arch,
"positive_label": textcat_positive_label,
}
else:
pipe_cfg = {}
nlp.add_pipe(nlp.create_pipe(pipe, config=pipe_cfg))
# Update tag map with provided mapping
nlp.vocab.morphology.tag_map.update(tag_map)
# Create empty extra lexeme tables so the data from spacy-lookups-data
# isn't loaded if these features are accessed
if omit_extra_lookups:
nlp.vocab.lookups_extra = Lookups()
nlp.vocab.lookups_extra.add_table("lexeme_cluster")
nlp.vocab.lookups_extra.add_table("lexeme_prob")
nlp.vocab.lookups_extra.add_table("lexeme_settings")
if vectors:
msg.text("Loading vector from model '{}'".format(vectors))
_load_vectors(nlp, vectors)
# Multitask objectives
multitask_options = [("parser", parser_multitasks), ("ner", entity_multitasks)]
for pipe_name, multitasks in multitask_options:
if multitasks:
if pipe_name not in pipeline:
msg.fail(
"Can't use multitask objective without '{}' in the "
"pipeline".format(pipe_name)
)
pipe = nlp.get_pipe(pipe_name)
for objective in multitasks.split(","):
pipe.add_multitask_objective(objective)
# Prepare training corpus
msg.text("Counting training words (limit={})".format(n_examples))
corpus = GoldCorpus(train_path, dev_path, limit=n_examples)
n_train_words = corpus.count_train()
if base_model and not pipes_added:
# Start with an existing model, use default optimizer
optimizer = create_default_optimizer(Model.ops)
else:
# Start with a blank model, call begin_training
cfg = {"device": use_gpu}
cfg["conv_depth"] = conv_depth
cfg["token_vector_width"] = width
cfg["bilstm_depth"] = bilstm_depth
cfg["cnn_maxout_pieces"] = cnn_pieces
cfg["embed_size"] = embed_rows
cfg["conv_window"] = cnn_window
cfg["subword_features"] = not use_chars
optimizer = nlp.begin_training(lambda: corpus.train_tuples, **cfg)
nlp._optimizer = None
# Load in pretrained weights
if init_tok2vec is not None:
components = _load_pretrained_tok2vec(nlp, init_tok2vec, base_components)
msg.text("Loaded pretrained tok2vec for: {}".format(components))
# Verify textcat config
if "textcat" in pipeline:
textcat_labels = nlp.get_pipe("textcat").cfg.get("labels", [])
if textcat_positive_label and textcat_positive_label not in textcat_labels:
msg.fail(
"The textcat_positive_label (tpl) '{}' does not match any "
"label in the training data.".format(textcat_positive_label),
exits=1,
)
if textcat_positive_label and len(textcat_labels) != 2:
msg.fail(
"A textcat_positive_label (tpl) '{}' was provided for training "
"data that does not appear to be a binary classification "
"problem with two labels.".format(textcat_positive_label),
exits=1,
)
train_docs = corpus.train_docs(
nlp,
noise_level=noise_level,
gold_preproc=gold_preproc,
max_length=0,
ignore_misaligned=True,
)
train_labels = set()
if textcat_multilabel:
multilabel_found = False
for text, gold in train_docs:
train_labels.update(gold.cats.keys())
if list(gold.cats.values()).count(1.0) != 1:
multilabel_found = True
if not multilabel_found and not base_model:
msg.warn(
"The textcat training instances look like they have "
"mutually-exclusive classes. Remove the flag "
"'--textcat-multilabel' to train a classifier with "
"mutually-exclusive classes."
)
if not textcat_multilabel:
for text, gold in train_docs:
train_labels.update(gold.cats.keys())
if list(gold.cats.values()).count(1.0) != 1 and not base_model:
msg.warn(
"Some textcat training instances do not have exactly "
"one positive label. Modifying training options to "
"include the flag '--textcat-multilabel' for classes "
"that are not mutually exclusive."
)
nlp.get_pipe("textcat").cfg["exclusive_classes"] = False
textcat_multilabel = True
break
if base_model and set(textcat_labels) != train_labels:
msg.fail(
"Cannot extend textcat model using data with different "
"labels. Base model labels: {}, training data labels: "
"{}.".format(textcat_labels, list(train_labels)),
exits=1,
)
if textcat_multilabel:
msg.text(
"Textcat evaluation score: ROC AUC score macro-averaged across "
"the labels '{}'".format(", ".join(textcat_labels))
)
elif textcat_positive_label and len(textcat_labels) == 2:
msg.text(
"Textcat evaluation score: F1-score for the "
"label '{}'".format(textcat_positive_label)
)
elif len(textcat_labels) > 1:
if len(textcat_labels) == 2:
msg.warn(
"If the textcat component is a binary classifier with "
"exclusive classes, provide '--textcat-positive-label' for "
"an evaluation on the positive class."
)
msg.text(
"Textcat evaluation score: F1-score macro-averaged across "
"the labels '{}'".format(", ".join(textcat_labels))
)
else:
msg.fail(
"Unsupported textcat configuration. Use `spacy debug-data` "
"for more information."
)
# fmt: off
row_head, output_stats = _configure_training_output(pipeline, use_gpu, has_beam_widths)
row_widths = [len(w) for w in row_head]
row_settings = {"widths": row_widths, "aligns": tuple(["r" for i in row_head]), "spacing": 2}
# fmt: on
print("")
msg.row(row_head, **row_settings)
msg.row(["-" * width for width in row_settings["widths"]], **row_settings)
try:
iter_since_best = 0
best_score = 0.0
for i in range(n_iter):
train_docs = corpus.train_docs(
nlp,
noise_level=noise_level,
orth_variant_level=orth_variant_level,
gold_preproc=gold_preproc,
max_length=0,
ignore_misaligned=True,
)
if raw_text:
random.shuffle(raw_text)
raw_batches = util.minibatch(
(nlp.make_doc(rt["text"]) for rt in raw_text), size=8
)
words_seen = 0
with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
losses = {}
for batch in util.minibatch_by_words(train_docs, size=batch_sizes):
if not batch:
continue
docs, golds = zip(*batch)
try:
nlp.update(
docs,
golds,
sgd=optimizer,
drop=next(dropout_rates),
losses=losses,
)
except ValueError as e:
err = "Error during training"
if init_tok2vec:
err += " Did you provide the same parameters during 'train' as during 'pretrain'?"
msg.fail(err, "Original error message: {}".format(e), exits=1)
if raw_text:
# If raw text is available, perform 'rehearsal' updates,
# which use unlabelled data to reduce overfitting.
raw_batch = list(next(raw_batches))
nlp.rehearse(raw_batch, sgd=optimizer, losses=losses)
if not int(os.environ.get("LOG_FRIENDLY", 0)):
pbar.update(sum(len(doc) for doc in docs))
words_seen += sum(len(doc) for doc in docs)
with nlp.use_params(optimizer.averages):
util.set_env_log(False)
epoch_model_path = output_path / ("model%d" % i)
nlp.to_disk(epoch_model_path)
nlp_loaded = util.load_model_from_path(epoch_model_path)
for beam_width in eval_beam_widths:
for name, component in nlp_loaded.pipeline:
if hasattr(component, "cfg"):
component.cfg["beam_width"] = beam_width
dev_docs = list(
corpus.dev_docs(
nlp_loaded,
gold_preproc=gold_preproc,
ignore_misaligned=True,
)
)
nwords = sum(len(doc_gold[0]) for doc_gold in dev_docs)
start_time = timer()
scorer = nlp_loaded.evaluate(dev_docs, verbose=verbose)
end_time = timer()
if use_gpu < 0:
gpu_wps = None
cpu_wps = nwords / (end_time - start_time)
else:
gpu_wps = nwords / (end_time - start_time)
# Only evaluate on CPU in the first iteration (for
# timing) if GPU is enabled
if i == 0:
with Model.use_device("cpu"):
nlp_loaded = util.load_model_from_path(epoch_model_path)
for name, component in nlp_loaded.pipeline:
if hasattr(component, "cfg"):
component.cfg["beam_width"] = beam_width
dev_docs = list(
corpus.dev_docs(
nlp_loaded,
gold_preproc=gold_preproc,
ignore_misaligned=True,
)
)
start_time = timer()
scorer = nlp_loaded.evaluate(dev_docs, verbose=verbose)
end_time = timer()
cpu_wps = nwords / (end_time - start_time)
acc_loc = output_path / ("model%d" % i) / "accuracy.json"
srsly.write_json(acc_loc, scorer.scores)
# Update model meta.json
meta["lang"] = nlp.lang
meta["pipeline"] = nlp.pipe_names
meta["spacy_version"] = ">=%s" % about.__version__
if beam_width == 1:
meta["speed"] = {
"nwords": nwords,
"cpu": cpu_wps,
"gpu": gpu_wps,
}
meta.setdefault("accuracy", {})
for component in nlp.pipe_names:
for metric in _get_metrics(component):
meta["accuracy"][metric] = scorer.scores[metric]
else:
meta.setdefault("beam_accuracy", {})
meta.setdefault("beam_speed", {})
for component in nlp.pipe_names:
for metric in _get_metrics(component):
meta["beam_accuracy"][metric] = scorer.scores[metric]
meta["beam_speed"][beam_width] = {
"nwords": nwords,
"cpu": cpu_wps,
"gpu": gpu_wps,
}
meta["vectors"] = {
"width": nlp.vocab.vectors_length,
"vectors": len(nlp.vocab.vectors),
"keys": nlp.vocab.vectors.n_keys,
"name": nlp.vocab.vectors.name,
}
meta.setdefault("name", "model%d" % i)
meta.setdefault("version", version)
meta["labels"] = nlp.meta["labels"]
meta_loc = output_path / ("model%d" % i) / "meta.json"
srsly.write_json(meta_loc, meta)
util.set_env_log(verbose)
progress = _get_progress(
i,
losses,
scorer.scores,
output_stats,
beam_width=beam_width if has_beam_widths else None,
cpu_wps=cpu_wps,
gpu_wps=gpu_wps,
)
if i == 0 and "textcat" in pipeline:
textcats_per_cat = scorer.scores.get("textcats_per_cat", {})
for cat, cat_score in textcats_per_cat.items():
if cat_score.get("roc_auc_score", 0) < 0:
msg.warn(
"Textcat ROC AUC score is undefined due to "
"only one value in label '{}'.".format(cat)
)
msg.row(progress, **row_settings)
# Early stopping
if n_early_stopping is not None:
current_score = _score_for_model(meta)
if current_score < best_score:
iter_since_best += 1
else:
iter_since_best = 0
best_score = current_score
if iter_since_best >= n_early_stopping:
msg.text(
"Early stopping, best iteration "
"is: {}".format(i - iter_since_best)
)
msg.text(
"Best score = {}; Final iteration "
"score = {}".format(best_score, current_score)
)
break
except Exception as e:
msg.warn(
"Aborting and saving the final best model. "
"Encountered exception: {}".format(e),
exits=1,
)
finally:
best_pipes = nlp.pipe_names
if disabled_pipes:
disabled_pipes.restore()
with nlp.use_params(optimizer.averages):
final_model_path = output_path / "model-final"
nlp.to_disk(final_model_path)
meta_loc = output_path / "model-final" / "meta.json"
final_meta = srsly.read_json(meta_loc)
final_meta.setdefault("accuracy", {})
final_meta["accuracy"].update(meta.get("accuracy", {}))
final_meta.setdefault("speed", {})
final_meta["speed"].setdefault("cpu", None)
final_meta["speed"].setdefault("gpu", None)
meta.setdefault("speed", {})
meta["speed"].setdefault("cpu", None)
meta["speed"].setdefault("gpu", None)
# combine cpu and gpu speeds with the base model speeds
if final_meta["speed"]["cpu"] and meta["speed"]["cpu"]:
speed = _get_total_speed(
[final_meta["speed"]["cpu"], meta["speed"]["cpu"]]
)
final_meta["speed"]["cpu"] = speed
if final_meta["speed"]["gpu"] and meta["speed"]["gpu"]:
speed = _get_total_speed(
[final_meta["speed"]["gpu"], meta["speed"]["gpu"]]
)
final_meta["speed"]["gpu"] = speed
# if there were no speeds to update, overwrite with meta
if (
final_meta["speed"]["cpu"] is None
and final_meta["speed"]["gpu"] is None
):
final_meta["speed"].update(meta["speed"])
# note: beam speeds are not combined with the base model
if has_beam_widths:
final_meta.setdefault("beam_accuracy", {})
final_meta["beam_accuracy"].update(meta.get("beam_accuracy", {}))
final_meta.setdefault("beam_speed", {})
final_meta["beam_speed"].update(meta.get("beam_speed", {}))
srsly.write_json(meta_loc, final_meta)
msg.good("Saved model to output directory", final_model_path)
with msg.loading("Creating best model..."):
best_model_path = _collate_best_model(final_meta, output_path, best_pipes)
msg.good("Created best model", best_model_path)
def _score_for_model(meta):
""" Returns mean score between tasks in pipeline that can be used for early stopping. """
mean_acc = list()
pipes = meta["pipeline"]
acc = meta["accuracy"]
if "tagger" in pipes:
mean_acc.append(acc["tags_acc"])
if "parser" in pipes:
mean_acc.append((acc["uas"] + acc["las"]) / 2)
if "ner" in pipes:
mean_acc.append((acc["ents_p"] + acc["ents_r"] + acc["ents_f"]) / 3)
if "textcat" in pipes:
mean_acc.append(acc["textcat_score"])
return sum(mean_acc) / len(mean_acc)
@contextlib.contextmanager
def _create_progress_bar(total):
if int(os.environ.get("LOG_FRIENDLY", 0)):
yield
else:
pbar = tqdm.tqdm(total=total, leave=False)
yield pbar
def _load_vectors(nlp, vectors):
util.load_model(vectors, vocab=nlp.vocab)
def _load_pretrained_tok2vec(nlp, loc, base_components):
"""Load pretrained weights for the 'token-to-vector' part of the component
models, which is typically a CNN. See 'spacy pretrain'. Experimental.
"""
with loc.open("rb") as file_:
weights_data = file_.read()
loaded = []
for name, component in nlp.pipeline:
if hasattr(component, "model") and hasattr(component.model, "tok2vec"):
if name in base_components:
raise ValueError(Errors.E200.format(component=name))
component.tok2vec.from_bytes(weights_data)
loaded.append(name)
return loaded
def _collate_best_model(meta, output_path, components):
bests = {}
meta.setdefault("accuracy", {})
for component in components:
bests[component] = _find_best(output_path, component)
best_dest = output_path / "model-best"
shutil.copytree(path2str(output_path / "model-final"), path2str(best_dest))
for component, best_component_src in bests.items():
shutil.rmtree(path2str(best_dest / component))
shutil.copytree(
path2str(best_component_src / component), path2str(best_dest / component)
)
accs = srsly.read_json(best_component_src / "accuracy.json")
for metric in _get_metrics(component):
meta["accuracy"][metric] = accs[metric]
srsly.write_json(best_dest / "meta.json", meta)
return best_dest
def _find_best(experiment_dir, component):
accuracies = []
for epoch_model in experiment_dir.iterdir():
if epoch_model.is_dir() and epoch_model.parts[-1] != "model-final":
accs = srsly.read_json(epoch_model / "accuracy.json")
scores = [accs.get(metric, 0.0) for metric in _get_metrics(component)]
# remove per_type dicts from score list for max() comparison
scores = [score for score in scores if isinstance(score, float)]
accuracies.append((scores, epoch_model))
if accuracies:
return max(accuracies)[1]
else:
return None
def _get_metrics(component):
if component == "parser":
return ("las", "uas", "las_per_type", "token_acc")
elif component == "tagger":
return ("tags_acc", "token_acc")
elif component == "ner":
return ("ents_f", "ents_p", "ents_r", "ents_per_type", "token_acc")
elif component == "textcat":
return ("textcat_score", "token_acc")
return ("token_acc",)
def _configure_training_output(pipeline, use_gpu, has_beam_widths):
row_head = ["Itn"]
output_stats = []
for pipe in pipeline:
if pipe == "tagger":
row_head.extend(["Tag Loss ", " Tag % "])
output_stats.extend(["tag_loss", "tags_acc"])
elif pipe == "parser":
row_head.extend(["Dep Loss ", " UAS ", " LAS "])
output_stats.extend(["dep_loss", "uas", "las"])
elif pipe == "ner":
row_head.extend(["NER Loss ", "NER P ", "NER R ", "NER F "])
output_stats.extend(["ner_loss", "ents_p", "ents_r", "ents_f"])
elif pipe == "textcat":
row_head.extend(["Textcat Loss", "Textcat"])
output_stats.extend(["textcat_loss", "textcat_score"])
row_head.extend(["Token %", "CPU WPS"])
output_stats.extend(["token_acc", "cpu_wps"])
if use_gpu >= 0:
row_head.extend(["GPU WPS"])
output_stats.extend(["gpu_wps"])
if has_beam_widths:
row_head.insert(1, "Beam W.")
return row_head, output_stats
def _get_progress(
itn, losses, dev_scores, output_stats, beam_width=None, cpu_wps=0.0, gpu_wps=0.0
):
scores = {}
for stat in output_stats:
scores[stat] = 0.0
scores["dep_loss"] = losses.get("parser", 0.0)
scores["ner_loss"] = losses.get("ner", 0.0)
scores["tag_loss"] = losses.get("tagger", 0.0)
scores["textcat_loss"] = losses.get("textcat", 0.0)
scores["cpu_wps"] = cpu_wps
scores["gpu_wps"] = gpu_wps or 0.0
scores.update(dev_scores)
formatted_scores = []
for stat in output_stats:
format_spec = "{:.3f}"
if stat.endswith("_wps"):
format_spec = "{:.0f}"
formatted_scores.append(format_spec.format(scores[stat]))
result = [itn + 1]
result.extend(formatted_scores)
if beam_width is not None:
result.insert(1, beam_width)
return result
def _get_total_speed(speeds):
seconds_per_word = 0.0
for words_per_second in speeds:
if words_per_second is None:
return None
seconds_per_word += 1.0 / words_per_second
return 1.0 / seconds_per_word

606
spacy/cli/train_from_config.py Normal file
View File

@ -0,0 +1,606 @@
from typing import Optional, Dict, List, Union, Sequence
from timeit import default_timer as timer
import srsly
from pydantic import BaseModel, FilePath
import tqdm
from pathlib import Path
from wasabi import msg
import thinc
import thinc.schedules
from thinc.api import Model, use_pytorch_for_gpu_memory
import random
from ..gold import GoldCorpus
from ..lookups import Lookups
from .. import util
from ..errors import Errors
# Don't remove - required to load the built-in architectures
from ..ml import models # noqa: F401
registry = util.registry
CONFIG_STR = """
[training]
patience = 10
eval_frequency = 10
dropout = 0.2
init_tok2vec = null
max_epochs = 100
orth_variant_level = 0.0
gold_preproc = false
max_length = 0
use_gpu = 0
scores = ["ents_p", "ents_r", "ents_f"]
score_weights = {"ents_f": 1.0}
limit = 0
[training.batch_size]
@schedules = "compounding.v1"
start = 100
stop = 1000
compound = 1.001
[optimizer]
@optimizers = "Adam.v1"
learn_rate = 0.001
beta1 = 0.9
beta2 = 0.999
[nlp]
lang = "en"
vectors = null
[nlp.pipeline.tok2vec]
factory = "tok2vec"
[nlp.pipeline.ner]
factory = "ner"
[nlp.pipeline.ner.model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 3
hidden_width = 64
maxout_pieces = 3
[nlp.pipeline.ner.model.tok2vec]
@architectures = "spacy.Tok2VecTensors.v1"
width = ${nlp.pipeline.tok2vec.model:width}
[nlp.pipeline.tok2vec.model]
@architectures = "spacy.HashEmbedCNN.v1"
pretrained_vectors = ${nlp:vectors}
width = 128
depth = 4
window_size = 1
embed_size = 10000
maxout_pieces = 3
subword_features = true
"""
class PipelineComponent(BaseModel):
factory: str
model: Model
class Config:
arbitrary_types_allowed = True
class ConfigSchema(BaseModel):
optimizer: Optional["Optimizer"]
class training(BaseModel):
patience: int = 10
eval_frequency: int = 100
dropout: float = 0.2
init_tok2vec: Optional[FilePath] = None
max_epochs: int = 100
orth_variant_level: float = 0.0
gold_preproc: bool = False
max_length: int = 0
use_gpu: int = 0
scores: List[str] = ["ents_p", "ents_r", "ents_f"]
score_weights: Dict[str, Union[int, float]] = {"ents_f": 1.0}
limit: int = 0
batch_size: Union[Sequence[int], int]
class nlp(BaseModel):
lang: str
vectors: Optional[str]
pipeline: Optional[Dict[str, PipelineComponent]]
class Config:
extra = "allow"
def train_cli(
# fmt: off
train_path: ("Location of JSON-formatted training data", "positional", None, Path),
dev_path: ("Location of JSON-formatted development data", "positional", None, Path),
config_path: ("Path to config file", "positional", None, Path),
output_path: ("Output directory to store model in", "option", "o", Path) = None,
init_tok2vec: ("Path to pretrained weights for the tok2vec components. See 'spacy pretrain'. Experimental.", "option", "t2v", Path) = None,
raw_text: ("Path to jsonl file with unlabelled text documents.", "option", "rt", Path) = None,
verbose: ("Display more information for debugging purposes", "flag", "VV", bool) = False,
use_gpu: ("Use GPU", "option", "g", int) = -1,
tag_map_path: ("Location of JSON-formatted tag map", "option", "tm", Path) = None,
omit_extra_lookups: ("Don't include extra lookups in model", "flag", "OEL", bool) = False,
# fmt: on
):
"""
Train or update a spaCy model. Requires data to be formatted in spaCy's
JSON format. To convert data from other formats, use the `spacy convert`
command.
"""
util.set_env_log(verbose)
# Make sure all files and paths exists if they are needed
if not config_path or not config_path.exists():
msg.fail("Config file not found", config_path, exits=1)
if not train_path or not train_path.exists():
msg.fail("Training data not found", train_path, exits=1)
if not dev_path or not dev_path.exists():
msg.fail("Development data not found", dev_path, exits=1)
if output_path is not None:
if not output_path.exists():
output_path.mkdir()
msg.good(f"Created output directory: {output_path}")
elif output_path.exists() and [p for p in output_path.iterdir() if p.is_dir()]:
msg.warn(
"Output directory is not empty.",
"This can lead to unintended side effects when saving the model. "
"Please use an empty directory or a different path instead. If "
"the specified output path doesn't exist, the directory will be "
"created for you.",
)
if raw_text is not None:
raw_text = list(srsly.read_jsonl(raw_text))
tag_map = {}
if tag_map_path is not None:
tag_map = srsly.read_json(tag_map_path)
weights_data = None
if init_tok2vec is not None:
if not init_tok2vec.exists():
msg.fail("Can't find pretrained tok2vec", init_tok2vec, exits=1)
with init_tok2vec.open("rb") as file_:
weights_data = file_.read()
if use_gpu >= 0:
msg.info("Using GPU: {use_gpu}")
util.use_gpu(use_gpu)
else:
msg.info("Using CPU")
train(
config_path,
{"train": train_path, "dev": dev_path},
output_path=output_path,
raw_text=raw_text,
tag_map=tag_map,
weights_data=weights_data,
omit_extra_lookups=omit_extra_lookups,
)
def train(
config_path,
data_paths,
raw_text=None,
output_path=None,
tag_map=None,
weights_data=None,
omit_extra_lookups=False,
):
msg.info(f"Loading config from: {config_path}")
# Read the config first without creating objects, to get to the original nlp_config
config = util.load_config(config_path, create_objects=False)
util.fix_random_seed(config["training"]["seed"])
if config["training"].get("use_pytorch_for_gpu_memory"):
# It feels kind of weird to not have a default for this.
use_pytorch_for_gpu_memory()
nlp_config = config["nlp"]
config = util.load_config(config_path, create_objects=True)
training = config["training"]
msg.info("Creating nlp from config")
nlp = util.load_model_from_config(nlp_config)
optimizer = training["optimizer"]
limit = training["limit"]
msg.info("Loading training corpus")
corpus = GoldCorpus(data_paths["train"], data_paths["dev"], limit=limit)
# verify textcat config
if "textcat" in nlp_config["pipeline"]:
textcat_labels = set(nlp.get_pipe("textcat").labels)
textcat_multilabel = not nlp_config["pipeline"]["textcat"]["model"][
"exclusive_classes"
]
# check whether the setting 'exclusive_classes' corresponds to the provided training data
if textcat_multilabel:
multilabel_found = False
for ex in corpus.train_examples:
cats = ex.doc_annotation.cats
textcat_labels.update(cats.keys())
if list(cats.values()).count(1.0) != 1:
multilabel_found = True
if not multilabel_found:
msg.warn(
"The textcat training instances look like they have "
"mutually exclusive classes. Set 'exclusive_classes' "
"to 'true' in the config to train a classifier with "
"mutually exclusive classes more accurately."
)
else:
for ex in corpus.train_examples:
cats = ex.doc_annotation.cats
textcat_labels.update(cats.keys())
if list(cats.values()).count(1.0) != 1:
msg.fail(
"Some textcat training instances do not have exactly "
"one positive label. Set 'exclusive_classes' "
"to 'false' in the config to train a classifier with classes "
"that are not mutually exclusive."
)
msg.info(
f"Initialized textcat component for {len(textcat_labels)} unique labels"
)
nlp.get_pipe("textcat").labels = tuple(textcat_labels)
# if 'positive_label' is provided: double check whether it's in the data and the task is binary
if nlp_config["pipeline"]["textcat"].get("positive_label", None):
textcat_labels = nlp.get_pipe("textcat").cfg.get("labels", [])
pos_label = nlp_config["pipeline"]["textcat"]["positive_label"]
if pos_label not in textcat_labels:
msg.fail(
f"The textcat's 'positive_label' config setting '{pos_label}' "
f"does not match any label in the training data.",
exits=1,
)
if len(textcat_labels) != 2:
msg.fail(
f"A textcat 'positive_label' '{pos_label}' was "
f"provided for training data that does not appear to be a "
f"binary classification problem with two labels.",
exits=1,
)
if training.get("resume", False):
msg.info("Resuming training")
nlp.resume_training()
else:
msg.info(f"Initializing the nlp pipeline: {nlp.pipe_names}")
nlp.begin_training(lambda: corpus.train_examples)
# Update tag map with provided mapping
nlp.vocab.morphology.tag_map.update(tag_map)
# Create empty extra lexeme tables so the data from spacy-lookups-data
# isn't loaded if these features are accessed
if omit_extra_lookups:
nlp.vocab.lookups_extra = Lookups()
nlp.vocab.lookups_extra.add_table("lexeme_cluster")
nlp.vocab.lookups_extra.add_table("lexeme_prob")
nlp.vocab.lookups_extra.add_table("lexeme_settings")
# Load a pretrained tok2vec model - cf. CLI command 'pretrain'
if weights_data is not None:
tok2vec_path = config.get("pretraining", {}).get("tok2vec_model", None)
if tok2vec_path is None:
msg.fail(
f"To use a pretrained tok2vec model, the config needs to specify which "
f"tok2vec layer to load in the setting [pretraining.tok2vec_model].",
exits=1,
)
tok2vec = config
for subpath in tok2vec_path.split("."):
tok2vec = tok2vec.get(subpath)
if not tok2vec:
msg.fail(
f"Could not locate the tok2vec model at {tok2vec_path}.", exits=1,
)
tok2vec.from_bytes(weights_data)
train_batches = create_train_batches(nlp, corpus, training)
evaluate = create_evaluation_callback(nlp, optimizer, corpus, training)
# Create iterator, which yields out info after each optimization step.
msg.info("Start training")
training_step_iterator = train_while_improving(
nlp,
optimizer,
train_batches,
evaluate,
dropout=training["dropout"],
accumulate_gradient=training["accumulate_gradient"],
patience=training.get("patience", 0),
max_steps=training.get("max_steps", 0),
eval_frequency=training["eval_frequency"],
raw_text=raw_text,
)
msg.info(f"Training. Initial learn rate: {optimizer.learn_rate}")
print_row = setup_printer(training, nlp)
try:
progress = tqdm.tqdm(total=training["eval_frequency"], leave=False)
for batch, info, is_best_checkpoint in training_step_iterator:
progress.update(1)
if is_best_checkpoint is not None:
progress.close()
print_row(info)
if is_best_checkpoint and output_path is not None:
update_meta(training, nlp, info)
nlp.to_disk(output_path / "model-best")
progress = tqdm.tqdm(total=training["eval_frequency"], leave=False)
# Clean up the objects to faciliate garbage collection.
for eg in batch:
eg.doc = None
eg.goldparse = None
eg.doc_annotation = None
eg.token_annotation = None
except Exception as e:
msg.warn(
f"Aborting and saving the final best model. "
f"Encountered exception: {str(e)}",
exits=1,
)
finally:
if output_path is not None:
final_model_path = output_path / "model-final"
if optimizer.averages:
with nlp.use_params(optimizer.averages):
nlp.to_disk(final_model_path)
else:
nlp.to_disk(final_model_path)
msg.good(f"Saved model to output directory {final_model_path}")
def create_train_batches(nlp, corpus, cfg):
epochs_todo = cfg.get("max_epochs", 0)
while True:
train_examples = list(
corpus.train_dataset(
nlp,
noise_level=0.0, # I think this is deprecated?
orth_variant_level=cfg["orth_variant_level"],
gold_preproc=cfg["gold_preproc"],
max_length=cfg["max_length"],
ignore_misaligned=True,
)
)
if len(train_examples) == 0:
raise ValueError(Errors.E988)
random.shuffle(train_examples)
batches = util.minibatch_by_words(
train_examples,
size=cfg["batch_size"],
discard_oversize=cfg["discard_oversize"],
)
# make sure the minibatch_by_words result is not empty, or we'll have an infinite training loop
try:
first = next(batches)
yield first
except StopIteration:
raise ValueError(Errors.E986)
for batch in batches:
yield batch
epochs_todo -= 1
# We intentionally compare exactly to 0 here, so that max_epochs < 1
# will not break.
if epochs_todo == 0:
break
def create_evaluation_callback(nlp, optimizer, corpus, cfg):
def evaluate():
dev_examples = list(
corpus.dev_dataset(
nlp, gold_preproc=cfg["gold_preproc"], ignore_misaligned=True
)
)
n_words = sum(len(ex.doc) for ex in dev_examples)
start_time = timer()
if optimizer.averages:
with nlp.use_params(optimizer.averages):
scorer = nlp.evaluate(dev_examples, batch_size=32)
else:
scorer = nlp.evaluate(dev_examples, batch_size=32)
end_time = timer()
wps = n_words / (end_time - start_time)
scores = scorer.scores
# Calculate a weighted sum based on score_weights for the main score
weights = cfg["score_weights"]
try:
weighted_score = sum(scores[s] * weights.get(s, 0.0) for s in weights)
except KeyError as e:
raise KeyError(
Errors.E983.format(
dict_name="score_weights", key=str(e), keys=list(scores.keys())
)
)
scores["speed"] = wps
return weighted_score, scores
return evaluate
def train_while_improving(
nlp,
optimizer,
train_data,
evaluate,
*,
dropout,
eval_frequency,
accumulate_gradient=1,
patience=0,
max_steps=0,
raw_text=None,
):
"""Train until an evaluation stops improving. Works as a generator,
with each iteration yielding a tuple `(batch, info, is_best_checkpoint)`,
where info is a dict, and is_best_checkpoint is in [True, False, None] --
None indicating that the iteration was not evaluated as a checkpoint.
The evaluation is conducted by calling the evaluate callback, which should
Positional arguments:
nlp: The spaCy pipeline to evaluate.
optimizer: The optimizer callable.
train_data (Iterable[Batch]): A generator of batches, with the training
data. Each batch should be a Sized[Tuple[Input, Annot]]. The training
data iterable needs to take care of iterating over the epochs and
shuffling.
evaluate (Callable[[], Tuple[float, Any]]): A callback to perform evaluation.
The callback should take no arguments and return a tuple
`(main_score, other_scores)`. The main_score should be a float where
higher is better. other_scores can be any object.
Every iteration, the function yields out a tuple with:
* batch: A zipped sequence of Tuple[Doc, GoldParse] pairs.
* info: A dict with various information about the last update (see below).
* is_best_checkpoint: A value in None, False, True, indicating whether this
was the best evaluation so far. You should use this to save the model
checkpoints during training. If None, evaluation was not conducted on
that iteration. False means evaluation was conducted, but a previous
evaluation was better.
The info dict provides the following information:
epoch (int): How many passes over the data have been completed.
step (int): How many steps have been completed.
score (float): The main score form the last evaluation.
other_scores: : The other scores from the last evaluation.
loss: The accumulated losses throughout training.
checkpoints: A list of previous results, where each result is a
(score, step, epoch) tuple.
"""
if isinstance(dropout, float):
dropouts = thinc.schedules.constant(dropout)
else:
dropouts = dropout
results = []
losses = {}
to_enable = [name for name, proc in nlp.pipeline if hasattr(proc, "model")]
if raw_text:
random.shuffle(raw_text)
raw_batches = util.minibatch(
(nlp.make_doc(rt["text"]) for rt in raw_text), size=8
)
for step, batch in enumerate(train_data):
dropout = next(dropouts)
with nlp.select_pipes(enable=to_enable):
for subbatch in subdivide_batch(batch, accumulate_gradient):
nlp.update(subbatch, drop=dropout, losses=losses, sgd=False)
if raw_text:
# If raw text is available, perform 'rehearsal' updates,
# which use unlabelled data to reduce overfitting.
raw_batch = list(next(raw_batches))
nlp.rehearse(raw_batch, sgd=optimizer, losses=losses)
for name, proc in nlp.pipeline:
if hasattr(proc, "model"):
proc.model.finish_update(optimizer)
optimizer.step_schedules()
if not (step % eval_frequency):
score, other_scores = evaluate()
results.append((score, step))
is_best_checkpoint = score == max(results)[0]
else:
score, other_scores = (None, None)
is_best_checkpoint = None
info = {
"step": step,
"score": score,
"other_scores": other_scores,
"losses": losses,
"checkpoints": results,
}
yield batch, info, is_best_checkpoint
if is_best_checkpoint is not None:
losses = {}
# Stop if no improvement in `patience` updates (if specified)
best_score, best_step = max(results)
if patience and (step - best_step) >= patience:
break
# Stop if we've exhausted our max steps (if specified)
if max_steps and (step * accumulate_gradient) >= max_steps:
break
def subdivide_batch(batch, accumulate_gradient):
batch = list(batch)
batch.sort(key=lambda eg: len(eg.doc))
sub_len = len(batch) // accumulate_gradient
start = 0
for i in range(accumulate_gradient):
subbatch = batch[start : start + sub_len]
if subbatch:
yield subbatch
start += len(subbatch)
subbatch = batch[start:]
if subbatch:
yield subbatch
def setup_printer(training, nlp):
score_cols = training["scores"]
score_widths = [max(len(col), 6) for col in score_cols]
loss_cols = [f"Loss {pipe}" for pipe in nlp.pipe_names]
loss_widths = [max(len(col), 8) for col in loss_cols]
table_header = ["#"] + loss_cols + score_cols + ["Score"]
table_header = [col.upper() for col in table_header]
table_widths = [6] + loss_widths + score_widths + [6]
table_aligns = ["r" for _ in table_widths]
msg.row(table_header, widths=table_widths)
msg.row(["-" * width for width in table_widths])
def print_row(info):
try:
losses = [
"{0:.2f}".format(float(info["losses"][pipe_name]))
for pipe_name in nlp.pipe_names
]
except KeyError as e:
raise KeyError(
Errors.E983.format(
dict_name="scores (losses)",
key=str(e),
keys=list(info["losses"].keys()),
)
)
try:
scores = [
"{0:.2f}".format(float(info["other_scores"][col])) for col in score_cols
]
except KeyError as e:
raise KeyError(
Errors.E983.format(
dict_name="scores (other)",
key=str(e),
keys=list(info["other_scores"].keys()),
)
)
data = (
[info["step"]] + losses + scores + ["{0:.2f}".format(float(info["score"]))]
)
msg.row(data, widths=table_widths, aligns=table_aligns)
return print_row
def update_meta(training, nlp, info):
score_cols = training["scores"]
nlp.meta["performance"] = {}
for metric in score_cols:
nlp.meta["performance"][metric] = info["other_scores"][metric]
for pipe_name in nlp.pipe_names:
nlp.meta["performance"][f"{pipe_name}_loss"] = info["losses"][pipe_name]

161
spacy/cli/validate.py
View File

@ -1,15 +1,11 @@
# coding: utf8
from __future__ import unicode_literals, print_function
from pathlib import Path
import sys
import requests
import srsly
from wasabi import msg
from ..compat import path2str
from ..util import get_data_path
from .. import about
from ..util import get_package_version, get_installed_models, get_base_version
from ..util import get_package_path, get_model_meta, is_compatible_version
def validate():
@ -17,51 +13,30 @@ def validate():
Validate that the currently installed version of spaCy is compatible
with the installed models. Should be run after `pip install -U spacy`.
"""
with msg.loading("Loading compatibility table..."):
r = requests.get(about.__compatibility__)
if r.status_code != 200:
msg.fail(
"Server error ({})".format(r.status_code),
"Couldn't fetch compatibility table.",
exits=1,
)
msg.good("Loaded compatibility table")
compat = r.json()["spacy"]
version = about.__version__
version = version.rsplit(".dev", 1)[0]
current_compat = compat.get(version)
model_pkgs, compat = get_model_pkgs()
spacy_version = get_base_version(about.__version__)
current_compat = compat.get(spacy_version, {})
if not current_compat:
msg.fail(
"Can't find spaCy v{} in compatibility table".format(version),
about.__compatibility__,
exits=1,
)
all_models = set()
for spacy_v, models in dict(compat).items():
all_models.update(models.keys())
for model, model_vs in models.items():
compat[spacy_v][model] = [reformat_version(v) for v in model_vs]
model_links = get_model_links(current_compat)
model_pkgs = get_model_pkgs(current_compat, all_models)
incompat_links = {l for l, d in model_links.items() if not d["compat"]}
msg.warn(f"No compatible models found for v{spacy_version} of spaCy")
incompat_models = {d["name"] for _, d in model_pkgs.items() if not d["compat"]}
incompat_models.update(
[d["name"] for _, d in model_links.items() if not d["compat"]]
)
na_models = [m for m in incompat_models if m not in current_compat]
update_models = [m for m in incompat_models if m in current_compat]
spacy_dir = Path(__file__).parent.parent
msg.divider("Installed models (spaCy v{})".format(about.__version__))
msg.info("spaCy installation: {}".format(path2str(spacy_dir)))
msg.divider(f"Installed models (spaCy v{about.__version__})")
msg.info(f"spaCy installation: {spacy_dir}")
if model_links or model_pkgs:
header = ("TYPE", "NAME", "MODEL", "VERSION", "")
if model_pkgs:
header = ("NAME", "SPACY", "VERSION", "")
rows = []
for name, data in model_pkgs.items():
rows.append(get_model_row(current_compat, name, data, msg))
for name, data in model_links.items():
rows.append(get_model_row(current_compat, name, data, msg, "link"))
if data["compat"]:
comp = msg.text("", color="green", icon="good", no_print=True)
version = msg.text(data["version"], color="green", no_print=True)
else:
version = msg.text(data["version"], color="red", no_print=True)
comp = f"--> {compat.get(data['name'], ['n/a'])[0]}"
rows.append((data["name"], data["spacy"], version, comp))
msg.table(rows, header=header)
else:
msg.text("No models found in your current environment.", exits=0)
@ -71,75 +46,51 @@ def validate():
cmd = "python -m spacy download {}"
print("\n".join([cmd.format(pkg) for pkg in update_models]) + "\n")
if na_models:
msg.text(
"The following models are not available for spaCy "
"v{}: {}".format(about.__version__, ", ".join(na_models))
msg.info(
f"The following models are custom spaCy models or not "
f"available for spaCy v{about.__version__}:",
", ".join(na_models),
)
if incompat_links:
msg.text(
"You may also want to overwrite the incompatible links using the "
"`python -m spacy link` command with `--force`, or remove them "
"from the data directory. "
"Data path: {path}".format(path=path2str(get_data_path()))
)
if incompat_models or incompat_links:
if incompat_models:
sys.exit(1)
def get_model_links(compat):
links = {}
data_path = get_data_path()
if data_path:
models = [p for p in data_path.iterdir() if is_model_path(p)]
for model in models:
meta_path = Path(model) / "meta.json"
if not meta_path.exists():
continue
meta = srsly.read_json(meta_path)
link = model.parts[-1]
name = meta["lang"] + "_" + meta["name"]
links[link] = {
"name": name,
"version": meta["version"],
"compat": is_compat(compat, name, meta["version"]),
}
return links
def get_model_pkgs(compat, all_models):
import pkg_resources
def get_model_pkgs():
with msg.loading("Loading compatibility table..."):
r = requests.get(about.__compatibility__)
if r.status_code != 200:
msg.fail(
f"Server error ({r.status_code})",
"Couldn't fetch compatibility table.",
exits=1,
)
msg.good("Loaded compatibility table")
compat = r.json()["spacy"]
all_models = set()
installed_models = get_installed_models()
for spacy_v, models in dict(compat).items():
all_models.update(models.keys())
for model, model_vs in models.items():
compat[spacy_v][model] = [reformat_version(v) for v in model_vs]
pkgs = {}
for pkg_name, pkg_data in pkg_resources.working_set.by_key.items():
for pkg_name in installed_models:
package = pkg_name.replace("-", "_")
if package in all_models:
version = pkg_data.version
pkgs[pkg_name] = {
"name": package,
"version": version,
"compat": is_compat(compat, package, version),
}
return pkgs
def get_model_row(compat, name, data, msg, model_type="package"):
if data["compat"]:
comp = msg.text("", color="green", icon="good", no_print=True)
version = msg.text(data["version"], color="green", no_print=True)
else:
version = msg.text(data["version"], color="red", no_print=True)
comp = "--> {}".format(compat.get(data["name"], ["n/a"])[0])
return (model_type, name, data["name"], version, comp)
def is_model_path(model_path):
exclude = ["cache", "pycache", "__pycache__"]
name = model_path.parts[-1]
return model_path.is_dir() and name not in exclude and not name.startswith(".")
def is_compat(compat, name, version):
return name in compat and version in compat[name]
version = get_package_version(pkg_name)
if package in compat:
is_compat = version in compat[package]
spacy_version = about.__version__
else:
model_path = get_package_path(package)
model_meta = get_model_meta(model_path)
spacy_version = model_meta.get("spacy_version", "n/a")
is_compat = is_compatible_version(about.__version__, spacy_version)
pkgs[pkg_name] = {
"name": package,
"version": version,
"spacy": spacy_version,
"compat": is_compat,
}
return pkgs, compat
def reformat_version(version):

129
spacy/compat.py
View File

@ -1,4 +1,3 @@
# coding: utf8
"""
Helpers for Python and platform compatibility. To distinguish them from
the builtin functions, replacement functions are suffixed with an underscore,
@ -6,15 +5,9 @@ e.g. `unicode_`.
DOCS: https://spacy.io/api/top-level#compat
"""
from __future__ import unicode_literals
import os
import sys
import itertools
import ast
import types
from thinc.neural.util import copy_array
from thinc.util import copy_array
try:
import cPickle as pickle
@ -36,91 +29,23 @@ try:
except ImportError:
cupy = None
try:
from thinc.neural.optimizers import Optimizer # noqa: F401
except ImportError:
from thinc.neural.optimizers import Adam as Optimizer # noqa: F401
from thinc.api import Optimizer # noqa: F401
pickle = pickle
copy_reg = copy_reg
CudaStream = CudaStream
cupy = cupy
copy_array = copy_array
izip = getattr(itertools, "izip", zip)
is_windows = sys.platform.startswith("win")
is_linux = sys.platform.startswith("linux")
is_osx = sys.platform == "darwin"
# See: https://github.com/benjaminp/six/blob/master/six.py
is_python2 = sys.version_info[0] == 2
is_python3 = sys.version_info[0] == 3
is_python_pre_3_5 = is_python2 or (is_python3 and sys.version_info[1] < 5)
if is_python2:
bytes_ = str
unicode_ = unicode # noqa: F821
basestring_ = basestring # noqa: F821
input_ = raw_input # noqa: F821
path2str = lambda path: str(path).decode("utf8")
class_types = (type, types.ClassType)
elif is_python3:
bytes_ = bytes
unicode_ = str
basestring_ = str
input_ = input
path2str = lambda path: str(path)
class_types = (type, types.ClassType) if is_python_pre_3_5 else type
def b_to_str(b_str):
"""Convert a bytes object to a string.
b_str (bytes): The object to convert.
RETURNS (unicode): The converted string.
"""
if is_python2:
return b_str
# Important: if no encoding is set, string becomes "b'...'"
return str(b_str, encoding="utf8")
def symlink_to(orig, dest):
"""Create a symlink. Used for model shortcut links.
orig (unicode / Path): The origin path.
dest (unicode / Path): The destination path of the symlink.
"""
if is_windows:
import subprocess
subprocess.check_call(
["mklink", "/d", path2str(orig), path2str(dest)], shell=True
)
else:
orig.symlink_to(dest)
def symlink_remove(link):
"""Remove a symlink. Used for model shortcut links.
link (unicode / Path): The path to the symlink.
"""
# https://stackoverflow.com/q/26554135/6400719
if os.path.isdir(path2str(link)) and is_windows:
# this should only be on Py2.7 and windows
os.rmdir(path2str(link))
else:
os.unlink(path2str(link))
def is_config(python2=None, python3=None, windows=None, linux=None, osx=None):
def is_config(windows=None, linux=None, osx=None, **kwargs):
"""Check if a specific configuration of Python version and operating system
matches the user's setup. Mostly used to display targeted error messages.
python2 (bool): spaCy is executed with Python 2.x.
python3 (bool): spaCy is executed with Python 3.x.
windows (bool): spaCy is executed on Windows.
linux (bool): spaCy is executed on Linux.
osx (bool): spaCy is executed on OS X or macOS.
@ -129,53 +54,7 @@ def is_config(python2=None, python3=None, windows=None, linux=None, osx=None):
DOCS: https://spacy.io/api/top-level#compat.is_config
"""
return (
python2 in (None, is_python2)
and python3 in (None, is_python3)
and windows in (None, is_windows)
windows in (None, is_windows)
and linux in (None, is_linux)
and osx in (None, is_osx)
)
def import_file(name, loc):
"""Import module from a file. Used to load models from a directory.
name (unicode): Name of module to load.
loc (unicode / Path): Path to the file.
RETURNS: The loaded module.
"""
loc = path2str(loc)
if is_python_pre_3_5:
import imp
return imp.load_source(name, loc)
else:
import importlib.util
spec = importlib.util.spec_from_file_location(name, str(loc))
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
return module
def unescape_unicode(string):
"""Python2.7's re module chokes when compiling patterns that have ranges
between escaped unicode codepoints if the two codepoints are unrecognised
in the unicode database. For instance:
re.compile('[\\uAA77-\\uAA79]').findall("hello")
Ends up matching every character (on Python 2). This problem doesn't occur
if we're dealing with unicode literals.
"""
if string is None:
return string
# We only want to unescape the unicode, so we first must protect the other
# backslashes.
string = string.replace("\\", "\\\\")
# Now we remove that protection for the unicode.
string = string.replace("\\\\u", "\\u")
string = string.replace("\\\\U", "\\U")
# Now we unescape by evaling the string with the AST. This can't execute
# code -- it only does the representational level.
return ast.literal_eval("u'''" + string + "'''")

0
spacy/data/__init__.py
View File

22
spacy/displacy/__init__.py
View File

@ -1,17 +1,13 @@
# coding: utf8
"""
spaCy's built in visualization suite for dependencies and named entities.
DOCS: https://spacy.io/api/top-level#displacy
USAGE: https://spacy.io/usage/visualizers
"""
from __future__ import unicode_literals
import warnings
from .render import DependencyRenderer, EntityRenderer
from ..tokens import Doc, Span
from ..compat import b_to_str
from ..errors import Errors, Warnings
from ..util import is_in_jupyter
@ -26,13 +22,13 @@ def render(
"""Render displaCy visualisation.
docs (list or Doc): Document(s) to visualise.
style (unicode): Visualisation style, 'dep' or 'ent'.
style (str): Visualisation style, 'dep' or 'ent'.
page (bool): Render markup as full HTML page.
minify (bool): Minify HTML markup.
jupyter (bool): Override Jupyter auto-detection.
options (dict): Visualiser-specific options, e.g. colors.
manual (bool): Don't parse `Doc` and instead expect a dict/list of dicts.
RETURNS (unicode): Rendered HTML markup.
RETURNS (str): Rendered HTML markup.
DOCS: https://spacy.io/api/top-level#displacy.render
USAGE: https://spacy.io/usage/visualizers
@ -77,13 +73,13 @@ def serve(
"""Serve displaCy visualisation.
docs (list or Doc): Document(s) to visualise.
style (unicode): Visualisation style, 'dep' or 'ent'.
style (str): Visualisation style, 'dep' or 'ent'.
page (bool): Render markup as full HTML page.
minify (bool): Minify HTML markup.
options (dict): Visualiser-specific options, e.g. colors.
manual (bool): Don't parse `Doc` and instead expect a dict/list of dicts.
port (int): Port to serve visualisation.
host (unicode): Host to serve visualisation.
host (str): Host to serve visualisation.
DOCS: https://spacy.io/api/top-level#displacy.serve
USAGE: https://spacy.io/usage/visualizers
@ -95,20 +91,20 @@ def serve(
render(docs, style=style, page=page, minify=minify, options=options, manual=manual)
httpd = simple_server.make_server(host, port, app)
print("\nUsing the '{}' visualizer".format(style))
print("Serving on http://{}:{} ...\n".format(host, port))
print(f"\nUsing the '{style}' visualizer")
print(f"Serving on http://{host}:{port} ...\n")
try:
httpd.serve_forever()
except KeyboardInterrupt:
print("Shutting down server on port {}.".format(port))
print(f"Shutting down server on port {port}.")
finally:
httpd.server_close()
def app(environ, start_response):
# Headers and status need to be bytes in Python 2, see #1227
headers = [(b_to_str(b"Content-type"), b_to_str(b"text/html; charset=utf-8"))]
start_response(b_to_str(b"200 OK"), headers)
headers = [("Content-type", "text/html; charset=utf-8")]
start_response("200 OK", headers)
res = _html["parsed"].encode(encoding="utf-8")
return [res]

33
spacy/displacy/render.py
View File

@ -1,6 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
import uuid
from .templates import (
@ -50,7 +47,7 @@ class DependencyRenderer(object):
parsed (list): Dependency parses to render.
page (bool): Render parses wrapped as full HTML page.
minify (bool): Minify HTML markup.
RETURNS (unicode): Rendered SVG or HTML markup.
RETURNS (str): Rendered SVG or HTML markup.
"""
# Create a random ID prefix to make sure parses don't receive the
# same ID, even if they're identical
@ -61,7 +58,7 @@ class DependencyRenderer(object):
settings = p.get("settings", {})
self.direction = settings.get("direction", DEFAULT_DIR)
self.lang = settings.get("lang", DEFAULT_LANG)
render_id = "{}-{}".format(id_prefix, i)
render_id = f"{id_prefix}-{i}"
svg = self.render_svg(render_id, p["words"], p["arcs"])
rendered.append(svg)
if page:
@ -81,7 +78,7 @@ class DependencyRenderer(object):
render_id (int): Unique ID, typically index of document.
words (list): Individual words and their tags.
arcs (list): Individual arcs and their start, end, direction and label.
RETURNS (unicode): Rendered SVG markup.
RETURNS (str): Rendered SVG markup.
"""
self.levels = self.get_levels(arcs)
self.highest_level = len(self.levels)
@ -115,10 +112,10 @@ class DependencyRenderer(object):
):
"""Render individual word.
text (unicode): Word text.
tag (unicode): Part-of-speech tag.
text (str): Word text.
tag (str): Part-of-speech tag.
i (int): Unique ID, typically word index.
RETURNS (unicode): Rendered SVG markup.
RETURNS (str): Rendered SVG markup.
"""
y = self.offset_y + self.word_spacing
x = self.offset_x + i * self.distance
@ -134,12 +131,12 @@ class DependencyRenderer(object):
def render_arrow(self, label, start, end, direction, i):
"""Render individual arrow.
label (unicode): Dependency label.
label (str): Dependency label.
start (int): Index of start word.
end (int): Index of end word.
direction (unicode): Arrow direction, 'left' or 'right'.
direction (str): Arrow direction, 'left' or 'right'.
i (int): Unique ID, typically arrow index.
RETURNS (unicode): Rendered SVG markup.
RETURNS (str): Rendered SVG markup.
"""
if start < 0 or end < 0:
error_args = dict(start=start, end=end, label=label, dir=direction)
@ -182,7 +179,7 @@ class DependencyRenderer(object):
y (int): Y-coordinate of arrow start and end point.
y_curve (int): Y-corrdinate of Cubic Bézier y_curve point.
x_end (int): X-coordinate of arrow end point.
RETURNS (unicode): Definition of the arc path ('d' attribute).
RETURNS (str): Definition of the arc path ('d' attribute).
"""
template = "M{x},{y} C{x},{c} {e},{c} {e},{y}"
if self.compact:
@ -192,11 +189,11 @@ class DependencyRenderer(object):
def get_arrowhead(self, direction, x, y, end):
"""Render individual arrow head.
direction (unicode): Arrow direction, 'left' or 'right'.
direction (str): Arrow direction, 'left' or 'right'.
x (int): X-coordinate of arrow start point.
y (int): Y-coordinate of arrow start and end point.
end (int): X-coordinate of arrow end point.
RETURNS (unicode): Definition of the arrow head path ('d' attribute).
RETURNS (str): Definition of the arrow head path ('d' attribute).
"""
if direction == "left":
pos1, pos2, pos3 = (x, x - self.arrow_width + 2, x + self.arrow_width - 2)
@ -282,7 +279,7 @@ class EntityRenderer(object):
parsed (list): Dependency parses to render.
page (bool): Render parses wrapped as full HTML page.
minify (bool): Minify HTML markup.
RETURNS (unicode): Rendered HTML markup.
RETURNS (str): Rendered HTML markup.
"""
rendered = []
for i, p in enumerate(parsed):
@ -303,9 +300,9 @@ class EntityRenderer(object):
def render_ents(self, text, spans, title):
"""Render entities in text.
text (unicode): Original text.
text (str): Original text.
spans (list): Individual entity spans and their start, end and label.
title (unicode or None): Document title set in Doc.user_data['title'].
title (str / None): Document title set in Doc.user_data['title'].
"""
markup = ""
offset = 0

4
spacy/displacy/templates.py
View File

@ -1,7 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
# Setting explicit height and max-width: none on the SVG is required for
# Jupyter to render it properly in a cell

106
spacy/errors.py
View File

@ -1,7 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
def add_codes(err_cls):
"""Add error codes to string messages via class attribute names."""
@ -93,7 +89,7 @@ class Warnings(object):
"lemmatization rules or data. This means that the trained model "
"may not be able to lemmatize correctly. If this is intentional "
"or the language you're using doesn't have lemmatization data, "
"please ignore this warning. If this is surprising, make sure you "
"you can ignore this warning. If this is surprising, make sure you "
"have the spacy-lookups-data package installed.")
W023 = ("Multiprocessing of Language.pipe is not supported in Python 2. "
"'n_process' will be set to 1.")
@ -135,6 +131,31 @@ class Warnings(object):
"package installed. The languages with lexeme normalization tables "
"are currently: da, de, el, en, id, lb, pt, ru, sr, ta, th.")
# TODO: fix numbering after merging develop into master
W094 = ("Model '{model}' ({model_version}) specifies an under-constrained "
"spaCy version requirement: {version}. This can lead to compatibility "
"problems with older versions, or as new spaCy versions are "
"released, because the model may say it's compatible when it's "
'not. Consider changing the "spacy_version" in your meta.json to a '
"version range, with a lower and upper pin. For example: {example}")
W095 = ("Model '{model}' ({model_version}) requires spaCy {version} and is "
"incompatible with the current version ({current}). This may lead "
"to unexpected results or runtime errors. To resolve this, "
"download a newer compatible model or retrain your custom model "
"with the current spaCy version. For more details and available "
"updates, run: python -m spacy validate")
W096 = ("The method 'disable_pipes' has become deprecated - use 'select_pipes' "
"instead.")
W097 = ("No Model config was provided to create the '{name}' component, "
"and no default configuration could be found either.")
W098 = ("No Model config was provided to create the '{name}' component, "
"so a default configuration was used.")
W099 = ("Expected 'dict' type for the 'model' argument of pipe '{pipe}', "
"but got '{type}' instead, so ignoring it.")
W100 = ("Skipping unsupported morphological feature(s): {feature}. "
"Provide features as a dict {{\"Field1\": \"Value1,Value2\"}} or "
"string \"Field1=Value1,Value2|Field2=Value3\".")
@add_codes
class Errors(object):
@ -156,7 +177,7 @@ class Errors(object):
E007 = ("'{name}' already exists in pipeline. Existing names: {opts}")
E008 = ("Some current components would be lost when restoring previous "
"pipeline state. If you added components after calling "
"`nlp.disable_pipes()`, you should remove them explicitly with "
"`nlp.select_pipes()`, you should remove them explicitly with "
"`nlp.remove_pipe()` before the pipeline is restored. Names of "
"the new components: {names}")
E009 = ("The `update` method expects same number of docs and golds, but "
@ -217,7 +238,7 @@ class Errors(object):
"the documentation:\nhttps://spacy.io/usage/models")
E030 = ("Sentence boundaries unset. You can add the 'sentencizer' "
"component to the pipeline with: "
"nlp.add_pipe(nlp.create_pipe('sentencizer')) "
"nlp.add_pipe(nlp.create_pipe('sentencizer')). "
"Alternatively, add the dependency parser, or set sentence "
"boundaries by setting doc[i].is_sent_start.")
E031 = ("Invalid token: empty string ('') at position {i}.")
@ -253,15 +274,10 @@ class Errors(object):
E047 = ("Can't assign a value to unregistered extension attribute "
"'{name}'. Did you forget to call the `set_extension` method?")
E048 = ("Can't import language {lang} from spacy.lang: {err}")
E049 = ("Can't find spaCy data directory: '{path}'. Check your "
"installation and permissions, or use spacy.util.set_data_path "
"to customise the location if necessary.")
E050 = ("Can't find model '{name}'. It doesn't seem to be a shortcut "
"link, a Python package or a valid path to a data directory.")
E051 = ("Cant' load '{name}'. If you're using a shortcut link, make sure "
"it points to a valid package (not just a data directory).")
E050 = ("Can't find model '{name}'. It doesn't seem to be a Python "
"package or a valid path to a data directory.")
E052 = ("Can't find model directory: {path}")
E053 = ("Could not read meta.json from {path}")
E053 = ("Could not read {name} from {path}")
E054 = ("No valid '{setting}' setting found in model meta.json.")
E055 = ("Invalid ORTH value in exception:\nKey: {key}\nOrths: {orths}")
E056 = ("Invalid tokenizer exception: ORTH values combined don't match "
@ -379,8 +395,8 @@ class Errors(object):
E108 = ("As of spaCy v2.1, the pipe name `sbd` has been deprecated "
"in favor of the pipe name `sentencizer`, which does the same "
"thing. For example, use `nlp.create_pipeline('sentencizer')`")
E109 = ("Model for component '{name}' not initialized. Did you forget to "
"load a model, or forget to call begin_training()?")
E109 = ("Component '{name}' could not be run. Did you forget to "
"call begin_training()?")
E110 = ("Invalid displaCy render wrapper. Expected callable, got: {obj}")
E111 = ("Pickling a token is not supported, because tokens are only views "
"of the parent Doc and can't exist on their own. A pickled token "
@ -450,8 +466,6 @@ class Errors(object):
E134 = ("Entity '{entity}' is not defined in the Knowledge Base.")
E135 = ("If you meant to replace a built-in component, use `create_pipe`: "
"`nlp.replace_pipe('{name}', nlp.create_pipe('{name}'))`")
E136 = ("This additional feature requires the jsonschema library to be "
"installed:\npip install jsonschema")
E137 = ("Expected 'dict' type, but got '{type}' from '{line}'. Make sure "
"to provide a valid JSON object as input with either the `text` "
"or `tokens` key. For more info, see the docs:\n"
@ -459,14 +473,11 @@ class Errors(object):
E138 = ("Invalid JSONL format for raw text '{text}'. Make sure the input "
"includes either the `text` or `tokens` key. For more info, see "
"the docs:\nhttps://spacy.io/api/cli#pretrain-jsonl")
E139 = ("Knowledge Base for component '{name}' not initialized. Did you "
"forget to call set_kb()?")
E139 = ("Knowledge Base for component '{name}' is empty.")
E140 = ("The list of entities, prior probabilities and entity vectors "
"should be of equal length.")
E141 = ("Entity vectors should be of length {required} instead of the "
"provided {found}.")
E142 = ("Unsupported loss_function '{loss_func}'. Use either 'L2' or "
"'cosine'.")
E143 = ("Labels for component '{name}' not initialized. Did you forget to "
"call add_label()?")
E144 = ("Could not find parameter `{param}` when building the entity "
@ -590,6 +601,47 @@ class Errors(object):
E200 = ("Specifying a base model with a pretrained component '{component}' "
"can not be combined with adding a pretrained Tok2Vec layer.")
# TODO: fix numbering after merging develop into master
E983 = ("Invalid key for '{dict_name}': {key}. Available keys: "
"{keys}")
E984 = ("Could not parse the {input} - double check the data is written "
"in the correct format as expected by spaCy.")
E985 = ("The pipeline component '{component}' is already available in the base "
"model. The settings in the component block in the config file are "
"being ignored. If you want to replace this component instead, set "
"'replace' to True in the training configuration.")
E986 = ("Could not create any training batches: check your input. "
"Perhaps discard_oversize should be set to False ?")
E987 = ("The text of an example training instance is either a Doc or "
"a string, but found {type} instead.")
E988 = ("Could not parse any training examples. Ensure the data is "
"formatted correctly.")
E989 = ("'nlp.update()' was called with two positional arguments. This "
"may be due to a backwards-incompatible change to the format "
"of the training data in spaCy 3.0 onwards. The 'update' "
"function should now be called with a batch of 'Example' "
"objects, instead of (text, annotation) tuples. ")
E990 = ("An entity linking component needs to be initialized with a "
"KnowledgeBase object, but found {type} instead.")
E991 = ("The function 'select_pipes' should be called with either a "
"'disable' argument to list the names of the pipe components "
"that should be disabled, or with an 'enable' argument that "
"specifies which pipes should not be disabled.")
E992 = ("The function `select_pipes` was called with `enable`={enable} "
"and `disable`={disable} but that information is conflicting "
"for the `nlp` pipeline with components {names}.")
E993 = ("The config for 'nlp' should include either a key 'name' to "
"refer to an existing model by name or path, or a key 'lang' "
"to create a new blank model.")
E996 = ("Could not parse {file}: {msg}")
E997 = ("Tokenizer special cases are not allowed to modify the text. "
"This would map '{chunk}' to '{orth}' given token attributes "
"'{token_attrs}'.")
E998 = ("To create GoldParse objects from Example objects without a "
"Doc, get_gold_parses() should be called with a Vocab object.")
E999 = ("Encountered an unexpected format for the dictionary holding "
"gold annotations: {gold_dict}")
@add_codes
class TempErrors(object):
@ -610,14 +662,14 @@ class MatchPatternError(ValueError):
def __init__(self, key, errors):
"""Custom error for validating match patterns.
key (unicode): The name of the matcher rule.
key (str): The name of the matcher rule.
errors (dict): Validation errors (sequence of strings) mapped to pattern
ID, i.e. the index of the added pattern.
"""
msg = "Invalid token patterns for matcher rule '{}'\n".format(key)
msg = f"Invalid token patterns for matcher rule '{key}'\n"
for pattern_idx, error_msgs in errors.items():
pattern_errors = "\n".join(["- {}".format(e) for e in error_msgs])
msg += "\nPattern {}:\n{}\n".format(pattern_idx, pattern_errors)
pattern_errors = "\n".join([f"- {e}" for e in error_msgs])
msg += f"\nPattern {pattern_idx}:\n{pattern_errors}\n"
ValueError.__init__(self, msg)

8
spacy/glossary.py
View File

@ -1,12 +1,8 @@
# coding: utf8
from __future__ import unicode_literals
def explain(term):
"""Get a description for a given POS tag, dependency label or entity type.
term (unicode): The term to explain.
RETURNS (unicode): The explanation, or `None` if not found in the glossary.
term (str): The term to explain.
RETURNS (str): The explanation, or `None` if not found in the glossary.
EXAMPLE:
>>> spacy.explain(u'NORP')

37
spacy/gold.pxd
View File

@ -1,9 +1,10 @@
from cymem.cymem cimport Pool
from .structs cimport TokenC
from .typedefs cimport attr_t
from .syntax.transition_system cimport Transition
from .tokens import Doc
cdef struct GoldParseC:
int* tags
@ -19,23 +20,49 @@ cdef class GoldParse:
cdef Pool mem
cdef GoldParseC c
cdef readonly TokenAnnotation orig
cdef int length
cdef public int loss
cdef public list words
cdef public list tags
cdef public list morphology
cdef public list pos
cdef public list morphs
cdef public list lemmas
cdef public list sent_starts
cdef public list heads
cdef public list labels
cdef public dict orths
cdef public list ner
cdef public list ents
cdef public dict brackets
cdef public object cats
cdef public dict cats
cdef public dict links
cdef readonly list cand_to_gold
cdef readonly list gold_to_cand
cdef readonly list orig_annot
cdef class TokenAnnotation:
cdef public list ids
cdef public list words
cdef public list tags
cdef public list pos
cdef public list morphs
cdef public list lemmas
cdef public list heads
cdef public list deps
cdef public list entities
cdef public list sent_starts
cdef public dict brackets_by_start
cdef class DocAnnotation:
cdef public object cats
cdef public object links
cdef class Example:
cdef public object doc
cdef public TokenAnnotation token_annotation
cdef public DocAnnotation doc_annotation
cdef public object goldparse

826
spacy/gold.pyx
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File diff suppressed because it is too large Load Diff

4
spacy/kb.pxd
View File

@ -1,15 +1,15 @@
"""Knowledge-base for entity or concept linking."""
from cymem.cymem cimport Pool
from preshed.maps cimport PreshMap
from libcpp.vector cimport vector
from libc.stdint cimport int32_t, int64_t
from libc.stdio cimport FILE
from .vocab cimport Vocab
from .typedefs cimport hash_t
from .structs cimport KBEntryC, AliasC
ctypedef vector[KBEntryC] entry_vec
ctypedef vector[AliasC] alias_vec
ctypedef vector[float] float_vec

13
spacy/kb.pyx
View File

@ -1,6 +1,4 @@
# cython: infer_types=True
# cython: profile=True
# coding: utf8
# cython: infer_types=True, profile=True
from cymem.cymem cimport Pool
from preshed.maps cimport PreshMap
from cpython.exc cimport PyErr_SetFromErrno
@ -8,12 +6,11 @@ from libc.stdio cimport fopen, fclose, fread, fwrite, feof, fseek
from libc.stdint cimport int32_t, int64_t
from libcpp.vector cimport vector
from pathlib import Path
import warnings
from os import path
from pathlib import Path
from .typedefs cimport hash_t
from .errors import Errors, Warnings
@ -41,7 +38,7 @@ cdef class Candidate:
@property
def entity_(self):
"""RETURNS (unicode): ID/name of this entity in the KB"""
"""RETURNS (str): ID/name of this entity in the KB"""
return self.kb.vocab.strings[self.entity_hash]
@property
@ -51,7 +48,7 @@ cdef class Candidate:
@property
def alias_(self):
"""RETURNS (unicode): ID of the original alias"""
"""RETURNS (str): ID of the original alias"""
return self.kb.vocab.strings[self.alias_hash]
@property
@ -445,6 +442,8 @@ cdef class KnowledgeBase:
cdef class Writer:
def __init__(self, object loc):
if path.exists(loc):
assert not path.isdir(loc), f"{loc} is directory"
if isinstance(loc, Path):
loc = bytes(loc)
if path.exists(loc):

3
spacy/lang/af/__init__.py
View File

@ -1,6 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
from .stop_words import STOP_WORDS
from ...language import Language
from ...attrs import LANG

4
spacy/lang/af/stop_words.py
View File

@ -1,7 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
# Source: https://github.com/stopwords-iso/stopwords-af
STOP_WORDS = set(

3
spacy/lang/ar/__init__.py
View File

@ -1,6 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
from .stop_words import STOP_WORDS
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_SUFFIXES

3
spacy/lang/ar/examples.py
View File

@ -1,6 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
"""
Example sentences to test spaCy and its language models.

2
spacy/lang/ar/lex_attrs.py
View File

@ -1,5 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
from ...attrs import LIKE_NUM
_num_words = set(

3
spacy/lang/ar/punctuation.py
View File

@ -1,6 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
from ..char_classes import LIST_PUNCT, LIST_ELLIPSES, LIST_QUOTES, CURRENCY
from ..char_classes import UNITS, ALPHA_UPPER

3
spacy/lang/ar/stop_words.py
View File

@ -1,6 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
STOP_WORDS = set(
"""
من

3
spacy/lang/ar/tokenizer_exceptions.py
View File

@ -1,6 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
from ...symbols import ORTH, LEMMA

3
spacy/lang/bg/__init__.py
View File

@ -1,6 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
from .stop_words import STOP_WORDS
from ...language import Language
from ...attrs import LANG

3
spacy/lang/bg/examples.py
View File

@ -1,6 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
"""
Example sentences to test spaCy and its language models.

4
spacy/lang/bg/stop_words.py
View File

@ -1,7 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
# Source: https://github.com/Alir3z4/stop-words
STOP_WORDS = set(

3
spacy/lang/bn/__init__.py
View File

@ -1,6 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
from .punctuation import TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES, TOKENIZER_INFIXES
from .tag_map import TAG_MAP

4
spacy/lang/bn/examples.py
View File

@ -1,7 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
"""
Example sentences to test spaCy and its language models.

3
spacy/lang/bn/morph_rules.py
View File

@ -1,6 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
from ...symbols import LEMMA, PRON_LEMMA

3
spacy/lang/bn/punctuation.py
View File

@ -1,6 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
from ..char_classes import LIST_PUNCT, LIST_ELLIPSES, LIST_QUOTES, LIST_ICONS
from ..char_classes import ALPHA_LOWER, ALPHA, HYPHENS, CONCAT_QUOTES, UNITS

4
spacy/lang/bn/stop_words.py
View File

@ -1,7 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
STOP_WORDS = set(
"""
অতএব অথচ অথবা অনুযায়ী অনেক অনেকে অনেকেই অন্তত অবধি অবশ্য অর্থাৎ অন্য অনুযায়ী অর্ধভাগে

7
spacy/lang/bn/tag_map.py
View File

@ -1,6 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
from ...symbols import POS, PUNCT, ADJ, CONJ, SCONJ, NUM, DET, ADV, ADP, X, VERB
from ...symbols import CCONJ, NOUN, PROPN, PART, INTJ, SPACE, PRON, AUX, SYM
@ -14,8 +11,8 @@ TAG_MAP = {
'""': {POS: PUNCT, "PunctType": "quot", "PunctSide": "fin"},
"''": {POS: PUNCT, "PunctType": "quot", "PunctSide": "fin"},
":": {POS: PUNCT},
"৳": {POS: SYM, "Other": {"SymType": "currency"}},
"#": {POS: SYM, "Other": {"SymType": "numbersign"}},
"৳": {POS: SYM, "SymType": "currency"},
"#": {POS: SYM, "SymType": "numbersign"},
"AFX": {POS: ADJ, "Hyph": "yes"},
"CC": {POS: CONJ, "ConjType": "coor"},
"CD": {POS: NUM, "NumType": "card"},

3
spacy/lang/bn/tokenizer_exceptions.py
View File

@ -1,6 +1,3 @@
# coding=utf-8
from __future__ import unicode_literals
from ...symbols import ORTH, LEMMA

3
spacy/lang/ca/__init__.py
View File

@ -1,6 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
from .stop_words import STOP_WORDS
from .lex_attrs import LEX_ATTRS

4
spacy/lang/ca/examples.py
View File

@ -1,7 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
"""
Example sentences to test spaCy and its language models.

3
spacy/lang/ca/lex_attrs.py
View File

@ -1,6 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
from ...attrs import LIKE_NUM

3
spacy/lang/ca/punctuation.py
View File

@ -1,6 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
from ..punctuation import TOKENIZER_INFIXES
from ..char_classes import ALPHA

4
spacy/lang/ca/stop_words.py
View File

@ -1,7 +1,3 @@
# encoding: utf8
from __future__ import unicode_literals
STOP_WORDS = set(
"""
a abans ací ah així això al aleshores algun alguna algunes alguns alhora allà allí allò

28
spacy/lang/ca/tag_map.py
View File

@ -1,28 +0,0 @@
# coding: utf8
from __future__ import unicode_literals
from ..symbols import POS, ADV, NOUN, ADP, PRON, SCONJ, PROPN, DET, SYM, INTJ
from ..symbols import PUNCT, NUM, AUX, X, CONJ, ADJ, VERB, PART, SPACE, CCONJ
TAG_MAP = {
"ADV": {POS: ADV},
"NOUN": {POS: NOUN},
"ADP": {POS: ADP},
"PRON": {POS: PRON},
"SCONJ": {POS: SCONJ},
"PROPN": {POS: PROPN},
"DET": {POS: DET},
"SYM": {POS: SYM},
"INTJ": {POS: INTJ},
"PUNCT": {POS: PUNCT},
"NUM": {POS: NUM},
"AUX": {POS: AUX},
"X": {POS: X},
"CONJ": {POS: CONJ},
"CCONJ": {POS: CCONJ},
"ADJ": {POS: ADJ},
"VERB": {POS: VERB},
"PART": {POS: PART},
"SP": {POS: SPACE},
}

7
spacy/lang/ca/tokenizer_exceptions.py
View File

@ -1,6 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
from ...symbols import ORTH, LEMMA
@ -33,9 +30,9 @@ _exc["12m."] = [{ORTH: "12"}, {ORTH: "m.", LEMMA: "p.m."}]
for h in range(1, 12 + 1):
for period in ["a.m.", "am"]:
_exc["%d%s" % (h, period)] = [{ORTH: "%d" % h}, {ORTH: period, LEMMA: "a.m."}]
_exc[f"{h}{period}"] = [{ORTH: f"{h}"}, {ORTH: period, LEMMA: "a.m."}]
for period in ["p.m.", "pm"]:
_exc["%d%s" % (h, period)] = [{ORTH: "%d" % h}, {ORTH: period, LEMMA: "p.m."}]
_exc[f"{h}{period}"] = [{ORTH: f"{h}"}, {ORTH: period, LEMMA: "p.m."}]
TOKENIZER_EXCEPTIONS = _exc

3
spacy/lang/char_classes.py
View File

@ -1,6 +1,3 @@
# coding: utf8
from __future__ import unicode_literals
split_chars = lambda char: list(char.strip().split(" "))
merge_chars = lambda char: char.strip().replace(" ", "|")
group_chars = lambda char: char.strip().replace(" ", "")

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