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106
.github/contributors/lfiedler.md vendored Normal file
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# spaCy contributor agreement
This spaCy Contributor Agreement (**"SCA"**) is based on the
[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
The SCA applies to any contribution that you make to any product or project
managed by us (the **"project"**), and sets out the intellectual property rights
you grant to us in the contributed materials. The term **"us"** shall mean
[ExplosionAI GmbH](https://explosion.ai/legal). The term
**"you"** shall mean the person or entity identified below.
If you agree to be bound by these terms, fill in the information requested
below and include the filled-in version with your first pull request, under the
folder [`.github/contributors/`](/.github/contributors/). The name of the file
should be your GitHub username, with the extension `.md`. For example, the user
example_user would create the file `.github/contributors/example_user.md`.
Read this agreement carefully before signing. These terms and conditions
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
documentation, or any other material posted or submitted by you to the project.
2. With respect to any worldwide copyrights, or copyright applications and
registrations, in your contribution:
* you hereby assign to us joint ownership, and to the extent that such
assignment is or becomes invalid, ineffective or unenforceable, you hereby
grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
royalty-free, unrestricted license to exercise all rights under those
copyrights. This includes, at our option, the right to sublicense these same
rights to third parties through multiple levels of sublicensees or other
licensing arrangements;
* you agree that each of us can do all things in relation to your
contribution as if each of us were the sole owners, and if one of us makes
a derivative work of your contribution, the one who makes the derivative
work (or has it made will be the sole owner of that derivative work;
* you agree that you will not assert any moral rights in your contribution
against us, our licensees or transferees;
* you agree that we may register a copyright in your contribution and
exercise all ownership rights associated with it; and
* you agree that neither of us has any duty to consult with, obtain the
consent of, pay or render an accounting to the other for any use or
distribution of your contribution.
3. With respect to any patents you own, or that you can license without payment
to any third party, you hereby grant to us a perpetual, irrevocable,
non-exclusive, worldwide, no-charge, royalty-free license to:
* make, have made, use, sell, offer to sell, import, and otherwise transfer
your contribution in whole or in part, alone or in combination with or
included in any product, work or materials arising out of the project to
which your contribution was submitted, and
* at our option, to sublicense these same rights to third parties through
multiple levels of sublicensees or other licensing arrangements.
4. Except as set out above, you keep all right, title, and interest in your
contribution. The rights that you grant to us under these terms are effective
on the date you first submitted a contribution to us, even if your submission
took place before the date you sign these terms.
5. You covenant, represent, warrant and agree that:
* Each contribution that you submit is and shall be an original work of
authorship and you can legally grant the rights set out in this SCA;
* to the best of your knowledge, each contribution will not violate any
third party's copyrights, trademarks, patents, or other intellectual
property rights; and
* each contribution shall be in compliance with U.S. export control laws and
other applicable export and import laws. You agree to notify us if you
become aware of any circumstance which would make any of the foregoing
representations inaccurate in any respect. We may publicly disclose your
participation in the project, including the fact that you have signed the SCA.
6. This SCA is governed by the laws of the State of California and applicable
U.S. Federal law. Any choice of law rules will not apply.
7. Please place an “x” on one of the applicable statement below. Please do NOT
mark both statements:
* [x] I am signing on behalf of myself as an individual and no other person
or entity, including my employer, has or will have rights with respect to my
contributions.
* [ ] I am signing on behalf of my employer or a legal entity and I have the
actual authority to contractually bind that entity.
## Contributor Details
| Field | Entry |
|------------------------------- | -------------------- |
| Name | Leander Fiedler |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 06 April 2020 |
| GitHub username | lfiedler |
| Website (optional) | |

1
spacy/errors.py
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@ -567,6 +567,7 @@ class Errors(object):
E197 = ("Row out of bounds, unable to add row {row} for key {key}.")
E198 = ("Unable to return {n} most similar vectors for the current vectors "
"table, which contains {n_rows} vectors.")
E199 = ("Unable to merge 0-length span at doc[{start}:{end}].")
@add_codes

8
spacy/kb.pyx
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@ -445,10 +445,10 @@ cdef class KnowledgeBase:
cdef class Writer:
def __init__(self, object loc):
if path.exists(loc):
assert not path.isdir(loc), "%s is directory." % loc
if isinstance(loc, Path):
loc = bytes(loc)
if path.exists(loc):
assert not path.isdir(loc), "%s is directory." % loc
cdef bytes bytes_loc = loc.encode('utf8') if type(loc) == unicode else loc
self._fp = fopen(<char*>bytes_loc, 'wb')
if not self._fp:
@ -490,10 +490,10 @@ cdef class Writer:
cdef class Reader:
def __init__(self, object loc):
assert path.exists(loc)
assert not path.isdir(loc)
if isinstance(loc, Path):
loc = bytes(loc)
assert path.exists(loc)
assert not path.isdir(loc)
cdef bytes bytes_loc = loc.encode('utf8') if type(loc) == unicode else loc
self._fp = fopen(<char*>bytes_loc, 'rb')
if not self._fp:

5
spacy/language.py
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@ -907,9 +907,8 @@ class Language(object):
serializers["tokenizer"] = lambda p: self.tokenizer.to_disk(
p, exclude=["vocab"]
)
serializers["meta.json"] = lambda p: p.open("w").write(
srsly.json_dumps(self.meta)
)
serializers["meta.json"] = lambda p: srsly.write_json(p, self.meta)
for name, proc in self.pipeline:
if not hasattr(proc, "name"):
continue

6
spacy/pipeline/pipes.pyx
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@ -203,7 +203,7 @@ class Pipe(object):
serialize["cfg"] = lambda p: srsly.write_json(p, self.cfg)
serialize["vocab"] = lambda p: self.vocab.to_disk(p)
if self.model not in (None, True, False):
serialize["model"] = lambda p: p.open("wb").write(self.model.to_bytes())
serialize["model"] = lambda p: self.model.to_disk(p)
exclude = util.get_serialization_exclude(serialize, exclude, kwargs)
util.to_disk(path, serialize, exclude)
@ -626,7 +626,7 @@ class Tagger(Pipe):
serialize = OrderedDict((
("vocab", lambda p: self.vocab.to_disk(p)),
("tag_map", lambda p: srsly.write_msgpack(p, tag_map)),
("model", lambda p: p.open("wb").write(self.model.to_bytes())),
("model", lambda p: self.model.to_disk(p)),
("cfg", lambda p: srsly.write_json(p, self.cfg))
))
exclude = util.get_serialization_exclude(serialize, exclude, kwargs)
@ -1395,7 +1395,7 @@ class EntityLinker(Pipe):
serialize["vocab"] = lambda p: self.vocab.to_disk(p)
serialize["kb"] = lambda p: self.kb.dump(p)
if self.model not in (None, True, False):
serialize["model"] = lambda p: p.open("wb").write(self.model.to_bytes())
serialize["model"] = lambda p: self.model.to_disk(p)
exclude = util.get_serialization_exclude(serialize, exclude, kwargs)
util.to_disk(path, serialize, exclude)

7
spacy/tests/doc/test_retokenize_merge.py
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@ -425,3 +425,10 @@ def test_retokenize_skip_duplicates(en_vocab):
retokenizer.merge(doc[0:2])
assert len(doc) == 2
assert doc[0].text == "hello world"
def test_retokenize_disallow_zero_length(en_vocab):
doc = Doc(en_vocab, words=["hello", "world", "!"])
with pytest.raises(ValueError):
with doc.retokenize() as retokenizer:
retokenizer.merge(doc[1:1])

142
spacy/tests/regression/test_issue5230.py Normal file
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@ -0,0 +1,142 @@
# coding: utf8
import warnings
from unittest import TestCase
import pytest
import srsly
from numpy import zeros
from spacy.kb import KnowledgeBase, Writer
from spacy.vectors import Vectors
from spacy.language import Language
from spacy.pipeline import Pipe
from spacy.tests.util import make_tempdir
def nlp():
return Language()
def vectors():
data = zeros((3, 1), dtype="f")
keys = ["cat", "dog", "rat"]
return Vectors(data=data, keys=keys)
def custom_pipe():
# create dummy pipe partially implementing interface -- only want to test to_disk
class SerializableDummy(object):
def __init__(self, **cfg):
if cfg:
self.cfg = cfg
else:
self.cfg = None
super(SerializableDummy, self).__init__()
def to_bytes(self, exclude=tuple(), disable=None, **kwargs):
return srsly.msgpack_dumps({"dummy": srsly.json_dumps(None)})
def from_bytes(self, bytes_data, exclude):
return self
def to_disk(self, path, exclude=tuple(), **kwargs):
pass
def from_disk(self, path, exclude=tuple(), **kwargs):
return self
class MyPipe(Pipe):
def __init__(self, vocab, model=True, **cfg):
if cfg:
self.cfg = cfg
else:
self.cfg = None
self.model = SerializableDummy()
self.vocab = SerializableDummy()
return MyPipe(None)
def tagger():
nlp = Language()
nlp.add_pipe(nlp.create_pipe("tagger"))
tagger = nlp.get_pipe("tagger")
# need to add model for two reasons:
# 1. no model leads to error in serialization,
# 2. the affected line is the one for model serialization
tagger.begin_training(pipeline=nlp.pipeline)
return tagger
def entity_linker():
nlp = Language()
nlp.add_pipe(nlp.create_pipe("entity_linker"))
entity_linker = nlp.get_pipe("entity_linker")
# need to add model for two reasons:
# 1. no model leads to error in serialization,
# 2. the affected line is the one for model serialization
kb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
entity_linker.set_kb(kb)
entity_linker.begin_training(pipeline=nlp.pipeline)
return entity_linker
objects_to_test = (
[nlp(), vectors(), custom_pipe(), tagger(), entity_linker()],
["nlp", "vectors", "custom_pipe", "tagger", "entity_linker"],
)
def write_obj_and_catch_warnings(obj):
with make_tempdir() as d:
with warnings.catch_warnings(record=True) as warnings_list:
warnings.filterwarnings("always", category=ResourceWarning)
obj.to_disk(d)
# in python3.5 it seems that deprecation warnings are not filtered by filterwarnings
return list(filter(lambda x: isinstance(x, ResourceWarning), warnings_list))
@pytest.mark.parametrize("obj", objects_to_test[0], ids=objects_to_test[1])
def test_to_disk_resource_warning(obj):
warnings_list = write_obj_and_catch_warnings(obj)
assert len(warnings_list) == 0
def test_writer_with_path_py35():
writer = None
with make_tempdir() as d:
path = d / "test"
try:
writer = Writer(path)
except Exception as e:
pytest.fail(str(e))
finally:
if writer:
writer.close()
def test_save_and_load_knowledge_base():
nlp = Language()
kb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
with make_tempdir() as d:
path = d / "kb"
try:
kb.dump(path)
except Exception as e:
pytest.fail(str(e))
try:
kb_loaded = KnowledgeBase(nlp.vocab, entity_vector_length=1)
kb_loaded.load_bulk(path)
except Exception as e:
pytest.fail(str(e))
class TestToDiskResourceWarningUnittest(TestCase):
def test_resource_warning(self):
scenarios = zip(*objects_to_test)
for scenario in scenarios:
with self.subTest(msg=scenario[1]):
warnings_list = write_obj_and_catch_warnings(scenario[0])
self.assertEqual(len(warnings_list), 0)

11
spacy/tests/test_misc.py
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@ -135,3 +135,14 @@ def test_ascii_filenames():
root = Path(__file__).parent.parent
for path in root.glob("**/*"):
assert all(ord(c) < 128 for c in path.name), path.name
def test_load_model_blank_shortcut():
"""Test that using a model name like "blank:en" works as a shortcut for
spacy.blank("en").
"""
nlp = util.load_model("blank:en")
assert nlp.lang == "en"
assert nlp.pipeline == []
with pytest.raises(ImportError):
util.load_model("blank:fjsfijsdof")

2
spacy/tokens/_retokenize.pyx
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@ -55,6 +55,8 @@ cdef class Retokenizer:
"""
if (span.start, span.end) in self._spans_to_merge:
return
if span.end - span.start <= 0:
raise ValueError(Errors.E199.format(start=span.start, end=span.end))
for token in span:
if token.i in self.tokens_to_merge:
raise ValueError(Errors.E102.format(token=repr(token)))

2
spacy/util.py
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@ -161,6 +161,8 @@ def load_model(name, **overrides):
if not data_path or not data_path.exists():
raise IOError(Errors.E049.format(path=path2str(data_path)))
if isinstance(name, basestring_): # in data dir / shortcut
if name.startswith("blank:"): # shortcut for blank model
return get_lang_class(name.replace("blank:", ""))()
if name in set([d.name for d in data_path.iterdir()]):
return load_model_from_link(name, **overrides)
if is_package(name): # installed as package

10
spacy/vectors.pyx
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@ -383,8 +383,16 @@ cdef class Vectors:
save_array = lambda arr, file_: xp.save(file_, arr, allow_pickle=False)
else:
save_array = lambda arr, file_: xp.save(file_, arr)
def save_vectors(path):
# the source of numpy.save indicates that the file object is closed after use.
# but it seems that somehow this does not happen, as ResourceWarnings are raised here.
# in order to not rely on this, wrap in context manager.
with path.open("wb") as _file:
save_array(self.data, _file)
serializers = OrderedDict((
("vectors", lambda p: save_array(self.data, p.open("wb"))),
("vectors", lambda p: save_vectors(p)),
("key2row", lambda p: srsly.write_msgpack(p, self.key2row))
))
return util.to_disk(path, serializers, [])

150
website/docs/api/token.md
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@ -351,25 +351,9 @@ property to `0` for the first word of the document.
- assert doc[4].sent_start == 1
+ assert doc[4].is_sent_start == True
```
</Infobox>
## Token.is_sent_end {#is_sent_end tag="property" new="2"}
A boolean value indicating whether the token ends a sentence. `None` if
unknown. Defaults to `True` for the last token in the `Doc`.
> #### Example
>
> ```python
> doc = nlp("Give it back! He pleaded.")
> assert doc[3].is_sent_end
> assert not doc[4].is_sent_end
> ```
| Name | Type | Description |
| ----------- | ---- | ------------------------------------ |
| **RETURNS** | bool | Whether the token ends a sentence. |
## Token.has_vector {#has_vector tag="property" model="vectors"}
A boolean value indicating whether a word vector is associated with the token.
@ -424,71 +408,71 @@ The L2 norm of the token's vector representation.
## Attributes {#attributes}
| Name | Type | Description |
| -------------------------------------------- | ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `doc` | `Doc` | The parent document. |
| `sent` <Tag variant="new">2.0.12</Tag> | `Span` | The sentence span that this token is a part of. |
| `text` | unicode | Verbatim text content. |
| `text_with_ws` | unicode | Text content, with trailing space character if present. |
| `whitespace_` | unicode | Trailing space character if present. |
| `orth` | int | ID of the verbatim text content. |
| `orth_` | unicode | Verbatim text content (identical to `Token.text`). Exists mostly for consistency with the other attributes. |
| `vocab` | `Vocab` | The vocab object of the parent `Doc`. |
| `tensor` <Tag variant="new">2.1.7</Tag> | `ndarray` | The tokens's slice of the parent `Doc`'s tensor. |
| `head` | `Token` | The syntactic parent, or "governor", of this token. |
| `left_edge` | `Token` | The leftmost token of this token's syntactic descendants. |
| `right_edge` | `Token` | The rightmost token of this token's syntactic descendants. |
| `i` | int | The index of the token within the parent document. |
| `ent_type` | int | Named entity type. |
| `ent_type_` | unicode | Named entity type. |
| `ent_iob` | int | IOB code of named entity tag. `3` means the token begins an entity, `2` means it is outside an entity, `1` means it is inside an entity, and `0` means no entity tag is set. |
| `ent_iob_` | unicode | IOB code of named entity tag. "B" means the token begins an entity, "I" means it is inside an entity, "O" means it is outside an entity, and "" means no entity tag is set. |
| `ent_kb_id` <Tag variant="new">2.2</Tag> | int | Knowledge base ID that refers to the named entity this token is a part of, if any. |
| `ent_kb_id_` <Tag variant="new">2.2</Tag> | unicode | Knowledge base ID that refers to the named entity this token is a part of, if any. |
| `ent_id` | int | ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution. |
| `ent_id_` | unicode | ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution. |
| `lemma` | int | Base form of the token, with no inflectional suffixes. |
| `lemma_` | unicode | Base form of the token, with no inflectional suffixes. |
| `norm` | int | The token's norm, i.e. a normalized form of the token text. Usually set in the language's [tokenizer exceptions](/usage/adding-languages#tokenizer-exceptions) or [norm exceptions](/usage/adding-languages#norm-exceptions). |
| `norm_` | unicode | The token's norm, i.e. a normalized form of the token text. Usually set in the language's [tokenizer exceptions](/usage/adding-languages#tokenizer-exceptions) or [norm exceptions](/usage/adding-languages#norm-exceptions). |
| `lower` | int | Lowercase form of the token. |
| `lower_` | unicode | Lowercase form of the token text. Equivalent to `Token.text.lower()`. |
| Name | Type | Description |
| -------------------------------------------- | ------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `doc` | `Doc` | The parent document. |
| `sent` <Tag variant="new">2.0.12</Tag> | `Span` | The sentence span that this token is a part of. |
| `text` | unicode | Verbatim text content. |
| `text_with_ws` | unicode | Text content, with trailing space character if present. |
| `whitespace_` | unicode | Trailing space character if present. |
| `orth` | int | ID of the verbatim text content. |
| `orth_` | unicode | Verbatim text content (identical to `Token.text`). Exists mostly for consistency with the other attributes. |
| `vocab` | `Vocab` | The vocab object of the parent `Doc`. |
| `tensor` <Tag variant="new">2.1.7</Tag> | `ndarray` | The tokens's slice of the parent `Doc`'s tensor. |
| `head` | `Token` | The syntactic parent, or "governor", of this token. |
| `left_edge` | `Token` | The leftmost token of this token's syntactic descendants. |
| `right_edge` | `Token` | The rightmost token of this token's syntactic descendants. |
| `i` | int | The index of the token within the parent document. |
| `ent_type` | int | Named entity type. |
| `ent_type_` | unicode | Named entity type. |
| `ent_iob` | int | IOB code of named entity tag. `3` means the token begins an entity, `2` means it is outside an entity, `1` means it is inside an entity, and `0` means no entity tag is set. |
| `ent_iob_` | unicode | IOB code of named entity tag. "B" means the token begins an entity, "I" means it is inside an entity, "O" means it is outside an entity, and "" means no entity tag is set. |
| `ent_kb_id` <Tag variant="new">2.2</Tag> | int | Knowledge base ID that refers to the named entity this token is a part of, if any. |
| `ent_kb_id_` <Tag variant="new">2.2</Tag> | unicode | Knowledge base ID that refers to the named entity this token is a part of, if any. |
| `ent_id` | int | ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution. |
| `ent_id_` | unicode | ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution. |
| `lemma` | int | Base form of the token, with no inflectional suffixes. |
| `lemma_` | unicode | Base form of the token, with no inflectional suffixes. |
| `norm` | int | The token's norm, i.e. a normalized form of the token text. Usually set in the language's [tokenizer exceptions](/usage/adding-languages#tokenizer-exceptions) or [norm exceptions](/usage/adding-languages#norm-exceptions). |
| `norm_` | unicode | The token's norm, i.e. a normalized form of the token text. Usually set in the language's [tokenizer exceptions](/usage/adding-languages#tokenizer-exceptions) or [norm exceptions](/usage/adding-languages#norm-exceptions). |
| `lower` | int | Lowercase form of the token. |
| `lower_` | unicode | Lowercase form of the token text. Equivalent to `Token.text.lower()`. |
| `shape` | int | Transform of the tokens's string, to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by `d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. |
| `shape_` | unicode | Transform of the tokens's string, to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by `d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. |
| `prefix` | int | Hash value of a length-N substring from the start of the token. Defaults to `N=1`. |
| `prefix_` | unicode | A length-N substring from the start of the token. Defaults to `N=1`. |
| `suffix` | int | Hash value of a length-N substring from the end of the token. Defaults to `N=3`. |
| `suffix_` | unicode | Length-N substring from the end of the token. Defaults to `N=3`. |
| `is_alpha` | bool | Does the token consist of alphabetic characters? Equivalent to `token.text.isalpha()`. |
| `is_ascii` | bool | Does the token consist of ASCII characters? Equivalent to `all(ord(c) < 128 for c in token.text)`. |
| `is_digit` | bool | Does the token consist of digits? Equivalent to `token.text.isdigit()`. |
| `is_lower` | bool | Is the token in lowercase? Equivalent to `token.text.islower()`. |
| `is_upper` | bool | Is the token in uppercase? Equivalent to `token.text.isupper()`. |
| `is_title` | bool | Is the token in titlecase? Equivalent to `token.text.istitle()`. |
| `is_punct` | bool | Is the token punctuation? |
| `is_left_punct` | bool | Is the token a left punctuation mark, e.g. `'('` ? |
| `is_right_punct` | bool | Is the token a right punctuation mark, e.g. `')'` ? |
| `is_space` | bool | Does the token consist of whitespace characters? Equivalent to `token.text.isspace()`. |
| `is_bracket` | bool | Is the token a bracket? |
| `is_quote` | bool | Is the token a quotation mark? |
| `is_currency` <Tag variant="new">2.0.8</Tag> | bool | Is the token a currency symbol? |
| `like_url` | bool | Does the token resemble a URL? |
| `like_num` | bool | Does the token represent a number? e.g. "10.9", "10", "ten", etc. |
| `like_email` | bool | Does the token resemble an email address? |
| `is_oov` | bool | Is the token out-of-vocabulary? |
| `is_stop` | bool | Is the token part of a "stop list"? |
| `pos` | int | Coarse-grained part-of-speech from the [Universal POS tag set](https://universaldependencies.org/docs/u/pos/). |
| `pos_` | unicode | Coarse-grained part-of-speech from the [Universal POS tag set](https://universaldependencies.org/docs/u/pos/). |
| `tag` | int | Fine-grained part-of-speech. |
| `tag_` | unicode | Fine-grained part-of-speech. |
| `dep` | int | Syntactic dependency relation. |
| `dep_` | unicode | Syntactic dependency relation. |
| `lang` | int | Language of the parent document's vocabulary. |
| `lang_` | unicode | Language of the parent document's vocabulary. |
| `prob` | float | Smoothed log probability estimate of token's word type (context-independent entry in the vocabulary). |
| `idx` | int | The character offset of the token within the parent document. |
| `sentiment` | float | A scalar value indicating the positivity or negativity of the token. |
| `lex_id` | int | Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors. |
| `rank` | int | Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors. |
| `cluster` | int | Brown cluster ID. |
| `_` | `Underscore` | User space for adding custom [attribute extensions](/usage/processing-pipelines#custom-components-attributes). |
| `prefix` | int | Hash value of a length-N substring from the start of the token. Defaults to `N=1`. |
| `prefix_` | unicode | A length-N substring from the start of the token. Defaults to `N=1`. |
| `suffix` | int | Hash value of a length-N substring from the end of the token. Defaults to `N=3`. |
| `suffix_` | unicode | Length-N substring from the end of the token. Defaults to `N=3`. |
| `is_alpha` | bool | Does the token consist of alphabetic characters? Equivalent to `token.text.isalpha()`. |
| `is_ascii` | bool | Does the token consist of ASCII characters? Equivalent to `all(ord(c) < 128 for c in token.text)`. |
| `is_digit` | bool | Does the token consist of digits? Equivalent to `token.text.isdigit()`. |
| `is_lower` | bool | Is the token in lowercase? Equivalent to `token.text.islower()`. |
| `is_upper` | bool | Is the token in uppercase? Equivalent to `token.text.isupper()`. |
| `is_title` | bool | Is the token in titlecase? Equivalent to `token.text.istitle()`. |
| `is_punct` | bool | Is the token punctuation? |
| `is_left_punct` | bool | Is the token a left punctuation mark, e.g. `'('` ? |
| `is_right_punct` | bool | Is the token a right punctuation mark, e.g. `')'` ? |
| `is_space` | bool | Does the token consist of whitespace characters? Equivalent to `token.text.isspace()`. |
| `is_bracket` | bool | Is the token a bracket? |
| `is_quote` | bool | Is the token a quotation mark? |
| `is_currency` <Tag variant="new">2.0.8</Tag> | bool | Is the token a currency symbol? |
| `like_url` | bool | Does the token resemble a URL? |
| `like_num` | bool | Does the token represent a number? e.g. "10.9", "10", "ten", etc. |
| `like_email` | bool | Does the token resemble an email address? |
| `is_oov` | bool | Is the token out-of-vocabulary? |
| `is_stop` | bool | Is the token part of a "stop list"? |
| `pos` | int | Coarse-grained part-of-speech from the [Universal POS tag set](https://universaldependencies.org/docs/u/pos/). |
| `pos_` | unicode | Coarse-grained part-of-speech from the [Universal POS tag set](https://universaldependencies.org/docs/u/pos/). |
| `tag` | int | Fine-grained part-of-speech. |
| `tag_` | unicode | Fine-grained part-of-speech. |
| `dep` | int | Syntactic dependency relation. |
| `dep_` | unicode | Syntactic dependency relation. |
| `lang` | int | Language of the parent document's vocabulary. |
| `lang_` | unicode | Language of the parent document's vocabulary. |
| `prob` | float | Smoothed log probability estimate of token's word type (context-independent entry in the vocabulary). |
| `idx` | int | The character offset of the token within the parent document. |
| `sentiment` | float | A scalar value indicating the positivity or negativity of the token. |
| `lex_id` | int | Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors. |
| `rank` | int | Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors. |
| `cluster` | int | Brown cluster ID. |
| `_` | `Underscore` | User space for adding custom [attribute extensions](/usage/processing-pipelines#custom-components-attributes). |