def add_codes(err_cls):
"""Add error codes to string messages via class attribute names."""
class ErrorsWithCodes(err_cls):
def __getattribute__(self, code):
msg = super(ErrorsWithCodes, self).__getattribute__(code)
if code.startswith("__"): # python system attributes like __class__
return msg
else:
return "[{code}] {msg}".format(code=code, msg=msg)
return ErrorsWithCodes()
# fmt: off
@add_codes
class Warnings:
W004 = ("No text fixing enabled. Run `pip install ftfy` to enable fixing "
"using ftfy.fix_text if necessary.")
W005 = ("Doc object not parsed. This means displaCy won't be able to "
"generate a dependency visualization for it. Make sure the Doc "
"was processed with a model that supports dependency parsing, and "
"not just a language class like `English()`. For more info, see "
"the docs:\nhttps://spacy.io/usage/models")
W006 = ("No entities to visualize found in Doc object. If this is "
"surprising to you, make sure the Doc was processed using a model "
"that supports named entity recognition, and check the `doc.ents` "
"property manually if necessary.")
W007 = ("The model you're using has no word vectors loaded, so the result "
"of the {obj}.similarity method will be based on the tagger, "
"parser and NER, which may not give useful similarity judgements. "
"This may happen if you're using one of the small models, e.g. "
"`en_core_web_sm`, which don't ship with word vectors and only "
"use context-sensitive tensors. You can always add your own word "
"vectors, or use one of the larger models instead if available.")
W008 = ("Evaluating {obj}.similarity based on empty vectors.")
W011 = ("It looks like you're calling displacy.serve from within a "
"Jupyter notebook or a similar environment. This likely means "
"you're already running a local web server, so there's no need to "
"make displaCy start another one. Instead, you should be able to "
"replace displacy.serve with displacy.render to show the "
"visualization.")
W012 = ("A Doc object you're adding to the PhraseMatcher for pattern "
"'{key}' is parsed and/or tagged, but to match on '{attr}', you "
"don't actually need this information. This means that creating "
"the patterns is potentially much slower, because all pipeline "
"components are applied. To only create tokenized Doc objects, "
"try using `nlp.make_doc(text)` or process all texts as a stream "
"using `list(nlp.tokenizer.pipe(all_texts))`.")
W017 = ("Alias '{alias}' already exists in the Knowledge Base.")
W018 = ("Entity '{entity}' already exists in the Knowledge Base - "
"ignoring the duplicate entry.")
W020 = ("Unnamed vectors. This won't allow multiple vectors models to be "
"loaded. (Shape: {shape})")
W021 = ("Unexpected hash collision in PhraseMatcher. Matches may be "
"incorrect. Modify PhraseMatcher._terminal_hash to fix.")
W022 = ("Training a new part-of-speech tagger using a model with no "
"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, "
"you can ignore this warning. If this is surprising, make sure you "
"have the spacy-lookups-data package installed.")
W024 = ("Entity '{entity}' - Alias '{alias}' combination already exists in "
"the Knowledge Base.")
W026 = ("Unable to set all sentence boundaries from dependency parses.")
W027 = ("Found a large training file of {size} bytes. Note that it may "
"be more efficient to split your training data into multiple "
"smaller JSON files instead.")
W028 = ("Doc.from_array was called with a vector of type '{type}', "
"but is expecting one of type 'uint64' instead. This may result "
"in problems with the vocab further on in the pipeline.")
W030 = ("Some entities could not be aligned in the text \"{text}\" with "
"entities \"{entities}\". Use "
"`spacy.gold.biluo_tags_from_offsets(nlp.make_doc(text), entities)`"
" to check the alignment. Misaligned entities ('-') will be "
"ignored during training.")
W033 = ("Training a new {model} using a model with no lexeme normalization "
"table. This may degrade the performance of the model to some "
"degree. If this is intentional or the language you're using "
"doesn't have a normalization table, please ignore this warning. "
"If this is surprising, make sure you have the spacy-lookups-data "
"package installed. The languages with lexeme normalization tables "
"are currently: {langs}")
# TODO: fix numbering after merging develop into master
W091 = ("Could not clean/remove the temp directory at {dir}: {msg}.")
W092 = ("Ignoring annotations for sentence starts, as dependency heads are set.")
W093 = ("Could not find any data to train the {name} on. Is your "
"input data correctly formatted ?")
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\".")
W101 = ("Skipping `Doc` custom extension '{name}' while merging docs.")
W102 = ("Skipping unsupported user data '{key}: {value}' while merging docs.")
W103 = ("Unknown {lang} word segmenter '{segmenter}'. Supported "
"word segmenters: {supported}. Defaulting to {default}.")
W104 = ("Skipping modifications for '{target}' segmenter. The current "
"segmenter is '{current}'.")
@add_codes
class Errors:
E001 = ("No component '{name}' found in pipeline. Available names: {opts}")
E002 = ("Can't find factory for '{name}' for language {lang} ({lang_code}). "
"This usually happens when spaCy calls nlp.{method} with custom "
"component name that's not registered on the current language class. "
"If you're using a custom component, make sure you've added the "
"decorator @Language.component (for function components) or "
"@Language.factory (for class components).\n\nAvailable "
"factories: {opts}")
E003 = ("Not a valid pipeline component. Expected callable, but "
"got {component} (name: '{name}'). If you're using a custom "
"component factory, double-check that it correctly returns your "
"initialized component.")
E004 = ("Can't set up pipeline component: a factory for '{name}' already exists.")
E005 = ("Pipeline component '{name}' returned None. If you're using a "
"custom component, maybe you forgot to return the processed Doc?")
E006 = ("Invalid constraints for adding pipeline component. You can only "
"set one of the following: before (component name or index), "
"after (component name or index), first (True) or last (True). "
"Invalid configuration: {args}. Existing components: {opts}")
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.select_pipes()`, you should remove them explicitly with "
"`nlp.remove_pipe()` before the pipeline is restored. Names of "
"the new components: {names}")
E010 = ("Word vectors set to length 0. This may be because you don't have "
"a model installed or loaded, or because your model doesn't "
"include word vectors. For more info, see the docs:\n"
"https://spacy.io/usage/models")
E011 = ("Unknown operator: '{op}'. Options: {opts}")
E012 = ("Cannot add pattern for zero tokens to matcher.\nKey: {key}")
E014 = ("Unknown tag ID: {tag}")
E016 = ("MultitaskObjective target should be function or one of: dep, "
"tag, ent, dep_tag_offset, ent_tag.")
E017 = ("Can only add unicode or bytes. Got type: {value_type}")
E018 = ("Can't retrieve string for hash '{hash_value}'. This usually "
"refers to an issue with the `Vocab` or `StringStore`.")
E019 = ("Can't create transition with unknown action ID: {action}. Action "
"IDs are enumerated in spacy/syntax/{src}.pyx.")
E022 = ("Could not find a transition with the name '{name}' in the NER "
"model.")
E024 = ("Could not find an optimal move to supervise the parser. Usually, "
"this means that the model can't be updated in a way that's valid "
"and satisfies the correct annotations specified in the GoldParse. "
"For example, are all labels added to the model? If you're "
"training a named entity recognizer, also make sure that none of "
"your annotated entity spans have leading or trailing whitespace "
"or punctuation. "
"You can also use the experimental `debug data` command to "
"validate your JSON-formatted training data. For details, run:\n"
"python -m spacy debug data --help")
E025 = ("String is too long: {length} characters. Max is 2**30.")
E026 = ("Error accessing token at position {i}: out of bounds in Doc of "
"length {length}.")
E027 = ("Arguments 'words' and 'spaces' should be sequences of the same "
"length, or 'spaces' should be left default at None. spaces "
"should be a sequence of booleans, with True meaning that the "
"word owns a ' ' character following it.")
E028 = ("orths_and_spaces expects either a list of unicode string or a "
"list of (unicode, bool) tuples. Got bytes instance: {value}")
E029 = ("noun_chunks requires the dependency parse, which requires a "
"statistical model to be installed and loaded. For more info, see "
"the documentation:\nhttps://spacy.io/usage/models")
E030 = ("Sentence boundaries unset. You can add the 'sentencizer' "
"component to the pipeline with: "
"nlp.add_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}.")
E032 = ("Conflicting attributes specified in doc.from_array(): "
"(HEAD, SENT_START). The HEAD attribute currently sets sentence "
"boundaries implicitly, based on the tree structure. This means "
"the HEAD attribute would potentially override the sentence "
"boundaries set by SENT_START.")
E033 = ("Cannot load into non-empty Doc of length {length}.")
E035 = ("Error creating span with start {start} and end {end} for Doc of "
"length {length}.")
E036 = ("Error calculating span: Can't find a token starting at character "
"offset {start}.")
E037 = ("Error calculating span: Can't find a token ending at character "
"offset {end}.")
E039 = ("Array bounds exceeded while searching for root word. This likely "
"means the parse tree is in an invalid state. Please report this "
"issue here: http://github.com/explosion/spaCy/issues")
E040 = ("Attempt to access token at {i}, max length {max_length}.")
E041 = ("Invalid comparison operator: {op}. Likely a Cython bug?")
E042 = ("Error accessing doc[{i}].nbor({j}), for doc of length {length}.")
E043 = ("Refusing to write to token.sent_start if its document is parsed, "
"because this may cause inconsistent state.")
E044 = ("Invalid value for token.sent_start: {value}. Must be one of: "
"None, True, False")
E045 = ("Possibly infinite loop encountered while looking for {attr}.")
E046 = ("Can't retrieve unregistered extension attribute '{name}'. Did "
"you forget to call the `set_extension` method?")
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}")
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 {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 "
"original string.\nKey: {key}\nOrths: {orths}")
E057 = ("Stepped slices not supported in Span objects. Try: "
"list(tokens)[start:stop:step] instead.")
E058 = ("Could not retrieve vector for key {key}.")
E059 = ("One (and only one) keyword arg must be set. Got: {kwargs}")
E060 = ("Cannot add new key to vectors: the table is full. Current shape: "
"({rows}, {cols}).")
E062 = ("Cannot find empty bit for new lexical flag. All bits between 0 "
"and 63 are occupied. You can replace one by specifying the "
"`flag_id` explicitly, e.g. "
"`nlp.vocab.add_flag(your_func, flag_id=IS_ALPHA`.")
E063 = ("Invalid value for flag_id: {value}. Flag IDs must be between 1 "
"and 63 (inclusive).")
E064 = ("Error fetching a Lexeme from the Vocab. When looking up a "
"string, the lexeme returned had an orth ID that did not match "
"the query string. This means that the cached lexeme structs are "
"mismatched to the string encoding table. The mismatched:\n"
"Query string: {string}\nOrth cached: {orth}\nOrth ID: {orth_id}")
E065 = ("Only one of the vector table's width and shape can be specified. "
"Got width {width} and shape {shape}.")
E067 = ("Invalid BILUO tag sequence: Got a tag starting with 'I' (inside "
"an entity) without a preceding 'B' (beginning of an entity). "
"Tag sequence:\n{tags}")
E068 = ("Invalid BILUO tag: '{tag}'.")
E071 = ("Error creating lexeme: specified orth ID ({orth}) does not "
"match the one in the vocab ({vocab_orth}).")
E073 = ("Cannot assign vector of length {new_length}. Existing vectors "
"are of length {length}. You can use `vocab.reset_vectors` to "
"clear the existing vectors and resize the table.")
E074 = ("Error interpreting compiled match pattern: patterns are expected "
"to end with the attribute {attr}. Got: {bad_attr}.")
E082 = ("Error deprojectivizing parse: number of heads ({n_heads}), "
"projective heads ({n_proj_heads}) and labels ({n_labels}) do not "
"match.")
E083 = ("Error setting extension: only one of `default`, `method`, or "
"`getter` (plus optional `setter`) is allowed. Got: {nr_defined}")
E084 = ("Error assigning label ID {label} to span: not in StringStore.")
E085 = ("Can't create lexeme for string '{string}'.")
E087 = ("Unknown displaCy style: {style}.")
E088 = ("Text of length {length} exceeds maximum of {max_length}. The "
"v2.x parser and NER models require roughly 1GB of temporary "
"memory per 100,000 characters in the input. This means long "
"texts may cause memory allocation errors. If you're not using "
"the parser or NER, it's probably safe to increase the "
"`nlp.max_length` limit. The limit is in number of characters, so "
"you can check whether your inputs are too long by checking "
"`len(text)`.")
E089 = ("Extensions can't have a setter argument without a getter "
"argument. Check the keyword arguments on `set_extension`.")
E090 = ("Extension '{name}' already exists on {obj}. To overwrite the "
"existing extension, set `force=True` on `{obj}.set_extension`.")
E091 = ("Invalid extension attribute {name}: expected callable or None, "
"but got: {value}")
E092 = ("Could not find or assign name for word vectors. Ususally, the "
"name is read from the model's meta.json in vector.name. "
"Alternatively, it is built from the 'lang' and 'name' keys in "
"the meta.json. Vector names are required to avoid issue #1660.")
E093 = ("token.ent_iob values make invalid sequence: I without B\n{seq}")
E094 = ("Error reading line {line_num} in vectors file {loc}.")
E095 = ("Can't write to frozen dictionary. This is likely an internal "
"error. Are you writing to a default function argument?")
E096 = ("Invalid object passed to displaCy: Can only visualize Doc or "
"Span objects, or dicts if set to manual=True.")
E097 = ("Invalid pattern: expected token pattern (list of dicts) or "
"phrase pattern (string) but got:\n{pattern}")
E098 = ("Invalid pattern specified: expected both SPEC and PATTERN.")
E099 = ("First node of pattern should be a root node. The root should "
"only contain NODE_NAME.")
E100 = ("Nodes apart from the root should contain NODE_NAME, NBOR_NAME and "
"NBOR_RELOP.")
E101 = ("NODE_NAME should be a new node and NBOR_NAME should already have "
"have been declared in previous edges.")
E102 = ("Can't merge non-disjoint spans. '{token}' is already part of "
"tokens to merge. If you want to find the longest non-overlapping "
"spans, you can use the util.filter_spans helper:\n"
"https://spacy.io/api/top-level#util.filter_spans")
E103 = ("Trying to set conflicting doc.ents: '{span1}' and '{span2}'. A "
"token can only be part of one entity, so make sure the entities "
"you're setting don't overlap.")
E106 = ("Can't find doc._.{attr} attribute specified in the underscore "
"settings: {opts}")
E107 = ("Value of doc._.{attr} is not JSON-serializable: {value}")
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 "
"would always have to include its Doc and Vocab, which has "
"practically no advantage over pickling the parent Doc directly. "
"So instead of pickling the token, pickle the Doc it belongs to.")
E112 = ("Pickling a span is not supported, because spans are only views "
"of the parent Doc and can't exist on their own. A pickled span "
"would always have to include its Doc and Vocab, which has "
"practically no advantage over pickling the parent Doc directly. "
"So instead of pickling the span, pickle the Doc it belongs to or "
"use Span.as_doc to convert the span to a standalone Doc object.")
E115 = ("All subtokens must have associated heads.")
E116 = ("Cannot currently add labels to pretrained text classifier. Add "
"labels before training begins. This functionality was available "
"in previous versions, but had significant bugs that led to poor "
"performance.")
E117 = ("The newly split tokens must match the text of the original token. "
"New orths: {new}. Old text: {old}.")
E118 = ("The custom extension attribute '{attr}' is not registered on the "
"Token object so it can't be set during retokenization. To "
"register an attribute, use the Token.set_extension classmethod.")
E119 = ("Can't set custom extension attribute '{attr}' during "
"retokenization because it's not writable. This usually means it "
"was registered with a getter function (and no setter) or as a "
"method extension, so the value is computed dynamically. To "
"overwrite a custom attribute manually, it should be registered "
"with a default value or with a getter AND setter.")
E120 = ("Can't set custom extension attributes during retokenization. "
"Expected dict mapping attribute names to values, but got: {value}")
E121 = ("Can't bulk merge spans. Attribute length {attr_len} should be "
"equal to span length ({span_len}).")
E122 = ("Cannot find token to be split. Did it get merged?")
E123 = ("Cannot find head of token to be split. Did it get merged?")
E125 = ("Unexpected value: {value}")
E126 = ("Unexpected matcher predicate: '{bad}'. Expected one of: {good}. "
"This is likely a bug in spaCy, so feel free to open an issue.")
E129 = ("Cannot write the label of an existing Span object because a Span "
"is a read-only view of the underlying Token objects stored in the "
"Doc. Instead, create a new Span object and specify the `label` "
"keyword argument, for example:\nfrom spacy.tokens import Span\n"
"span = Span(doc, start={start}, end={end}, label='{label}')")
E130 = ("You are running a narrow unicode build, which is incompatible "
"with spacy >= 2.1.0. To fix this, reinstall Python and use a wide "
"unicode build instead. You can also rebuild Python and set the "
"--enable-unicode=ucs4 flag.")
E131 = ("Cannot write the kb_id of an existing Span object because a Span "
"is a read-only view of the underlying Token objects stored in "
"the Doc. Instead, create a new Span object and specify the "
"`kb_id` keyword argument, for example:\nfrom spacy.tokens "
"import Span\nspan = Span(doc, start={start}, end={end}, "
"label='{label}', kb_id='{kb_id}')")
E132 = ("The vectors for entities and probabilities for alias '{alias}' "
"should have equal length, but found {entities_length} and "
"{probabilities_length} respectively.")
E133 = ("The sum of prior probabilities for alias '{alias}' should not "
"exceed 1, but found {sum}.")
E134 = ("Entity '{entity}' is not defined in the Knowledge Base.")
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"
"https://spacy.io/api/cli#pretrain-jsonl")
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}' is empty. Use the methods "
"kb.add_entity and kb.add_alias to add entries.")
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}.")
E143 = ("Labels for component '{name}' not initialized. Did you forget to "
"call add_label()?")
E145 = ("Error reading `{param}` from input file.")
E146 = ("Could not access `{path}`.")
E147 = ("Unexpected error in the {method} functionality of the "
"EntityLinker: {msg}. This is likely a bug in spaCy, so feel free "
"to open an issue.")
E148 = ("Expected {ents} KB identifiers but got {ids}. Make sure that "
"each entity in `doc.ents` is assigned to a KB identifier.")
E149 = ("Error deserializing model. Check that the config used to create "
"the component matches the model being loaded.")
E150 = ("The language of the `nlp` object and the `vocab` should be the "
"same, but found '{nlp}' and '{vocab}' respectively.")
E152 = ("The attribute {attr} is not supported for token patterns. "
"Please use the option validate=True with Matcher, PhraseMatcher, "
"or EntityRuler for more details.")
E153 = ("The value type {vtype} is not supported for token patterns. "
"Please use the option validate=True with Matcher, PhraseMatcher, "
"or EntityRuler for more details.")
E154 = ("One of the attributes or values is not supported for token "
"patterns. Please use the option validate=True with Matcher, "
"PhraseMatcher, or EntityRuler for more details.")
E155 = ("The pipeline needs to include a tagger in order to use "
"Matcher or PhraseMatcher with the attributes POS, TAG, or LEMMA. "
"Try using nlp() instead of nlp.make_doc() or list(nlp.pipe()) "
"instead of list(nlp.tokenizer.pipe()).")
E156 = ("The pipeline needs to include a parser in order to use "
"Matcher or PhraseMatcher with the attribute DEP. Try using "
"nlp() instead of nlp.make_doc() or list(nlp.pipe()) instead of "
"list(nlp.tokenizer.pipe()).")
E157 = ("Can't render negative values for dependency arc start or end. "
"Make sure that you're passing in absolute token indices, not "
"relative token offsets.\nstart: {start}, end: {end}, label: "
"{label}, direction: {dir}")
E158 = ("Can't add table '{name}' to lookups because it already exists.")
E159 = ("Can't find table '{name}' in lookups. Available tables: {tables}")
E160 = ("Can't find language data file: {path}")
E161 = ("Found an internal inconsistency when predicting entity links. "
"This is likely a bug in spaCy, so feel free to open an issue.")
E162 = ("Cannot evaluate textcat model on data with different labels.\n"
"Labels in model: {model_labels}\nLabels in evaluation "
"data: {eval_labels}")
E163 = ("cumsum was found to be unstable: its last element does not "
"correspond to sum")
E164 = ("x is neither increasing nor decreasing: {}.")
E165 = ("Only one class present in y_true. ROC AUC score is not defined in "
"that case.")
E166 = ("Can only merge DocBins with the same pre-defined attributes.\n"
"Current DocBin: {current}\nOther DocBin: {other}")
E169 = ("Can't find module: {module}")
E170 = ("Cannot apply transition {name}: invalid for the current state.")
E171 = ("Matcher.add received invalid 'on_match' callback argument: expected "
"callable or None, but got: {arg_type}")
E175 = ("Can't remove rule for unknown match pattern ID: {key}")
E176 = ("Alias '{alias}' is not defined in the Knowledge Base.")
E177 = ("Ill-formed IOB input detected: {tag}")
E178 = ("Each pattern should be a list of dicts, but got: {pat}. Maybe you "
"accidentally passed a single pattern to Matcher.add instead of a "
"list of patterns? If you only want to add one pattern, make sure "
"to wrap it in a list. For example: matcher.add('{key}', [pattern])")
E179 = ("Invalid pattern. Expected a list of Doc objects but got a single "
"Doc. If you only want to add one pattern, make sure to wrap it "
"in a list. For example: matcher.add('{key}', [doc])")
E180 = ("Span attributes can't be declared as required or assigned by "
"components, since spans are only views of the Doc. Use Doc and "
"Token attributes (or custom extension attributes) only and remove "
"the following: {attrs}")
E181 = ("Received invalid attributes for unkown object {obj}: {attrs}. "
"Only Doc and Token attributes are supported.")
E182 = ("Received invalid attribute declaration: {attr}\nDid you forget "
"to define the attribute? For example: {attr}.???")
E183 = ("Received invalid attribute declaration: {attr}\nOnly top-level "
"attributes are supported, for example: {solution}")
E184 = ("Only attributes without underscores are supported in component "
"attribute declarations (because underscore and non-underscore "
"attributes are connected anyways): {attr} -> {solution}")
E185 = ("Received invalid attribute in component attribute declaration: "
"{obj}.{attr}\nAttribute '{attr}' does not exist on {obj}.")
E186 = ("'{tok_a}' and '{tok_b}' are different texts.")
E187 = ("Only unicode strings are supported as labels.")
E189 = ("Each argument to `get_doc` should be of equal length.")
E190 = ("Token head out of range in `Doc.from_array()` for token index "
"'{index}' with value '{value}' (equivalent to relative head "
"index: '{rel_head_index}'). The head indices should be relative "
"to the current token index rather than absolute indices in the "
"array.")
E191 = ("Invalid head: the head token must be from the same doc as the "
"token itself.")
E192 = ("Unable to resize vectors in place with cupy.")
E193 = ("Unable to resize vectors in place if the resized vector dimension "
"({new_dim}) is not the same as the current vector dimension "
"({curr_dim}).")
E194 = ("Unable to aligned mismatched text '{text}' and words '{words}'.")
E195 = ("Matcher can be called on {good} only, got {got}.")
E196 = ("Refusing to write to token.is_sent_end. Sentence boundaries can "
"only be fixed with token.is_sent_start.")
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}].")
# TODO: fix numbering after merging develop into master
E941 = ("Can't find model '{name}'. It looks like you're trying to load a "
"model from a shortcut, which is deprecated as of spaCy v3.0. To "
"load the model, use its full name instead:\n\n"
"nlp = spacy.load(\"{full}\")\n\nFor more details on the available "
"models, see the models directory: https://spacy.io/models. If you "
"want to create a blank model, use spacy.blank: "
"nlp = spacy.blank(\"{name}\")")
E942 = ("Executing after_{name} callback failed. Expected the function to "
"return an initialized nlp object but got: {value}. Maybe "
"you forgot to return the modified object in your function?")
E943 = ("Executing before_creation callback failed. Expected the function to "
"return an uninitialized Language subclass but got: {value}. Maybe "
"you forgot to return the modified object in your function or "
"returned the initialized nlp object instead?")
E944 = ("Can't copy pipeline component '{name}' from source model '{model}': "
"not found in pipeline. Available components: {opts}")
E945 = ("Can't copy pipeline component '{name}' from source. Expected loaded "
"nlp object, but got: {source}")
E946 = ("The Vocab for the knowledge base is not initialized. Did you forget to "
"call kb.initialize()?")
E947 = ("Matcher.add received invalid 'greedy' argument: expected "
"a string value from {expected} but got: '{arg}'")
E948 = ("Matcher.add received invalid 'patterns' argument: expected "
"a List, but got: {arg_type}")
E949 = ("Can only create an alignment when the texts are the same.")
E952 = ("The section '{name}' is not a valid section in the provided config.")
E953 = ("Mismatched IDs received by the Tok2Vec listener: {id1} vs. {id2}")
E954 = ("The Tok2Vec listener did not receive a valid input.")
E955 = ("Can't find table '{table}' for language '{lang}' in spacy-lookups-data.")
E956 = ("Can't find component '{name}' in [components] block in the config. "
"Available components: {opts}")
E957 = ("Writing directly to Language.factories isn't needed anymore in "
"spaCy v3. Instead, you can use the @Language.factory decorator "
"to register your custom component factory or @Language.component "
"to register a simple stateless function component that just takes "
"a Doc and returns it.")
E958 = ("Language code defined in config ({bad_lang_code}) does not match "
"language code of current Language subclass {lang} ({lang_code})")
E959 = ("Can't insert component {dir} index {idx}. Existing components: {opts}")
E960 = ("No config data found for component '{name}'. This is likely a bug "
"in spaCy.")
E961 = ("Found non-serializable Python object in config. Configs should "
"only include values that can be serialized to JSON. If you need "
"to pass models or other objects to your component, use a reference "
"to a registered function or initialize the object in your "
"component.\n\n{config}")
E962 = ("Received incorrect {style} for pipe '{name}'. Expected dict, "
"got: {cfg_type}.")
E963 = ("Can't read component info from @Language.{decorator} decorator. "
"Maybe you forgot to call it? Make sure you're using "
"@Language.{decorator}() instead of @Language.{decorator}.")
E964 = ("The pipeline component factory for '{name}' needs to have the "
"following named arguments, which are passed in by spaCy:\n- nlp: "
"receives the current nlp object and lets you access the vocab\n- "
"name: the name of the component instance, can be used to identify "
"the component, output losses etc.")
E965 = ("It looks like you're using the @Language.component decorator to "
"register '{name}' on a class instead of a function component. If "
"you need to register a class or function that *returns* a component "
"function, use the @Language.factory decorator instead.")
E966 = ("nlp.add_pipe now takes the string name of the registered component "
"factory, not a callable component. Expected string, but got "
"{component} (name: '{name}').\n\n- If you created your component "
"with nlp.create_pipe('name'): remove nlp.create_pipe and call "
"nlp.add_pipe('name') instead.\n\n- If you passed in a component "
"like TextCategorizer(): call nlp.add_pipe with the string name "
"instead, e.g. nlp.add_pipe('textcat').\n\n- If you're using a custom "
"component: Add the decorator @Language.component (for function "
"components) or @Language.factory (for class components / factories) "
"to your custom component and assign it a name, e.g. "
"@Language.component('your_name'). You can then run "
"nlp.add_pipe('your_name') to add it to the pipeline.")
E967 = ("No {meta} meta information found for '{name}'. This is likely a bug in spaCy.")
E968 = ("nlp.replace_pipe now takes the string name of the registered component "
"factory, not a callable component. Expected string, but got "
"{component}.\n\n- If you created your component with"
"with nlp.create_pipe('name'): remove nlp.create_pipe and call "
"nlp.replace_pipe('{name}', 'name') instead.\n\n- If you passed in a "
"component like TextCategorizer(): call nlp.replace_pipe with the "
"string name instead, e.g. nlp.replace_pipe('{name}', 'textcat').\n\n"
"- If you're using a custom component: Add the decorator "
"@Language.component (for function components) or @Language.factory "
"(for class components / factories) to your custom component and "
"assign it a name, e.g. @Language.component('your_name'). You can "
"then run nlp.replace_pipe('{name}', 'your_name').")
E969 = ("Expected string values for field '{field}', but received {types} instead. ")
E970 = ("Can not execute command '{str_command}'. Do you have '{tool}' installed?")
E971 = ("Found incompatible lengths in Doc.from_array: {array_length} for the "
"array and {doc_length} for the Doc itself.")
E972 = ("Example.__init__ got None for '{arg}'. Requires Doc.")
E973 = ("Unexpected type for NER data")
E974 = ("Unknown {obj} attribute: {key}")
E976 = ("The method 'Example.from_dict' expects a {type} as {n} argument, "
"but received None.")
E977 = ("Can not compare a MorphAnalysis with a string object. "
"This is likely a bug in spaCy, so feel free to open an issue.")
E978 = ("The '{method}' method of {name} takes a list of Example objects, "
"but found {types} instead.")
E979 = ("Cannot convert {type} to an Example object.")
E980 = ("Each link annotation should refer to a dictionary with at most one "
"identifier mapping to 1.0, and all others to 0.0.")
E981 = ("The offsets of the annotations for 'links' could not be aligned "
"to token boundaries.")
E982 = ("The 'ent_iob' attribute of a Token should be an integer indexing "
"into {values}, but found {value}.")
E983 = ("Invalid key for '{dict}': {key}. Available keys: "
"{keys}")
E984 = ("Invalid component config for '{name}': component block needs either "
"a key 'factory' specifying the registered function used to "
"initialize the component, or a key 'source' key specifying a "
"spaCy model to copy the component from. For example, factory = "
"\"ner\" will use the 'ner' factory and all other settings in the "
"block will be passed to it as arguments. Alternatively, source = "
"\"en_core_web_sm\" will copy the component from that model.\n\n{config}")
E985 = ("Can't load model from config file: no 'nlp' section found.\n\n{config}")
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' needs to include a key 'lang' specifying "
"the code of the language to initialize it with (for example "
"'en' for English) - this can't be 'None'.\n\n{config}")
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}'.")
E999 = ("Unable to merge the `Doc` objects because they do not all share "
"the same `Vocab`.")
E1000 = ("No pkuseg model available. Provide a pkuseg model when "
"initializing the pipeline:\n"
'cfg = {"tokenizer": {"segmenter": "pkuseg", "pkuseg_model": name_or_path}}\n'
'nlp = Chinese(config=cfg)')
E1001 = ("Target token outside of matched span for match with tokens "
"'{span}' and offset '{index}' matched by patterns '{patterns}'.")
E1002 = ("Span index out of range.")
@add_codes
class TempErrors:
T003 = ("Resizing pretrained Tagger models is not currently supported.")
T007 = ("Can't yet set {attr} from Span. Vote for this feature on the "
"issue tracker: http://github.com/explosion/spaCy/issues")
# Deprecated model shortcuts, only used in errors and warnings
OLD_MODEL_SHORTCUTS = {
"en": "en_core_web_sm", "de": "de_core_news_sm", "es": "es_core_news_sm",
"pt": "pt_core_news_sm", "fr": "fr_core_news_sm", "it": "it_core_news_sm",
"nl": "nl_core_news_sm", "el": "el_core_news_sm", "nb": "nb_core_news_sm",
"lt": "lt_core_news_sm", "xx": "xx_ent_wiki_sm"
}
# fmt: on
class MatchPatternError(ValueError):
def __init__(self, key, errors):
"""Custom error for validating match patterns.
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 = f"Invalid token patterns for matcher rule '{key}'\n"
for pattern_idx, error_msgs in errors.items():
pattern_errors = "\n".join([f"- {e}" for e in error_msgs])
msg += f"\nPattern {pattern_idx}:\n{pattern_errors}\n"
ValueError.__init__(self, msg)