mirror of https://github.com/explosion/spaCy.git
620 lines
39 KiB
Python
620 lines
39 KiB
Python
def add_codes(err_cls):
|
|
"""Add error codes to string messages via class attribute names."""
|
|
|
|
class ErrorsWithCodes(object):
|
|
def __getattribute__(self, code):
|
|
msg = getattr(err_cls, code)
|
|
return f"[{code}] {msg}"
|
|
|
|
return ErrorsWithCodes()
|
|
|
|
|
|
# fmt: off
|
|
|
|
@add_codes
|
|
class Warnings(object):
|
|
W001 = ("As of spaCy v2.0, the keyword argument `path=` is deprecated. "
|
|
"You can now call spacy.load with the path as its first argument, "
|
|
"and the model's meta.json will be used to determine the language "
|
|
"to load. For example:\nnlp = spacy.load('{path}')")
|
|
W002 = ("Tokenizer.from_list is now deprecated. Create a new Doc object "
|
|
"instead and pass in the strings as the `words` keyword argument, "
|
|
"for example:\nfrom spacy.tokens import Doc\n"
|
|
"doc = Doc(nlp.vocab, words=[...])")
|
|
W003 = ("Positional arguments to Doc.merge are deprecated. Instead, use "
|
|
"the keyword arguments, for example tag=, lemma= or ent_type=.")
|
|
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.")
|
|
W009 = ("Custom factory '{name}' provided by entry points of another "
|
|
"package overwrites built-in factory.")
|
|
W010 = ("As of v2.1.0, the PhraseMatcher doesn't have a phrase length "
|
|
"limit anymore, so the max_length argument is now deprecated. "
|
|
"If you did not specify this parameter, make sure you call the "
|
|
"constructor with named arguments instead of positional ones.")
|
|
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))`.")
|
|
W013 = ("As of v2.1.0, {obj}.merge is deprecated. Please use the more "
|
|
"efficient and less error-prone Doc.retokenize context manager "
|
|
"instead.")
|
|
W014 = ("As of v2.1.0, the `disable` keyword argument on the serialization "
|
|
"methods is and should be replaced with `exclude`. This makes it "
|
|
"consistent with the other serializable objects.")
|
|
W015 = ("As of v2.1.0, the use of keyword arguments to exclude fields from "
|
|
"being serialized or deserialized is deprecated. Please use the "
|
|
"`exclude` argument instead. For example: exclude=['{arg}'].")
|
|
W016 = ("The keyword argument `n_threads` is now deprecated. As of v2.2.2, "
|
|
"the argument `n_process` controls parallel inference via "
|
|
"multiprocessing.")
|
|
W017 = ("Alias '{alias}' already exists in the Knowledge Base.")
|
|
W018 = ("Entity '{entity}' already exists in the Knowledge Base - "
|
|
"ignoring the duplicate entry.")
|
|
W019 = ("Changing vectors name from {old} to {new}, to avoid clash with "
|
|
"previously loaded vectors. See Issue #3853.")
|
|
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.")
|
|
W025 = ("'{name}' requires '{attr}' to be assigned, but none of the "
|
|
"previous components in the pipeline declare that they assign it.")
|
|
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.")
|
|
W029 = ("Skipping unsupported morphological feature(s): {feature}. "
|
|
"Provide features as a dict {{\"Field1\": \"Value1,Value2\"}} or "
|
|
"string \"Field1=Value1,Value2|Field2=Value3\".")
|
|
|
|
# TODO: fix numbering after merging develop into master
|
|
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.")
|
|
|
|
|
|
@add_codes
|
|
class Errors(object):
|
|
E001 = ("No component '{name}' found in pipeline. Available names: {opts}")
|
|
E002 = ("Can't find factory for '{name}'. This usually happens when spaCy "
|
|
"calls `nlp.create_pipe` with a component name that's not built "
|
|
"in - for example, when constructing the pipeline from a model's "
|
|
"meta.json. If you're using a custom component, you can write to "
|
|
"`Language.factories['{name}']` or remove it from the model meta "
|
|
"and add it via `nlp.add_pipe` instead.")
|
|
E003 = ("Not a valid pipeline component. Expected callable, but "
|
|
"got {component} (name: '{name}').")
|
|
E004 = ("If you meant to add a built-in component, use `create_pipe`: "
|
|
"`nlp.add_pipe(nlp.create_pipe('{component}'))`")
|
|
E005 = ("Pipeline component '{name}' returned None. If you're using a "
|
|
"custom component, maybe you forgot to return the processed Doc?")
|
|
E006 = ("Invalid constraints. You can only set one of the following: "
|
|
"before, after, first, last.")
|
|
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}")
|
|
E009 = ("The `update` method expects same number of docs and golds, but "
|
|
"got: {n_docs} docs, {n_golds} golds.")
|
|
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}")
|
|
E013 = ("Error selecting action in matcher")
|
|
E014 = ("Unknown tag ID: {tag}")
|
|
E015 = ("Conflicting morphology exception for ({tag}, {orth}). Use "
|
|
"`force=True` to overwrite.")
|
|
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.")
|
|
E020 = ("Could not find a gold-standard action to supervise the "
|
|
"dependency parser. The tree is non-projective (i.e. it has "
|
|
"crossing arcs - see spacy/syntax/nonproj.pyx for definitions). "
|
|
"The ArcEager transition system only supports projective trees. "
|
|
"To learn non-projective representations, transform the data "
|
|
"before training and after parsing. Either pass "
|
|
"`make_projective=True` to the GoldParse class, or use "
|
|
"spacy.syntax.nonproj.preprocess_training_data.")
|
|
E021 = ("Could not find a gold-standard action to supervise the "
|
|
"dependency parser. The GoldParse was projective. The transition "
|
|
"system has {n_actions} actions. State at failure: {state}")
|
|
E022 = ("Could not find a transition with the name '{name}' in the NER "
|
|
"model.")
|
|
E023 = ("Error cleaning up beam: The same state occurred twice at "
|
|
"memory address {addr} and position {i}.")
|
|
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(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}.")
|
|
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}.")
|
|
E034 = ("Doc.merge received {n_args} non-keyword arguments. Expected "
|
|
"either 3 arguments (deprecated), or 0 (use keyword arguments).\n"
|
|
"Arguments supplied:\n{args}\nKeyword arguments:{kwargs}")
|
|
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}.")
|
|
E038 = ("Error finding sentence for span. Infinite loop detected.")
|
|
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}).")
|
|
E061 = ("Bad file name: {filename}. Example of a valid file name: "
|
|
"'vectors.128.f.bin'")
|
|
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}.")
|
|
E066 = ("Error creating model helper for extracting columns. Can only "
|
|
"extract columns by positive integer. Got: {value}.")
|
|
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}'.")
|
|
E069 = ("Invalid gold-standard parse tree. Found cycle between word "
|
|
"IDs: {cycle} (tokens: {cycle_tokens}) in the document starting "
|
|
"with tokens: {doc_tokens}.")
|
|
E070 = ("Invalid gold-standard data. Number of documents ({n_docs}) "
|
|
"does not align with number of annotations ({n_annots}).")
|
|
E071 = ("Error creating lexeme: specified orth ID ({orth}) does not "
|
|
"match the one in the vocab ({vocab_orth}).")
|
|
E072 = ("Error serializing lexeme: expected data length {length}, "
|
|
"got {bad_length}.")
|
|
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}.")
|
|
E075 = ("Error accepting match: length ({length}) > maximum length "
|
|
"({max_len}).")
|
|
E076 = ("Error setting tensor on Doc: tensor has {rows} rows, while Doc "
|
|
"has {words} words.")
|
|
E077 = ("Error computing {value}: number of Docs ({n_docs}) does not "
|
|
"equal number of GoldParse objects ({n_golds}) in batch.")
|
|
E078 = ("Error computing score: number of words in Doc ({words_doc}) does "
|
|
"not equal number of words in GoldParse ({words_gold}).")
|
|
E079 = ("Error computing states in beam: number of predicted beams "
|
|
"({pbeams}) does not equal number of gold beams ({gbeams}).")
|
|
E080 = ("Duplicate state found in beam: {key}.")
|
|
E081 = ("Error getting gradient in beam: number of histories ({n_hist}) "
|
|
"does not equal number of losses ({losses}).")
|
|
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}'.")
|
|
E086 = ("Error deserializing lexeme '{string}': orth ID {orth_id} does "
|
|
"not match hash {hash_id} in StringStore.")
|
|
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.")
|
|
E104 = ("Can't find JSON schema for '{name}'.")
|
|
E105 = ("The Doc.print_tree() method is now deprecated. Please use "
|
|
"Doc.to_json() instead or write your own function.")
|
|
E106 = ("Can't find doc._.{attr} attribute specified in the underscore "
|
|
"settings: {opts}")
|
|
E107 = ("Value of doc._.{attr} is not JSON-serializable: {value}")
|
|
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 = ("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.")
|
|
E113 = ("The newly split token can only have one root (head = 0).")
|
|
E114 = ("The newly split token needs to have a root (head = 0).")
|
|
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?")
|
|
E124 = ("Cannot read from file: {path}. Supported formats: {formats}")
|
|
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.")
|
|
E127 = ("Cannot create phrase pattern representation for length 0. This "
|
|
"is likely a bug in spaCy.")
|
|
E128 = ("Unsupported serialization argument: '{arg}'. The use of keyword "
|
|
"arguments to exclude fields from being serialized or deserialized "
|
|
"is now deprecated. Please use the `exclude` argument instead. "
|
|
"For example: exclude=['{arg}'].")
|
|
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.")
|
|
E135 = ("If you meant to replace a built-in component, use `create_pipe`: "
|
|
"`nlp.replace_pipe('{name}', nlp.create_pipe('{name}'))`")
|
|
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.")
|
|
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()?")
|
|
E144 = ("Could not find parameter `{param}` when building the entity "
|
|
"linker model.")
|
|
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.")
|
|
E151 = ("Trying to call nlp.update without required annotation types. "
|
|
"Expected top-level keys: {exp}. Got: {unexp}.")
|
|
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}")
|
|
E167 = ("Unknown morphological feature: '{feat}' ({feat_id}). This can "
|
|
"happen if the tagger was trained with a different set of "
|
|
"morphological features. If you're using a pretrained model, make "
|
|
"sure that your models are up to date:\npython -m spacy validate")
|
|
E168 = ("Unknown field: {field}")
|
|
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}")
|
|
E172 = ("The Lemmatizer.load classmethod is deprecated. To create a "
|
|
"Lemmatizer, initialize the class directly. See the docs for "
|
|
"details: https://spacy.io/api/lemmatizer")
|
|
E173 = ("As of v2.2, the Lemmatizer is initialized with an instance of "
|
|
"Lookups containing the lemmatization tables. See the docs for "
|
|
"details: https://spacy.io/api/lemmatizer#init")
|
|
E174 = ("Architecture '{name}' not found in registry. Available "
|
|
"names: {names}")
|
|
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 = ("Invalid pattern. Expected 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.")
|
|
E188 = ("Could not match the gold entity links to entities in the doc - "
|
|
"make sure the gold EL data refers to valid results of the "
|
|
"named entity recognizer in the `nlp` pipeline.")
|
|
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.")
|
|
|
|
# TODO: fix numbering after merging develop into master
|
|
|
|
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):
|
|
T003 = ("Resizing pretrained Tagger models is not currently supported.")
|
|
T004 = ("Currently parser depth is hard-coded to 1. Received: {value}.")
|
|
T007 = ("Can't yet set {attr} from Span. Vote for this feature on the "
|
|
"issue tracker: http://github.com/explosion/spaCy/issues")
|
|
T008 = ("Bad configuration of Tagger. This is probably a bug within "
|
|
"spaCy. We changed the name of an internal attribute for loading "
|
|
"pretrained vectors, and the class has been passed the old name "
|
|
"(pretrained_dims) but not the new name (pretrained_vectors).")
|
|
|
|
|
|
# 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)
|
|
|
|
|
|
class AlignmentError(ValueError):
|
|
pass
|