mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 04:08:09 +03:00
595f9dc2e4
This was the only registry that expected the registered objects to be dictionaries instead of functions that return something. We can still support plain dicts but we should also support functions for consistency
691 lines
44 KiB
Python
691 lines
44 KiB
Python
def add_codes(err_cls):
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"""Add error codes to string messages via class attribute names."""
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class ErrorsWithCodes(err_cls):
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def __getattribute__(self, code):
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msg = super(ErrorsWithCodes, self).__getattribute__(code)
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if code.startswith("__"): # python system attributes like __class__
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return msg
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else:
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return "[{code}] {msg}".format(code=code, msg=msg)
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return ErrorsWithCodes()
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# fmt: off
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@add_codes
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class Warnings:
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W004 = ("No text fixing enabled. Run `pip install ftfy` to enable fixing "
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"using ftfy.fix_text if necessary.")
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W005 = ("Doc object not parsed. This means displaCy won't be able to "
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"generate a dependency visualization for it. Make sure the Doc "
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"was processed with a model that supports dependency parsing, and "
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"not just a language class like `English()`. For more info, see "
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"the docs:\nhttps://spacy.io/usage/models")
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W006 = ("No entities to visualize found in Doc object. If this is "
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"surprising to you, make sure the Doc was processed using a model "
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"that supports named entity recognition, and check the `doc.ents` "
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"property manually if necessary.")
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W007 = ("The model you're using has no word vectors loaded, so the result "
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"of the {obj}.similarity method will be based on the tagger, "
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"parser and NER, which may not give useful similarity judgements. "
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"This may happen if you're using one of the small models, e.g. "
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"`en_core_web_sm`, which don't ship with word vectors and only "
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"use context-sensitive tensors. You can always add your own word "
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"vectors, or use one of the larger models instead if available.")
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W008 = ("Evaluating {obj}.similarity based on empty vectors.")
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W011 = ("It looks like you're calling displacy.serve from within a "
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"Jupyter notebook or a similar environment. This likely means "
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"you're already running a local web server, so there's no need to "
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"make displaCy start another one. Instead, you should be able to "
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"replace displacy.serve with displacy.render to show the "
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"visualization.")
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W012 = ("A Doc object you're adding to the PhraseMatcher for pattern "
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"'{key}' is parsed and/or tagged, but to match on '{attr}', you "
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"don't actually need this information. This means that creating "
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"the patterns is potentially much slower, because all pipeline "
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"components are applied. To only create tokenized Doc objects, "
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"try using `nlp.make_doc(text)` or process all texts as a stream "
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"using `list(nlp.tokenizer.pipe(all_texts))`.")
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W017 = ("Alias '{alias}' already exists in the Knowledge Base.")
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W018 = ("Entity '{entity}' already exists in the Knowledge Base - "
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"ignoring the duplicate entry.")
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W020 = ("Unnamed vectors. This won't allow multiple vectors models to be "
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"loaded. (Shape: {shape})")
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W021 = ("Unexpected hash collision in PhraseMatcher. Matches may be "
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"incorrect. Modify PhraseMatcher._terminal_hash to fix.")
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W024 = ("Entity '{entity}' - Alias '{alias}' combination already exists in "
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"the Knowledge Base.")
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W026 = ("Unable to set all sentence boundaries from dependency parses.")
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W027 = ("Found a large training file of {size} bytes. Note that it may "
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"be more efficient to split your training data into multiple "
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"smaller JSON files instead.")
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W028 = ("Doc.from_array was called with a vector of type '{type}', "
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"but is expecting one of type 'uint64' instead. This may result "
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"in problems with the vocab further on in the pipeline.")
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W030 = ("Some entities could not be aligned in the text \"{text}\" with "
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"entities \"{entities}\". Use "
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"`spacy.gold.biluo_tags_from_offsets(nlp.make_doc(text), entities)`"
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" to check the alignment. Misaligned entities ('-') will be "
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"ignored during training.")
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W033 = ("Training a new {model} using a model with no lexeme normalization "
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"table. This may degrade the performance of the model to some "
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"degree. If this is intentional or the language you're using "
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"doesn't have a normalization table, please ignore this warning. "
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"If this is surprising, make sure you have the spacy-lookups-data "
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"package installed. The languages with lexeme normalization tables "
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"are currently: {langs}")
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# TODO: fix numbering after merging develop into master
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W090 = ("Could not locate any binary .spacy files in path '{path}'.")
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W091 = ("Could not clean/remove the temp directory at {dir}: {msg}.")
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W092 = ("Ignoring annotations for sentence starts, as dependency heads are set.")
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W093 = ("Could not find any data to train the {name} on. Is your "
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"input data correctly formatted?")
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W094 = ("Model '{model}' ({model_version}) specifies an under-constrained "
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"spaCy version requirement: {version}. This can lead to compatibility "
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"problems with older versions, or as new spaCy versions are "
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"released, because the model may say it's compatible when it's "
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'not. Consider changing the "spacy_version" in your meta.json to a '
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"version range, with a lower and upper pin. For example: {example}")
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W095 = ("Model '{model}' ({model_version}) requires spaCy {version} and is "
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"incompatible with the current version ({current}). This may lead "
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"to unexpected results or runtime errors. To resolve this, "
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"download a newer compatible model or retrain your custom model "
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"with the current spaCy version. For more details and available "
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"updates, run: python -m spacy validate")
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W096 = ("The method 'disable_pipes' has become deprecated - use 'select_pipes' "
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"instead.")
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W097 = ("No Model config was provided to create the '{name}' component, "
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"and no default configuration could be found either.")
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W098 = ("No Model config was provided to create the '{name}' component, "
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"so a default configuration was used.")
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W099 = ("Expected 'dict' type for the 'model' argument of pipe '{pipe}', "
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"but got '{type}' instead, so ignoring it.")
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W100 = ("Skipping unsupported morphological feature(s): '{feature}'. "
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"Provide features as a dict {{\"Field1\": \"Value1,Value2\"}} or "
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"string \"Field1=Value1,Value2|Field2=Value3\".")
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W101 = ("Skipping `Doc` custom extension '{name}' while merging docs.")
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W102 = ("Skipping unsupported user data '{key}: {value}' while merging docs.")
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W103 = ("Unknown {lang} word segmenter '{segmenter}'. Supported "
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"word segmenters: {supported}. Defaulting to {default}.")
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W104 = ("Skipping modifications for '{target}' segmenter. The current "
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"segmenter is '{current}'.")
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W105 = ("As of spaCy v3.0, the {matcher}.pipe method is deprecated. If you "
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"need to match on a stream of documents, you can use nlp.pipe and "
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"call the {matcher} on each Doc object.")
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@add_codes
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class Errors:
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E001 = ("No component '{name}' found in pipeline. Available names: {opts}")
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E002 = ("Can't find factory for '{name}' for language {lang} ({lang_code}). "
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"This usually happens when spaCy calls nlp.{method} with custom "
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"component name that's not registered on the current language class. "
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"If you're using a custom component, make sure you've added the "
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"decorator @Language.component (for function components) or "
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"@Language.factory (for class components).\n\nAvailable "
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"factories: {opts}")
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E003 = ("Not a valid pipeline component. Expected callable, but "
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"got {component} (name: '{name}'). If you're using a custom "
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"component factory, double-check that it correctly returns your "
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"initialized component.")
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E004 = ("Can't set up pipeline component: a factory for '{name}' already "
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"exists. Existing factory: {func}. New factory: {new_func}")
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E005 = ("Pipeline component '{name}' returned None. If you're using a "
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"custom component, maybe you forgot to return the processed Doc?")
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E006 = ("Invalid constraints for adding pipeline component. You can only "
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"set one of the following: before (component name or index), "
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"after (component name or index), first (True) or last (True). "
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"Invalid configuration: {args}. Existing components: {opts}")
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E007 = ("'{name}' already exists in pipeline. Existing names: {opts}")
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E008 = ("Can't restore disabled pipeline component '{name}' because it "
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"doesn't exist in the pipeline anymore. If you want to remove "
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"components from the pipeline, you should do it before calling "
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"`nlp.select_pipes()` or after restoring the disabled components.")
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E010 = ("Word vectors set to length 0. This may be because you don't have "
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"a model installed or loaded, or because your model doesn't "
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"include word vectors. For more info, see the docs:\n"
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"https://spacy.io/usage/models")
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E011 = ("Unknown operator: '{op}'. Options: {opts}")
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E012 = ("Cannot add pattern for zero tokens to matcher.\nKey: {key}")
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E014 = ("Unknown tag ID: {tag}")
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E016 = ("MultitaskObjective target should be function or one of: dep, "
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"tag, ent, dep_tag_offset, ent_tag.")
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E017 = ("Can only add unicode or bytes. Got type: {value_type}")
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E018 = ("Can't retrieve string for hash '{hash_value}'. This usually "
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"refers to an issue with the `Vocab` or `StringStore`.")
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E019 = ("Can't create transition with unknown action ID: {action}. Action "
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"IDs are enumerated in spacy/syntax/{src}.pyx.")
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E022 = ("Could not find a transition with the name '{name}' in the NER "
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"model.")
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E024 = ("Could not find an optimal move to supervise the parser. Usually, "
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"this means that the model can't be updated in a way that's valid "
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"and satisfies the correct annotations specified in the GoldParse. "
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"For example, are all labels added to the model? If you're "
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"training a named entity recognizer, also make sure that none of "
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"your annotated entity spans have leading or trailing whitespace "
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"or punctuation. "
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"You can also use the experimental `debug data` command to "
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"validate your JSON-formatted training data. For details, run:\n"
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"python -m spacy debug data --help")
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E025 = ("String is too long: {length} characters. Max is 2**30.")
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E026 = ("Error accessing token at position {i}: out of bounds in Doc of "
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"length {length}.")
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E027 = ("Arguments 'words' and 'spaces' should be sequences of the same "
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"length, or 'spaces' should be left default at None. spaces "
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"should be a sequence of booleans, with True meaning that the "
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"word owns a ' ' character following it.")
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E028 = ("orths_and_spaces expects either a list of unicode string or a "
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"list of (unicode, bool) tuples. Got bytes instance: {value}")
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E029 = ("noun_chunks requires the dependency parse, which requires a "
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"statistical model to be installed and loaded. For more info, see "
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"the documentation:\nhttps://spacy.io/usage/models")
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E030 = ("Sentence boundaries unset. You can add the 'sentencizer' "
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"component to the pipeline with: "
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"nlp.add_pipe('sentencizer'). "
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"Alternatively, add the dependency parser, or set sentence "
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"boundaries by setting doc[i].is_sent_start.")
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E031 = ("Invalid token: empty string ('') at position {i}.")
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E032 = ("Conflicting attributes specified in doc.from_array(): "
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"(HEAD, SENT_START). The HEAD attribute currently sets sentence "
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"boundaries implicitly, based on the tree structure. This means "
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"the HEAD attribute would potentially override the sentence "
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"boundaries set by SENT_START.")
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E033 = ("Cannot load into non-empty Doc of length {length}.")
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E035 = ("Error creating span with start {start} and end {end} for Doc of "
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"length {length}.")
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E036 = ("Error calculating span: Can't find a token starting at character "
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"offset {start}.")
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E037 = ("Error calculating span: Can't find a token ending at character "
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"offset {end}.")
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E039 = ("Array bounds exceeded while searching for root word. This likely "
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"means the parse tree is in an invalid state. Please report this "
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"issue here: http://github.com/explosion/spaCy/issues")
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E040 = ("Attempt to access token at {i}, max length {max_length}.")
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E041 = ("Invalid comparison operator: {op}. Likely a Cython bug?")
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E042 = ("Error accessing doc[{i}].nbor({j}), for doc of length {length}.")
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E043 = ("Refusing to write to token.sent_start if its document is parsed, "
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"because this may cause inconsistent state.")
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E044 = ("Invalid value for token.sent_start: {value}. Must be one of: "
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"None, True, False")
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E045 = ("Possibly infinite loop encountered while looking for {attr}.")
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E046 = ("Can't retrieve unregistered extension attribute '{name}'. Did "
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"you forget to call the `set_extension` method?")
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E047 = ("Can't assign a value to unregistered extension attribute "
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"'{name}'. Did you forget to call the `set_extension` method?")
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E048 = ("Can't import language {lang} from spacy.lang: {err}")
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E050 = ("Can't find model '{name}'. It doesn't seem to be a Python "
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"package or a valid path to a data directory.")
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E052 = ("Can't find model directory: {path}")
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E053 = ("Could not read {name} from {path}")
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E054 = ("No valid '{setting}' setting found in model meta.json.")
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E055 = ("Invalid ORTH value in exception:\nKey: {key}\nOrths: {orths}")
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E056 = ("Invalid tokenizer exception: ORTH values combined don't match "
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"original string.\nKey: {key}\nOrths: {orths}")
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E057 = ("Stepped slices not supported in Span objects. Try: "
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"list(tokens)[start:stop:step] instead.")
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E058 = ("Could not retrieve vector for key {key}.")
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E059 = ("One (and only one) keyword arg must be set. Got: {kwargs}")
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E060 = ("Cannot add new key to vectors: the table is full. Current shape: "
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"({rows}, {cols}).")
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E062 = ("Cannot find empty bit for new lexical flag. All bits between 0 "
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"and 63 are occupied. You can replace one by specifying the "
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"`flag_id` explicitly, e.g. "
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"`nlp.vocab.add_flag(your_func, flag_id=IS_ALPHA`.")
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E063 = ("Invalid value for flag_id: {value}. Flag IDs must be between 1 "
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"and 63 (inclusive).")
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E064 = ("Error fetching a Lexeme from the Vocab. When looking up a "
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"string, the lexeme returned had an orth ID that did not match "
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"the query string. This means that the cached lexeme structs are "
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"mismatched to the string encoding table. The mismatched:\n"
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"Query string: {string}\nOrth cached: {orth}\nOrth ID: {orth_id}")
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E065 = ("Only one of the vector table's width and shape can be specified. "
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"Got width {width} and shape {shape}.")
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E067 = ("Invalid BILUO tag sequence: Got a tag starting with 'I' (inside "
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"an entity) without a preceding 'B' (beginning of an entity). "
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"Tag sequence:\n{tags}")
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E068 = ("Invalid BILUO tag: '{tag}'.")
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E071 = ("Error creating lexeme: specified orth ID ({orth}) does not "
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"match the one in the vocab ({vocab_orth}).")
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E073 = ("Cannot assign vector of length {new_length}. Existing vectors "
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"are of length {length}. You can use `vocab.reset_vectors` to "
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"clear the existing vectors and resize the table.")
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E074 = ("Error interpreting compiled match pattern: patterns are expected "
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"to end with the attribute {attr}. Got: {bad_attr}.")
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E082 = ("Error deprojectivizing parse: number of heads ({n_heads}), "
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"projective heads ({n_proj_heads}) and labels ({n_labels}) do not "
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"match.")
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E083 = ("Error setting extension: only one of `default`, `method`, or "
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"`getter` (plus optional `setter`) is allowed. Got: {nr_defined}")
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E084 = ("Error assigning label ID {label} to span: not in StringStore.")
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E085 = ("Can't create lexeme for string '{string}'.")
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E087 = ("Unknown displaCy style: {style}.")
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E088 = ("Text of length {length} exceeds maximum of {max_length}. The "
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"v2.x parser and NER models require roughly 1GB of temporary "
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"memory per 100,000 characters in the input. This means long "
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"texts may cause memory allocation errors. If you're not using "
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"the parser or NER, it's probably safe to increase the "
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"`nlp.max_length` limit. The limit is in number of characters, so "
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"you can check whether your inputs are too long by checking "
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"`len(text)`.")
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E089 = ("Extensions can't have a setter argument without a getter "
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"argument. Check the keyword arguments on `set_extension`.")
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E090 = ("Extension '{name}' already exists on {obj}. To overwrite the "
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"existing extension, set `force=True` on `{obj}.set_extension`.")
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E091 = ("Invalid extension attribute {name}: expected callable or None, "
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"but got: {value}")
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E093 = ("token.ent_iob values make invalid sequence: I without B\n{seq}")
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E094 = ("Error reading line {line_num} in vectors file {loc}.")
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E095 = ("Can't write to frozen dictionary. This is likely an internal "
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"error. Are you writing to a default function argument?")
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E096 = ("Invalid object passed to displaCy: Can only visualize Doc or "
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"Span objects, or dicts if set to manual=True.")
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E097 = ("Invalid pattern: expected token pattern (list of dicts) or "
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"phrase pattern (string) but got:\n{pattern}")
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E098 = ("Invalid pattern specified: expected both SPEC and PATTERN.")
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E099 = ("First node of pattern should be a root node. The root should "
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"only contain NODE_NAME.")
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E100 = ("Nodes apart from the root should contain NODE_NAME, NBOR_NAME and "
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"NBOR_RELOP.")
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E101 = ("NODE_NAME should be a new node and NBOR_NAME should already have "
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"have been declared in previous edges.")
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E102 = ("Can't merge non-disjoint spans. '{token}' is already part of "
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"tokens to merge. If you want to find the longest non-overlapping "
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"spans, you can use the util.filter_spans helper:\n"
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"https://spacy.io/api/top-level#util.filter_spans")
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E103 = ("Trying to set conflicting doc.ents: '{span1}' and '{span2}'. A "
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"token can only be part of one entity, so make sure the entities "
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"you're setting don't overlap.")
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E106 = ("Can't find doc._.{attr} attribute specified in the underscore "
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"settings: {opts}")
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E107 = ("Value of doc._.{attr} is not JSON-serializable: {value}")
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E109 = ("Component '{name}' could not be run. Did you forget to "
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"call begin_training()?")
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E110 = ("Invalid displaCy render wrapper. Expected callable, got: {obj}")
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E111 = ("Pickling a token is not supported, because tokens are only views "
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"of the parent Doc and can't exist on their own. A pickled token "
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"would always have to include its Doc and Vocab, which has "
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"practically no advantage over pickling the parent Doc directly. "
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"So instead of pickling the token, pickle the Doc it belongs to.")
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E112 = ("Pickling a span is not supported, because spans are only views "
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"of the parent Doc and can't exist on their own. A pickled span "
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"would always have to include its Doc and Vocab, which has "
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"practically no advantage over pickling the parent Doc directly. "
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"So instead of pickling the span, pickle the Doc it belongs to or "
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"use Span.as_doc to convert the span to a standalone Doc object.")
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E115 = ("All subtokens must have associated heads.")
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E116 = ("Cannot currently add labels to pretrained text classifier. Add "
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"labels before training begins. This functionality was available "
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"in previous versions, but had significant bugs that led to poor "
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"performance.")
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E117 = ("The newly split tokens must match the text of the original token. "
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"New orths: {new}. Old text: {old}.")
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E118 = ("The custom extension attribute '{attr}' is not registered on the "
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"Token object so it can't be set during retokenization. To "
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"register an attribute, use the Token.set_extension classmethod.")
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E119 = ("Can't set custom extension attribute '{attr}' during "
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"retokenization because it's not writable. This usually means it "
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"was registered with a getter function (and no setter) or as a "
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"method extension, so the value is computed dynamically. To "
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"overwrite a custom attribute manually, it should be registered "
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"with a default value or with a getter AND setter.")
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E120 = ("Can't set custom extension attributes during retokenization. "
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"Expected dict mapping attribute names to values, but got: {value}")
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E121 = ("Can't bulk merge spans. Attribute length {attr_len} should be "
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"equal to span length ({span_len}).")
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E122 = ("Cannot find token to be split. Did it get merged?")
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E123 = ("Cannot find head of token to be split. Did it get merged?")
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E125 = ("Unexpected value: {value}")
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E126 = ("Unexpected matcher predicate: '{bad}'. Expected one of: {good}. "
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"This is likely a bug in spaCy, so feel free to open an issue.")
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E129 = ("Cannot write the label of an existing Span object because a Span "
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"is a read-only view of the underlying Token objects stored in the "
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"Doc. Instead, create a new Span object and specify the `label` "
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"keyword argument, for example:\nfrom spacy.tokens import Span\n"
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"span = Span(doc, start={start}, end={end}, label='{label}')")
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E130 = ("You are running a narrow unicode build, which is incompatible "
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"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
|
|
E925 = ("Invalid color values for displaCy visualizer: expected dictionary "
|
|
"mapping label names to colors but got: {obj}")
|
|
E926 = ("It looks like you're trying to modify nlp.{attr} directly. This "
|
|
"doesn't work because it's an immutable computed property. If you "
|
|
"need to modify the pipeline, use the built-in methods like "
|
|
"nlp.add_pipe, nlp.remove_pipe, nlp.disable_pipe or nlp.enable_pipe "
|
|
"instead.")
|
|
E927 = ("Can't write to frozen list Maybe you're trying to modify a computed "
|
|
"property or default function argument?")
|
|
E928 = ("A 'KnowledgeBase' should be written to / read from a file, but the "
|
|
"provided argument {loc} is an existing directory.")
|
|
E929 = ("A 'KnowledgeBase' could not be read from {loc} - the path does "
|
|
"not seem to exist.")
|
|
E930 = ("Received invalid get_examples callback in {name}.begin_training. "
|
|
"Expected function that returns an iterable of Example objects but "
|
|
"got: {obj}")
|
|
E931 = ("Encountered Pipe subclass without Pipe.{method} method in component "
|
|
"'{name}'. If the component is trainable and you want to use this "
|
|
"method, make sure it's overwritten on the subclass. If your "
|
|
"component isn't trainable, add a method that does nothing or "
|
|
"don't use the Pipe base class.")
|
|
E940 = ("Found NaN values in scores.")
|
|
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}")
|
|
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(s) '{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 {name} method takes a list of Example objects, but got: {types}")
|
|
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. "
|
|
"Are the train and dev paths defined? "
|
|
"Is 'discard_oversize' set appropriately? ")
|
|
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. ")
|
|
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.")
|
|
E1003 = ("Unsupported lemmatizer mode '{mode}'.")
|
|
E1004 = ("Missing lemmatizer table(s) found for lemmatizer mode '{mode}'. "
|
|
"Required tables '{tables}', found '{found}'. If you are not "
|
|
"providing custom lookups, make sure you have the package "
|
|
"spacy-lookups-data installed.")
|
|
E1005 = ("Unable to set attribute '{attr}' in tokenizer exception for "
|
|
"'{chunk}'. Tokenizer exceptions are only allowed to specify "
|
|
"`ORTH` and `NORM`.")
|
|
E1006 = ("Unable to initialize {name} model with 0 labels.")
|
|
|
|
|
|
@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)
|