mirror of
https://github.com/explosion/spaCy.git
synced 2025-10-30 15:37:29 +03:00
632 lines
40 KiB
Python
632 lines
40 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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W022 = ("Training a new part-of-speech tagger using a model with no "
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"lemmatization rules or data. This means that the trained model "
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"may not be able to lemmatize correctly. If this is intentional "
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"or the language you're using doesn't have lemmatization data, "
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"you can ignore this warning. If this is surprising, make sure you "
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"have the spacy-lookups-data package installed.")
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W024 = ("Entity '{entity}' - Alias '{alias}' combination already exists in "
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"the Knowledge Base.")
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W025 = ("'{name}' requires '{attr}' to be assigned, but none of the "
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"previous components in the pipeline declare that they assign it.")
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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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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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@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 exists.")
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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 = ("Some current components would be lost when restoring previous "
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"pipeline state. If you added components after calling "
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"`nlp.select_pipes()`, you should remove them explicitly with "
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"`nlp.remove_pipe()` before the pipeline is restored. Names of "
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"the new components: {names}")
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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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E092 = ("Could not find or assign name for word vectors. Ususally, the "
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"name is read from the model's meta.json in vector.name. "
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"Alternatively, it is built from the 'lang' and 'name' keys in "
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"the meta.json. Vector names are required to avoid issue #1660.")
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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 "
|
|
"Doc. Instead, create a new Span object and specify the `label` "
|
|
"keyword argument, for example:\nfrom spacy.tokens import Span\n"
|
|
"span = Span(doc, start={start}, end={end}, label='{label}')")
|
|
E130 = ("You are running a narrow unicode build, which is incompatible "
|
|
"with spacy >= 2.1.0. To fix this, reinstall Python and use a wide "
|
|
"unicode build instead. You can also rebuild Python and set the "
|
|
"--enable-unicode=ucs4 flag.")
|
|
E131 = ("Cannot write the kb_id of an existing Span object because a Span "
|
|
"is a read-only view of the underlying Token objects stored in "
|
|
"the Doc. Instead, create a new Span object and specify the "
|
|
"`kb_id` keyword argument, for example:\nfrom spacy.tokens "
|
|
"import Span\nspan = Span(doc, start={start}, end={end}, "
|
|
"label='{label}', kb_id='{kb_id}')")
|
|
E132 = ("The vectors for entities and probabilities for alias '{alias}' "
|
|
"should have equal length, but found {entities_length} and "
|
|
"{probabilities_length} respectively.")
|
|
E133 = ("The sum of prior probabilities for alias '{alias}' should not "
|
|
"exceed 1, but found {sum}.")
|
|
E134 = ("Entity '{entity}' is not defined in the Knowledge Base.")
|
|
E137 = ("Expected 'dict' type, but got '{type}' from '{line}'. Make sure "
|
|
"to provide a valid JSON object as input with either the `text` "
|
|
"or `tokens` key. For more info, see the docs:\n"
|
|
"https://spacy.io/api/cli#pretrain-jsonl")
|
|
E138 = ("Invalid JSONL format for raw text '{text}'. Make sure the input "
|
|
"includes either the `text` or `tokens` key. For more info, see "
|
|
"the docs:\nhttps://spacy.io/api/cli#pretrain-jsonl")
|
|
E139 = ("Knowledge Base for component '{name}' is empty.")
|
|
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 = ("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.")
|
|
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
|
|
E952 = ("Invalid requirement specified by component '{name}': {req}")
|
|
E953 = ("Mismatched IDs received by the Tok2Vec listener: {id1} vs. {id2}")
|
|
E954 = ("The Tok2Vec listener did not receive a valid input.")
|
|
E955 = ("Can't find table '{table}' for language '{lang}' in spacy-lookups-data.")
|
|
E956 = ("Can't find component '{name}' in [components] block in the config. "
|
|
"Available components: {opts}")
|
|
E957 = ("Writing directly to Language.factories isn't needed anymore in "
|
|
"spaCy v3. Instead, you can use the @Language.factory decorator "
|
|
"to register your custom component factory or @Language.component "
|
|
"to register a simple stateless function component that just takes "
|
|
"a Doc and returns it.")
|
|
E958 = ("Language code defined in config ({bad_lang_code}) does not match "
|
|
"language code of current Language subclass {lang} ({lang_code})")
|
|
E959 = ("Can't insert component {dir} index {idx}. Existing components: {opts}")
|
|
E960 = ("No config data found for component '{name}'. This is likely a bug "
|
|
"in spaCy.")
|
|
E961 = ("Found non-serializable Python object in config. Configs should "
|
|
"only include values that can be serialized to JSON. If you need "
|
|
"to pass models or other objects to your component, use a reference "
|
|
"to a registered function or initialize the object in your "
|
|
"component.\n\n{config}")
|
|
E962 = ("Received incorrect {style} for pipe '{name}'. Expected dict, "
|
|
"got: {cfg_type}.")
|
|
E963 = ("Can't read component info from @Language.{decorator} decorator. "
|
|
"Maybe you forgot to call it? Make sure you're using "
|
|
"@Language.{decorator}() instead of @Language.{decorator}.")
|
|
E964 = ("The pipeline component factory for '{name}' needs to have the "
|
|
"following named arguments, which are passed in by spaCy:\n- nlp: "
|
|
"receives the current nlp object and lets you access the vocab\n- "
|
|
"name: the name of the component instance, can be used to identify "
|
|
"the component, output losses etc.")
|
|
E965 = ("It looks like you're using the @Language.component decorator to "
|
|
"register '{name}' on a class instead of a function component. If "
|
|
"you need to register a class or function that *returns* a component "
|
|
"function, use the @Language.factory decorator instead.")
|
|
E966 = ("nlp.add_pipe now takes the string name of the registered component "
|
|
"factory, not a callable component. Expected string, but got "
|
|
"{component} (name: '{name}').\n\n- If you created your component "
|
|
"with nlp.create_pipe('name'): remove nlp.create_pipe and call "
|
|
"nlp.add_pipe('name') instead.\n\n- If you passed in a component "
|
|
"like TextCategorizer(): call nlp.add_pipe with the string name "
|
|
"instead, e.g. nlp.add_pipe('textcat').\n\n- If you're using a custom "
|
|
"component: Add the decorator @Language.component (for function "
|
|
"components) or @Language.factory (for class components / factories) "
|
|
"to your custom component and assign it a name, e.g. "
|
|
"@Language.component('your_name'). You can then run "
|
|
"nlp.add_pipe('your_name') to add it to the pipeline.")
|
|
E967 = ("No {meta} meta information found for '{name}'. This is likely a bug in spaCy.")
|
|
E968 = ("nlp.replace_pipe now takes the string name of the registered component "
|
|
"factory, not a callable component. Expected string, but got "
|
|
"{component}.\n\n- If you created your component with"
|
|
"with nlp.create_pipe('name'): remove nlp.create_pipe and call "
|
|
"nlp.replace_pipe('{name}', 'name') instead.\n\n- If you passed in a "
|
|
"component like TextCategorizer(): call nlp.replace_pipe with the "
|
|
"string name instead, e.g. nlp.replace_pipe('{name}', 'textcat').\n\n"
|
|
"- If you're using a custom component: Add the decorator "
|
|
"@Language.component (for function components) or @Language.factory "
|
|
"(for class components / factories) to your custom component and "
|
|
"assign it a name, e.g. @Language.component('your_name'). You can "
|
|
"then run nlp.replace_pipe('{name}', 'your_name').")
|
|
E969 = ("Expected string values for field '{field}', but received {types} instead. ")
|
|
E970 = ("Can not execute command '{str_command}'. Do you have '{tool}' installed?")
|
|
E971 = ("Found incompatible lengths in Doc.from_array: {array_length} for the "
|
|
"array and {doc_length} for the Doc itself.")
|
|
E972 = ("Example.__init__ got None for '{arg}'. Requires Doc.")
|
|
E973 = ("Unexpected type for NER data")
|
|
E974 = ("Unknown {obj} attribute: {key}")
|
|
E976 = ("The method 'Example.from_dict' expects a {type} as {n} argument, "
|
|
"but received None.")
|
|
E977 = ("Can not compare a MorphAnalysis with a string object. "
|
|
"This is likely a bug in spaCy, so feel free to open an issue.")
|
|
E978 = ("The '{method}' method of {name} takes a list of Example objects, "
|
|
"but found {types} instead.")
|
|
E979 = ("Cannot convert {type} to an Example object.")
|
|
E980 = ("Each link annotation should refer to a dictionary with at most one "
|
|
"identifier mapping to 1.0, and all others to 0.0.")
|
|
E981 = ("The offsets of the annotations for 'links' could not be aligned "
|
|
"to token boundaries.")
|
|
E982 = ("The 'ent_iob' attribute of a Token should be an integer indexing "
|
|
"into {values}, but found {value}.")
|
|
E983 = ("Invalid key for '{dict}': {key}. Available keys: "
|
|
"{keys}")
|
|
E984 = ("Invalid component config for '{name}': no 'factory' key "
|
|
"specifying the registered function used to initialize the "
|
|
"component. For example, factory = \"ner\" will use the 'ner' "
|
|
"factory and all other settings in the block will be passed "
|
|
"to it as arguments.\n\n{config}")
|
|
E985 = ("Can't load model from config file: no 'nlp' section found.\n\n{config}")
|
|
E986 = ("Could not create any training batches: check your input. "
|
|
"Perhaps discard_oversize should be set to False ?")
|
|
E987 = ("The text of an example training instance is either a Doc or "
|
|
"a string, but found {type} instead.")
|
|
E988 = ("Could not parse any training examples. Ensure the data is "
|
|
"formatted correctly.")
|
|
E989 = ("'nlp.update()' was called with two positional arguments. This "
|
|
"may be due to a backwards-incompatible change to the format "
|
|
"of the training data in spaCy 3.0 onwards. The 'update' "
|
|
"function should now be called with a batch of 'Example' "
|
|
"objects, instead of (text, annotation) tuples. ")
|
|
E990 = ("An entity linking component needs to be initialized with a "
|
|
"KnowledgeBase object, but found {type} instead.")
|
|
E991 = ("The function 'select_pipes' should be called with either a "
|
|
"'disable' argument to list the names of the pipe components "
|
|
"that should be disabled, or with an 'enable' argument that "
|
|
"specifies which pipes should not be disabled.")
|
|
E992 = ("The function `select_pipes` was called with `enable`={enable} "
|
|
"and `disable`={disable} but that information is conflicting "
|
|
"for the `nlp` pipeline with components {names}.")
|
|
E993 = ("The config for 'nlp' needs to include a key 'lang' specifying "
|
|
"the code of the language to initialize it with (for example "
|
|
"'en' for English).\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)')
|
|
|
|
|
|
@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")
|
|
|
|
|
|
# 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)
|