import warnings def add_codes(err_cls): """Add error codes to string messages via class attribute names.""" class ErrorsWithCodes(err_cls): def __getattribute__(self, code): msg = super(ErrorsWithCodes, self).__getattribute__(code) if code.startswith("__"): # python system attributes like __class__ return msg else: return "[{code}] {msg}".format(code=code, msg=msg) return ErrorsWithCodes() def setup_default_warnings(): # ignore certain numpy warnings filter_warning("ignore", error_msg="numpy.dtype size changed") # noqa filter_warning("ignore", error_msg="numpy.ufunc size changed") # noqa # warn about entity_ruler & matcher having no patterns only once for pipe in ["matcher", "entity_ruler"]: filter_warning("once", error_msg=Warnings.W036.format(name=pipe)) # warn once about lemmatizer without required POS filter_warning("once", error_msg="[W108]") def filter_warning(action: str, error_msg: str): """Customize how spaCy should handle a certain warning. error_msg (str): e.g. "W006", or a full error message action (str): "default", "error", "ignore", "always", "module" or "once" """ warnings.filterwarnings(action, message=_escape_warning_msg(error_msg)) def _escape_warning_msg(msg): """To filter with warnings.filterwarnings, the [] brackets need to be escaped""" return msg.replace("[", "\\[").replace("]", "\\]") # fmt: off @add_codes class Warnings: W005 = ("Doc object not parsed. This means displaCy won't be able to " "generate a dependency visualization for it. Make sure the Doc " "was processed with a model that supports dependency parsing, and " "not just a language class like `English()`. For more info, see " "the docs:\nhttps://spacy.io/usage/models") W006 = ("No entities to visualize found in Doc object. If this is " "surprising to you, make sure the Doc was processed using a model " "that supports named entity recognition, and check the `doc.ents` " "property manually if necessary.") W007 = ("The model you're using has no word vectors loaded, so the result " "of the {obj}.similarity method will be based on the tagger, " "parser and NER, which may not give useful similarity judgements. " "This may happen if you're using one of the small models, e.g. " "`en_core_web_sm`, which don't ship with word vectors and only " "use context-sensitive tensors. You can always add your own word " "vectors, or use one of the larger models instead if available.") W008 = ("Evaluating {obj}.similarity based on empty vectors.") W011 = ("It looks like you're calling displacy.serve from within a " "Jupyter notebook or a similar environment. This likely means " "you're already running a local web server, so there's no need to " "make displaCy start another one. Instead, you should be able to " "replace displacy.serve with displacy.render to show the " "visualization.") W012 = ("A Doc object you're adding to the PhraseMatcher for pattern " "'{key}' is parsed and/or tagged, but to match on '{attr}', you " "don't actually need this information. This means that creating " "the patterns is potentially much slower, because all pipeline " "components are applied. To only create tokenized Doc objects, " "try using `nlp.make_doc(text)` or process all texts as a stream " "using `list(nlp.tokenizer.pipe(all_texts))`.") W017 = ("Alias '{alias}' already exists in the Knowledge Base.") W018 = ("Entity '{entity}' already exists in the Knowledge Base - " "ignoring the duplicate entry.") W021 = ("Unexpected hash collision in PhraseMatcher. Matches may be " "incorrect. Modify PhraseMatcher._terminal_hash to fix.") W024 = ("Entity '{entity}' - Alias '{alias}' combination already exists in " "the Knowledge Base.") W026 = ("Unable to set all sentence boundaries from dependency parses. If " "you are constructing a parse tree incrementally by setting " "token.head values, you can probably ignore this warning. Consider " "using Doc(words, ..., heads=heads, deps=deps) instead.") W027 = ("Found a large training file of {size} bytes. Note that it may " "be more efficient to split your training data into multiple " "smaller JSON files instead.") W028 = ("Doc.from_array was called with a vector of type '{type}', " "but is expecting one of type uint64 instead. This may result " "in problems with the vocab further on in the pipeline.") W030 = ("Some entities could not be aligned in the text \"{text}\" with " "entities \"{entities}\". Use " "`spacy.training.offsets_to_biluo_tags(nlp.make_doc(text), entities)`" " to check the alignment. Misaligned entities ('-') will be " "ignored during training.") W033 = ("Training a new {model} using a model with no lexeme normalization " "table. This may degrade the performance of the model to some " "degree. If this is intentional or the language you're using " "doesn't have a normalization table, please ignore this warning. " "If this is surprising, make sure you have the spacy-lookups-data " "package installed and load the table in your config. The " "languages with lexeme normalization tables are currently: " "{langs}\n\nLoad the table in your config with:\n\n" "[initialize.lookups]\n" "@misc = \"spacy.LookupsDataLoader.v1\"\n" "lang = ${{nlp.lang}}\n" "tables = [\"lexeme_norm\"]\n") W035 = ("Discarding subpattern '{pattern}' due to an unrecognized " "attribute or operator.") W036 = ("The component '{name}' does not have any patterns defined.") # New warnings added in v3.x W086 = ("Component '{listener}' will be (re)trained, but it needs the component " "'{name}' which is frozen. You can either freeze both, or neither " "of the two. If you're sourcing the component from " "an existing pipeline, you can use the `replace_listeners` setting in " "the config block to replace its token-to-vector listener with a copy " "and make it independent. For example, `replace_listeners = " "[\"model.tok2vec\"]` See the documentation for details: " "https://spacy.io/usage/training#config-components-listeners") W087 = ("Component '{name}' will be (re)trained, but the component '{listener}' " "depends on it via a listener and is frozen. This means that the " "performance of '{listener}' will be degraded. You can either freeze " "both, or neither of the two. If you're sourcing the component from " "an existing pipeline, you can use the `replace_listeners` setting in " "the config block to replace its token-to-vector listener with a copy " "and make it independent. For example, `replace_listeners = " "[\"model.tok2vec\"]` See the documentation for details: " "https://spacy.io/usage/training#config-components-listeners") W088 = ("The pipeline component {name} implements a `begin_training` " "method, which won't be called by spaCy. As of v3.0, `begin_training` " "has been renamed to `initialize`, so you likely want to rename the " "component method. See the documentation for details: " "https://spacy.io/api/language#initialize") W089 = ("As of spaCy v3.0, the `nlp.begin_training` method has been renamed " "to `nlp.initialize`.") W090 = ("Could not locate any {format} files in path '{path}'.") W091 = ("Could not clean/remove the temp directory at {dir}: {msg}.") W092 = ("Ignoring annotations for sentence starts, as dependency heads are set.") W093 = ("Could not find any data to train the {name} on. Is your " "input data correctly formatted?") W094 = ("Model '{model}' ({model_version}) specifies an under-constrained " "spaCy version requirement: {version}. This can lead to compatibility " "problems with older versions, or as new spaCy versions are " "released, because the model may say it's compatible when it's " 'not. Consider changing the "spacy_version" in your meta.json to a ' "version range, with a lower and upper pin. For example: {example}") W095 = ("Model '{model}' ({model_version}) was trained with spaCy " "{version} and may not be 100% compatible with the current version " "({current}). If you see errors or degraded performance, download " "a newer compatible model or retrain your custom model with the " "current spaCy version. For more details and available updates, " "run: python -m spacy validate") W096 = ("The method `nlp.disable_pipes` is now deprecated - use " "`nlp.select_pipes` instead.") W100 = ("Skipping unsupported morphological feature(s): '{feature}'. " "Provide features as a dict {{\"Field1\": \"Value1,Value2\"}} or " "string \"Field1=Value1,Value2|Field2=Value3\".") W101 = ("Skipping Doc custom extension '{name}' while merging docs.") W102 = ("Skipping unsupported user data '{key}: {value}' while merging docs.") W103 = ("Unknown {lang} word segmenter '{segmenter}'. Supported " "word segmenters: {supported}. Defaulting to {default}.") W104 = ("Skipping modifications for '{target}' segmenter. The current " "segmenter is '{current}'.") W105 = ("As of spaCy v3.0, the `{matcher}.pipe` method is deprecated. If you " "need to match on a stream of documents, you can use `nlp.pipe` and " "call the {matcher} on each Doc object.") W107 = ("The property `Doc.{prop}` is deprecated. Use " "`Doc.has_annotation(\"{attr}\")` instead.") W108 = ("The rule-based lemmatizer did not find POS annotation for the " "token '{text}'. Check that your pipeline includes components that " "assign token.pos, typically 'tagger'+'attribute_ruler' or " "'morphologizer'.") W109 = ("Unable to save user hooks while serializing the doc. Re-add any " "required user hooks to the doc after processing.") W110 = ("The DependencyMatcher token pattern {pattern} matched a span " "{tokens} that is 2+ tokens long. Only the first token in the span " "will be included in the results. For better results, token " "patterns should return matches that are each exactly one token " "long.") W111 = ("Jupyter notebook detected: if using `prefer_gpu()` or " "`require_gpu()`, include it in the same cell right before " "`spacy.load()` to ensure that the model is loaded on the correct " "device. More information: " "http://spacy.io/usage/v3#jupyter-notebook-gpu") W112 = ("The model specified to use for initial vectors ({name}) has no " "vectors. This is almost certainly a mistake.") W113 = ("Sourced component '{name}' may not work as expected: source " "vectors are not identical to current pipeline vectors.") @add_codes class Errors: E001 = ("No component '{name}' found in pipeline. Available names: {opts}") E002 = ("Can't find factory for '{name}' for language {lang} ({lang_code}). " "This usually happens when spaCy calls `nlp.{method}` with a custom " "component name that's not registered on the current language class. " "If you're using a Transformer, make sure to install 'spacy-transformers'. " "If you're using a custom component, make sure you've added the " "decorator `@Language.component` (for function components) or " "`@Language.factory` (for class components).\n\nAvailable " "factories: {opts}") E003 = ("Not a valid pipeline component. Expected callable, but " "got {component} (name: '{name}'). If you're using a custom " "component factory, double-check that it correctly returns your " "initialized component.") E004 = ("Can't set up pipeline component: a factory for '{name}' already " "exists. Existing factory: {func}. New factory: {new_func}") E005 = ("Pipeline component '{name}' returned None. If you're using a " "custom component, maybe you forgot to return the processed Doc?") E006 = ("Invalid constraints for adding pipeline component. You can only " "set one of the following: before (component name or index), " "after (component name or index), first (True) or last (True). " "Invalid configuration: {args}. Existing components: {opts}") E007 = ("'{name}' already exists in pipeline. Existing names: {opts}") E008 = ("Can't restore disabled pipeline component '{name}' because it " "doesn't exist in the pipeline anymore. If you want to remove " "components from the pipeline, you should do it before calling " "`nlp.select_pipes` or after restoring the disabled components.") E010 = ("Word vectors set to length 0. This may be because you don't have " "a model installed or loaded, or because your model doesn't " "include word vectors. For more info, see the docs:\n" "https://spacy.io/usage/models") E011 = ("Unknown operator: '{op}'. Options: {opts}") E012 = ("Cannot add pattern for zero tokens to matcher.\nKey: {key}") E016 = ("MultitaskObjective target should be function or one of: dep, " "tag, ent, dep_tag_offset, ent_tag.") E017 = ("Can only add unicode or bytes. Got type: {value_type}") E018 = ("Can't retrieve string for hash '{hash_value}'. This usually " "refers to an issue with the `Vocab` or `StringStore`.") E019 = ("Can't create transition with unknown action ID: {action}. Action " "IDs are enumerated in spacy/syntax/{src}.pyx.") E022 = ("Could not find a transition with the name '{name}' in the NER " "model.") E024 = ("Could not find an optimal move to supervise the parser. Usually, " "this means that the model can't be updated in a way that's valid " "and satisfies the correct annotations specified in the GoldParse. " "For example, are all labels added to the model? If you're " "training a named entity recognizer, also make sure that none of " "your annotated entity spans have leading or trailing whitespace " "or punctuation. You can also use the `debug data` command to " "validate your JSON-formatted training data. For details, run:\n" "python -m spacy debug data --help") E025 = ("String is too long: {length} characters. Max is 2**30.") E026 = ("Error accessing token at position {i}: out of bounds in Doc of " "length {length}.") E027 = ("Arguments `words` and `spaces` should be sequences of the same " "length, or `spaces` should be left default at None. `spaces` " "should be a sequence of booleans, with True meaning that the " "word owns a ' ' character following it.") E028 = ("`words` expects a list of unicode strings, but got bytes instance: {value}") E029 = ("`noun_chunks` requires the dependency parse, which requires a " "statistical model to be installed and loaded. For more info, see " "the documentation:\nhttps://spacy.io/usage/models") E030 = ("Sentence boundaries unset. You can add the 'sentencizer' " "component to the pipeline with: `nlp.add_pipe('sentencizer')`. " "Alternatively, add the dependency parser or sentence recognizer, " "or set sentence boundaries by setting `doc[i].is_sent_start`.") E031 = ("Invalid token: empty string ('') at position {i}.") E033 = ("Cannot load into non-empty Doc of length {length}.") E035 = ("Error creating span with start {start} and end {end} for Doc of " "length {length}.") E036 = ("Error calculating span: Can't find a token starting at character " "offset {start}.") E037 = ("Error calculating span: Can't find a token ending at character " "offset {end}.") E039 = ("Array bounds exceeded while searching for root word. This likely " "means the parse tree is in an invalid state. Please report this " "issue here: http://github.com/explosion/spaCy/issues") E040 = ("Attempt to access token at {i}, max length {max_length}.") E041 = ("Invalid comparison operator: {op}. Likely a Cython bug?") E042 = ("Error accessing `doc[{i}].nbor({j})`, for doc of length {length}.") E043 = ("Refusing to write to token.sent_start if its document is parsed, " "because this may cause inconsistent state.") E044 = ("Invalid value for token.sent_start: {value}. Must be one of: " "None, True, False") E045 = ("Possibly infinite loop encountered while looking for {attr}.") E046 = ("Can't retrieve unregistered extension attribute '{name}'. Did " "you forget to call the `set_extension` method?") E047 = ("Can't assign a value to unregistered extension attribute " "'{name}'. Did you forget to call the `set_extension` method?") E048 = ("Can't import language {lang} from spacy.lang: {err}") E050 = ("Can't find model '{name}'. It doesn't seem to be a Python " "package or a valid path to a data directory.") E052 = ("Can't find model directory: {path}") E053 = ("Could not read {name} from {path}") E054 = ("No valid '{setting}' setting found in model meta.json.") E055 = ("Invalid ORTH value in exception:\nKey: {key}\nOrths: {orths}") E056 = ("Invalid tokenizer exception: ORTH values combined don't match " "original string.\nKey: {key}\nOrths: {orths}") E057 = ("Stepped slices not supported in Span objects. Try: " "`list(tokens)[start:stop:step]` instead.") E058 = ("Could not retrieve vector for key {key}.") E059 = ("One (and only one) keyword arg must be set. Got: {kwargs}") E060 = ("Cannot add new key to vectors: the table is full. Current shape: " "({rows}, {cols}).") E062 = ("Cannot find empty bit for new lexical flag. All bits between 0 " "and 63 are occupied. You can replace one by specifying the " "`flag_id` explicitly, e.g. " "`nlp.vocab.add_flag(your_func, flag_id=IS_ALPHA`.") E063 = ("Invalid value for `flag_id`: {value}. Flag IDs must be between 1 " "and 63 (inclusive).") E064 = ("Error fetching a Lexeme from the Vocab. When looking up a " "string, the lexeme returned had an orth ID that did not match " "the query string. This means that the cached lexeme structs are " "mismatched to the string encoding table. The mismatched:\n" "Query string: {string}\nOrth cached: {orth}\nOrth ID: {orth_id}") E065 = ("Only one of the vector table's width and shape can be specified. " "Got width {width} and shape {shape}.") E067 = ("Invalid BILUO tag sequence: Got a tag starting with {start} " "without a preceding 'B' (beginning of an entity). " "Tag sequence:\n{tags}") E068 = ("Invalid BILUO tag: '{tag}'.") E071 = ("Error creating lexeme: specified orth ID ({orth}) does not " "match the one in the vocab ({vocab_orth}).") E073 = ("Cannot assign vector of length {new_length}. Existing vectors " "are of length {length}. You can use `vocab.reset_vectors` to " "clear the existing vectors and resize the table.") E074 = ("Error interpreting compiled match pattern: patterns are expected " "to end with the attribute {attr}. Got: {bad_attr}.") E082 = ("Error deprojectivizing parse: number of heads ({n_heads}), " "projective heads ({n_proj_heads}) and labels ({n_labels}) do not " "match.") E083 = ("Error setting extension: only one of `default`, `method`, or " "`getter` (plus optional `setter`) is allowed. Got: {nr_defined}") E084 = ("Error assigning label ID {label} to span: not in StringStore.") E085 = ("Can't create lexeme for string '{string}'.") E087 = ("Unknown displaCy style: {style}.") E088 = ("Text of length {length} exceeds maximum of {max_length}. The " "parser and NER models require roughly 1GB of temporary " "memory per 100,000 characters in the input. This means long " "texts may cause memory allocation errors. If you're not using " "the parser or NER, it's probably safe to increase the " "`nlp.max_length` limit. The limit is in number of characters, so " "you can check whether your inputs are too long by checking " "`len(text)`.") E089 = ("Extensions can't have a setter argument without a getter " "argument. Check the keyword arguments on `set_extension`.") E090 = ("Extension '{name}' already exists on {obj}. To overwrite the " "existing extension, set `force=True` on `{obj}.set_extension`.") E091 = ("Invalid extension attribute {name}: expected callable or None, " "but got: {value}") E093 = ("token.ent_iob values make invalid sequence: I without B\n{seq}") E094 = ("Error reading line {line_num} in vectors file {loc}.") E095 = ("Can't write to frozen dictionary. This is likely an internal " "error. Are you writing to a default function argument?") E096 = ("Invalid object passed to displaCy: Can only visualize `Doc` or " "Span objects, or dicts if set to `manual=True`.") E097 = ("Invalid pattern: expected token pattern (list of dicts) or " "phrase pattern (string) but got:\n{pattern}") E098 = ("Invalid pattern: expected both RIGHT_ID and RIGHT_ATTRS.") E099 = ("Invalid pattern: the first node of pattern should be an anchor " "node. The node should only contain RIGHT_ID and RIGHT_ATTRS.") E100 = ("Nodes other than the anchor node should all contain LEFT_ID, " "REL_OP and RIGHT_ID.") E101 = ("RIGHT_ID should be a new node and LEFT_ID should already have " "have been declared in previous edges.") E102 = ("Can't merge non-disjoint spans. '{token}' is already part of " "tokens to merge. If you want to find the longest non-overlapping " "spans, you can use the util.filter_spans helper:\n" "https://spacy.io/api/top-level#util.filter_spans") E103 = ("Trying to set conflicting doc.ents: '{span1}' and '{span2}'. A " "token can only be part of one entity, so make sure the entities " "you're setting don't overlap. To work with overlapping entities, " "consider using doc.spans instead.") E106 = ("Can't find `doc._.{attr}` attribute specified in the underscore " "settings: {opts}") E107 = ("Value of `doc._.{attr}` is not JSON-serializable: {value}") E109 = ("Component '{name}' could not be run. Did you forget to " "call `initialize()`?") E110 = ("Invalid displaCy render wrapper. Expected callable, got: {obj}") E111 = ("Pickling a token is not supported, because tokens are only views " "of the parent Doc and can't exist on their own. A pickled token " "would always have to include its Doc and Vocab, which has " "practically no advantage over pickling the parent Doc directly. " "So instead of pickling the token, pickle the Doc it belongs to.") E112 = ("Pickling a span is not supported, because spans are only views " "of the parent Doc and can't exist on their own. A pickled span " "would always have to include its Doc and Vocab, which has " "practically no advantage over pickling the parent Doc directly. " "So instead of pickling the span, pickle the Doc it belongs to or " "use Span.as_doc to convert the span to a standalone Doc object.") E115 = ("All subtokens must have associated heads.") E117 = ("The newly split tokens must match the text of the original token. " "New orths: {new}. Old text: {old}.") E118 = ("The custom extension attribute '{attr}' is not registered on the " "`Token` object so it can't be set during retokenization. To " "register an attribute, use the `Token.set_extension` classmethod.") E119 = ("Can't set custom extension attribute '{attr}' during " "retokenization because it's not writable. This usually means it " "was registered with a getter function (and no setter) or as a " "method extension, so the value is computed dynamically. To " "overwrite a custom attribute manually, it should be registered " "with a default value or with a getter AND setter.") E120 = ("Can't set custom extension attributes during retokenization. " "Expected dict mapping attribute names to values, but got: {value}") E121 = ("Can't bulk merge spans. Attribute length {attr_len} should be " "equal to span length ({span_len}).") E122 = ("Cannot find token to be split. Did it get merged?") E123 = ("Cannot find head of token to be split. Did it get merged?") E125 = ("Unexpected value: {value}") E126 = ("Unexpected matcher predicate: '{bad}'. Expected one of: {good}. " "This is likely a bug in spaCy, so feel free to open an issue.") 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`.") 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.") 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. This can be fixed " "by calling add_label, or by providing a representative batch of " "examples to the component's `initialize` method.") 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: https://github.com/explosion/spaCy/issues") 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 the 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 the Matcher, " "PhraseMatcher, or EntityRuler for more details.") E155 = ("The pipeline needs to include a {pipe} in order to use " "Matcher or PhraseMatcher with the attribute {attr}. " "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: " "https://github.com/explosion/spaCy/issues") E163 = ("cumsum was found to be unstable: its last element does not " "correspond to sum") E164 = ("x is neither increasing nor decreasing: {x}.") E165 = ("Only one class present in the gold labels: {label}. " "ROC AUC score is not defined in that case.") E166 = ("Can only merge DocBins with the same value for '{param}'.\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}.") E187 = ("Only unicode strings are supported as labels.") E189 = ("Each argument to `Doc.__init__` 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}]`.") E200 = ("Can't yet set {attr} from Span. Vote for this feature on the " "issue tracker: http://github.com/explosion/spaCy/issues") E202 = ("Unsupported alignment mode '{mode}'. Supported modes: {modes}.") # New errors added in v3.x E868 = ("Found a conflicting gold annotation in a reference document, " "with the following char-based span occurring both in the gold ents " "as well as in the negative spans: {span}.") E869 = ("The notation '{label}' is not supported anymore. To annotate " "negative NER samples, use `doc.spans[key]` instead, and " "specify the key as 'incorrect_spans_key' when constructing " "the NER component.") E870 = ("Could not serialize the DocBin because it is too large. Consider " "splitting up your documents into several doc bins and serializing " "each separately. spacy.Corpus.v1 will search recursively for all " "*.spacy files if you provide a directory instead of a filename as " "the 'path'.") E871 = ("Error encountered in nlp.pipe with multiprocessing:\n\n{error}") E872 = ("Unable to copy tokenizer from base model due to different " 'tokenizer settings: current tokenizer config "{curr_config}" ' 'vs. base model "{base_config}"') E873 = ("Unable to merge a span from doc.spans with key '{key}' and text " "'{text}'. This is likely a bug in spaCy, so feel free to open an " "issue: https://github.com/explosion/spaCy/issues") E874 = ("Could not initialize the tok2vec model from component " "'{component}' and layer '{layer}'.") E875 = ("To use the PretrainVectors objective, make sure that static vectors are loaded. " "In the config, these are defined by the initialize.vectors setting.") E879 = ("Unexpected type for 'spans' data. Provide a dictionary mapping keys to " "a list of spans, with each span represented by a tuple (start_char, end_char). " "The tuple can be optionally extended with a label and a KB ID.") E880 = ("The 'wandb' library could not be found - did you install it? " "Alternatively, specify the 'ConsoleLogger' in the 'training.logger' " "config section, instead of the 'WandbLogger'.") E884 = ("The pipeline could not be initialized because the vectors " "could not be found at '{vectors}'. If your pipeline was already " "initialized/trained before, call 'resume_training' instead of 'initialize', " "or initialize only the components that are new.") E885 = ("entity_linker.set_kb received an invalid 'kb_loader' argument: expected " "a callable function, but got: {arg_type}") E886 = ("Can't replace {name} -> {tok2vec} listeners: path '{path}' not " "found in config for component '{name}'.") E887 = ("Can't replace {name} -> {tok2vec} listeners: the paths to replace " "({paths}) don't match the available listeners in the model ({n_listeners}).") E888 = ("Can't replace listeners for '{name}' ({pipe}): invalid upstream " "component that doesn't seem to support listeners. Expected Tok2Vec " "or Transformer component. If you didn't call nlp.replace_listeners " "manually, this is likely a bug in spaCy.") E889 = ("Can't replace '{tok2vec}' listeners of component '{name}' because " "'{unknown}' is not in the pipeline. Available components: {opts}. " "If you didn't call nlp.replace_listeners manually, this is likely " "a bug in spaCy.") E890 = ("Cannot add the alias '{alias}' to the Knowledge base. " "Each alias should be a meaningful string.") E891 = ("Alias '{alias}' could not be added to the Knowledge base. " "This is likely a bug in spaCy.") E892 = ("Unknown function registry: '{name}'.\n\nAvailable names: {available}") E893 = ("Could not find function '{name}' in function registry '{reg_name}'. " "If you're using a custom function, make sure the code is available. " "If the function is provided by a third-party package, e.g. " "spacy-transformers, make sure the package is installed in your " "environment.\n\nAvailable names: {available}") E894 = ("The 'noun_chunks' syntax iterator is not implemented for language " "'{lang}'.") E895 = ("The 'textcat' component received gold-standard annotations with " "multiple labels per document. In spaCy 3 you should use the " "'textcat_multilabel' component for this instead. " "Example of an offending annotation: {value}") E896 = ("There was an error using the static vectors. Ensure that the vectors " "of the vocab are properly initialized, or set 'include_static_vectors' " "to False.") E897 = ("Field '{field}' should be a dot-notation string referring to the " "relevant section in the config, but found type {type} instead.") E898 = ("Can't serialize trainable pipe '{name}': the `model` attribute " "is not set or None. If you've implemented a custom component, make " "sure to store the component model as `self.model` in your " "component's __init__ method.") E899 = ("Can't serialize trainable pipe '{name}': the `vocab` attribute " "is not set or None. If you've implemented a custom component, make " "sure to store the current `nlp` object's vocab as `self.vocab` in " "your component's __init__ method.") E900 = ("Could not run the full pipeline for evaluation. If you specified " "frozen components, make sure they were already initialized and " "trained. Full pipeline: {pipeline}") E901 = ("Failed to remove existing output directory: {path}. If your " "config and the components you train change between runs, a " "non-empty output directory can lead to stale pipeline data. To " "solve this, remove the existing directories in the output directory.") E902 = ("The sentence-per-line IOB/IOB2 file is not formatted correctly. " "Try checking whitespace and delimiters. See " "https://spacy.io/api/cli#convert") E903 = ("The token-per-line NER file is not formatted correctly. Try checking " "whitespace and delimiters. See https://spacy.io/api/cli#convert") E904 = ("Cannot initialize StaticVectors layer: nO dimension unset. This " "dimension refers to the output width, after the linear projection " "has been applied.") E905 = ("Cannot initialize StaticVectors layer: nM dimension unset. This " "dimension refers to the width of the vectors table.") E906 = ("Unexpected `loss` value in pretraining objective: '{found}'. Supported values " "are: {supported}") E908 = ("Can't set `spaces` without `words` in `Doc.__init__`.") E909 = ("Expected {name} in parser internals. This is likely a bug in spaCy.") E910 = ("Encountered NaN value when computing loss for component '{name}'.") E911 = ("Invalid feature: {feat}. Must be a token attribute.") E912 = ("Failed to initialize lemmatizer. Missing lemmatizer table(s) found " "for mode '{mode}'. Required tables: {tables}. Found: {found}.") E913 = ("Corpus path can't be None. Maybe you forgot to define it in your " "config.cfg or override it on the CLI?") E914 = ("Executing {name} callback failed. Expected the function to " "return the nlp object but got: {value}. Maybe you forgot to return " "the modified object in your function?") E915 = ("Can't use score '{name}' to calculate final weighted score. Expected " "float or int but got: {score_type}. To exclude the score from the " "final score, set its weight to null in the [training.score_weights] " "section of your training config.") E916 = ("Can't log score for '{name}' in table: not a valid score ({score_type})") E917 = ("Received invalid value {value} for `state_type` in " "TransitionBasedParser: only 'parser' or 'ner' are valid options.") E918 = ("Received invalid value for vocab: {vocab} ({vocab_type}). Valid " "values are an instance of `spacy.vocab.Vocab` or True to create one" " (default).") E919 = ("A textcat `positive_label` '{pos_label}' was provided for training " "data that does not appear to be a binary classification problem " "with two labels. Labels found: {labels}") E920 = ("The textcat's `positive_label` setting '{pos_label}' " "does not match any label in the training data or provided during " "initialization. Available labels: {labels}") E921 = ("The method `set_output` can only be called on components that have " "a Model with a `resize_output` attribute. Otherwise, the output " "layer can not be dynamically changed.") E922 = ("Component '{name}' has been initialized with an output dimension of " "{nO} - cannot add any more labels.") E923 = ("It looks like there is no proper sample data to initialize the " "Model of component '{name}'. To check your input data paths and " "annotation, run: python -m spacy debug data config.cfg") E924 = ("The '{name}' component does not seem to be initialized properly. " "This is likely a bug in spaCy, so feel free to open an issue: " "https://github.com/explosion/spaCy/issues") 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 can only be serialized to/from from a directory, " "but the provided argument {loc} points to a file.") E929 = ("Couldn't read KnowledgeBase from {loc}. The path does not seem to exist.") E930 = ("Received invalid get_examples callback in `{method}`. " "Expected function that returns an iterable of Example objects but " "got: {obj}") E931 = ("Encountered {parent} subclass without `{parent}.{method}` " "method in component '{name}'. If you want to use this " "method, make sure it's overwritten on the subclass.") 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 obsolete 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}': " "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 = ("Unable to align tokens for the predicted and reference docs. It " "is only possible to align the docs when both texts are the same " "except for whitespace and capitalization. The predicted tokens " "start with: {x}. The reference tokens start with: {y}.") 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 any valid input from an upstream " "component.") E955 = ("Can't find table(s) {table} for language '{lang}' in " "spacy-lookups-data. Make sure you have the package installed or " "provide your own lookup tables if no default lookups are available " "for your language.") 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}). " "If you want to create an nlp object from a config, make sure to " "use the matching subclass with the language-specific settings and " "data.") 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: " "https://github.com/explosion/spaCy/issues") E978 = ("The {name} method takes a list of Example objects, but got: {types}") 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 `Token.ent_iob` attribute 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? ") 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 `nlp.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 the nlp object 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}") 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 = ("The Chinese word segmenter is pkuseg but no pkuseg model was " "loaded. Provide the name of a pretrained model or the path to " "a model and initialize the pipeline:\n\n" 'nlp.tokenizer.initialize(pkuseg_model="default")') 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}. Maybe you forgot to " "call `nlp.initialize()` to load in the data?") E1005 = ("Unable to set attribute '{attr}' in tokenizer exception for " "'{chunk}'. Tokenizer exceptions are only allowed to specify " "ORTH and NORM.") E1007 = ("Unsupported DependencyMatcher operator '{op}'.") E1008 = ("Invalid pattern: each pattern should be a list of dicts. Check " "that you are providing a list of patterns as `List[List[dict]]`.") E1010 = ("Unable to set entity information for token {i} which is included " "in more than one span in entities, blocked, missing or outside.") E1011 = ("Unsupported default '{default}' in `doc.set_ents`. Available " "options: {modes}") E1012 = ("Entity spans and blocked/missing/outside spans should be " "provided to `doc.set_ents` as lists of Span objects.") E1013 = ("Invalid morph: the MorphAnalysis must have the same vocab as the " "token itself. To set the morph from this MorphAnalysis, set from " "the string value with: `token.set_morph(str(other_morph))`.") E1014 = ("Error loading DocBin data. It doesn't look like the data is in " "DocBin (.spacy) format. If your data is in spaCy v2's JSON " "training format, convert it using `python -m spacy convert " "file.json .`.") E1015 = ("Can't initialize model from config: no {value} found. For more " "information, run: python -m spacy debug config config.cfg") E1016 = ("The operators 'OP': '?', '*', and '+' are not supported in " "DependencyMatcher token patterns. The token pattern in " "RIGHT_ATTR should return matches that are each exactly one token " "long. Invalid pattern:\n{node}") E1017 = ("A Doc object requires both 'deps' and 'heads' for dependency " "parses. If no dependency labels are available, provide " "placeholder deps such as `deps=[\"dep\"]*len(heads)`.") E1018 = ("Knowledge base for component '{name}' is not set. " "Make sure either `nel.initialize` or `nel.set_kb` " "is called with a `kb_loader` function.") # 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)