h&  SSKrSSKJr "SS\5rSrS\SS \4S jrS r"S S \S9r "SS\S9r SSSSSSSSSSSS. r "SS\ 5r g)N)Literalc(^\rSrSrU4SjrSrU=r$)ErrorsWithCodescp>[TU]U5nURS5(aU$SRXS9$)N__z[{code}] {msg})codemsg)super__getattribute__ startswithformat)selfr r __class__s F/home/james-whalen/.local/lib/python3.13/site-packages/spacy/errors.pyr ErrorsWithCodes.__getattribute__s:g&t, ??4 J#***> >)__name__ __module__ __qualname____firstlineno__r __static_attributes__ __classcell__)rs@rrrs ??rrc[SSS9 [SSS9 SH(n[S[RRUS9S9 M* [S[RS9 [SSS9 g) Nignoreznumpy.dtype size changed) error_msgznumpy.ufunc size changed)matcher entity_ruler span_ruleronce)namez[W114])filter_warningWarningsW036rW108)pipes rsetup_default_warningsr)s\8'AB8'AB:v)=)=4)=)HI:6X]]36X.raction)defaulterrorralwaysmoduler"rc@[R"U[U5S9 g)zCustomize how spaCy should handle a certain warning. error_msg (str): e.g. "W006", or a full error message action (str): "default", "error", "ignore", "always", "module" or "once" )messageN)warningsfilterwarnings_escape_warning_msg)r*rs rr$r$s F,? ,JKrcFURSS5RSS5$)zJTo filter with warnings.filterwarnings, the [] brackets need to be escaped[z\[]z\])replace)r s rr3r3+s ;;sE " * *3 66rc\rSrSrSrSrSrSrSrSr Sr S r S r S r S rS rSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSr Sr!S r"S!r#S"r$S#r%S$r&S%r'S&r(S'r)S(r*S)r+S*r,S+r-S,r.S-r/S.r0S/r1S0r2S1r3S2r4S3r5S4r6S5r7S6r8S7r9S8r:S9r;g:);r%2a!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: https://spacy.io/usage/modelszNo 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.aThe 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.z3Evaluating {obj}.similarity based on empty vectors.a>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.aA 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))`.z5Alias '{alias}' already exists in the Knowledge Base.zVEntity '{entity}' already exists in the Knowledge Base - ignoring the duplicate entry.zqUnexpected hash collision in PhraseMatcher. Matches may be incorrect. Modify PhraseMatcher._terminal_hash to fix.zUEntity '{entity}' - Alias '{alias}' combination already exists in the Knowledge Base.zUnable 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.zFound 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.zDoc.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.zSome 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.aETraining 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} Load the table in your config with: [initialize.lookups] @misc = "spacy.LookupsDataLoader.v1" lang = ${{nlp.lang}} tables = ["lexeme_norm"] zODiscarding subpattern '{pattern}' due to an unrecognized attribute or operator.z:The component '{name}' does not have any patterns defined.awComponent '{listener}' will be (re)trained, but it needs the component '{name}' which is frozen. If you want to prevent retraining '{name}' but want to train '{listener}' on top of it, you should add '{name}' to the list of 'annotating_components' in the 'training' block in the config. See the documentation for details: https://spacy.io/usage/training#annotating-componentsaDComponent '{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-listenersa!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#initializezWAs of spaCy v3.0, the `nlp.begin_training` method has been renamed to `nlp.initialize`.z5Could not locate any {format} files in path '{path}'.z:Could not clean/remove the temp directory at {dir}: {msg}.zFIgnoring annotations for sentence starts, as dependency heads are set.zWCould not find any data to train the {name} on. Is your input data correctly formatted?aModel '{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}aWModel '{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 validatezRThe method `nlp.disable_pipes` is now deprecated - use `nlp.select_pipes` instead.zSkipping unsupported morphological feature(s): '{feature}'. Provide features as a dict {{"Field1": "Value1,Value2"}} or string "Field1=Value1,Value2|Field2=Value3".z:Skipping Doc custom extension '{name}' while merging docs.zCSkipping unsupported user data '{key}: {value}' while merging docs.zmUnknown {lang} word segmenter '{segmenter}'. Supported word segmenters: {supported}. Defaulting to {default}.zVSkipping modifications for '{target}' segmenter. The current segmenter is '{current}'.zAs 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.zTThe property `Doc.{prop}` is deprecated. Use `Doc.has_annotation("{attr}")` instead.zThe rule-based lemmatizer did not find POS annotation for one or more tokens. Check that your pipeline includes components that assign token.pos, typically 'tagger'+'attribute_ruler' or 'morphologizer'.zpUnable to save user hooks while serializing the doc. Re-add any required user hooks to the doc after processing.zThe 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.zJupyter 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-gpuzkThe model specified to use for initial vectors ({name}) has no vectors. This is almost certainly a mistake.zrSourced component '{name}' may not work as expected: source vectors are not identical to current pipeline vectors.zPUsing multiprocessing with GPU models is not recommended and may lead to errors.zlSkipping {method}: the floret vector table cannot be modified. Vectors are calculated from character ngrams.z#Unable to clean attribute '{attr}'.aNo spans to visualize found in Doc object with spans_key: '{spans_key}'. If this is surprising to you, make sure the Doc was processed using a model that supports span categorization, and check the `doc.spans[spans_key]` property manually if necessary. Available keys: {keys}zTerm '{term}' not found in glossary. It may however be explained in documentation for the corpora used to train the language. Please check `nlp.meta["sources"]` for any relevant links.zXOverriding pipe name in `config` is not supported. Ignoring override '{name_in_config}'.zUnable to load all spans in Doc.spans: more than one span group with the name '{group_name}' was found in the saved spans data. Only the last span group will be loaded under Doc.spans['{group_name}']. Skipping span group with values: {group_values}zCAttempting to trace non-existent method '{method}' in pipe '{pipe}'zpCouldn't trace method '{method}' in pipe '{pipe}'. This can happen if the pipe class is a Cython extension type.zArgument `enable` with value {enable} does not contain all values specified in the config option `enabled` ({enabled}). Be aware that this might affect other components in your pipeline.za{host}:{port} is already in use, using the nearest available port {serve_port} as an alternative.zThe StaticVectors key_attr is no longer used. To set a custom key attribute for vectors, configure it through Vectors(attr=) or 'spacy init vectors --attr'z)These keys are unsupported: {unsupported}z?Not all `Language.pipe` worker processes completed successfullyrN)D gD D RD *D hD oD *D 8D MDrr%) metaclasscv\rSrSrSrSrSrSrSrSr Sr S r S r S r S rS rSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSr Sr!S r"S!r#S"r$S#r%S$r&S%r'S&r(S'r)S(r*S)r+S*r,S+r-S,r.S-r/S.r0S/r1S0r2S1r3S2r4S3r5S4r6S5r7S6r8S7r9S8r:S9r;S:rS=r?S>r@S?rAS@rBSArCSBrDSCrESDrFSErGSFrHSGrISHrJSIrKSJrLSKrMSLrNSMrOSNrPSOrQSPrRSQrSSRrTSSrUSTrVSUrWSVrXSWrYSXrZSYr[SZr\S[r]S\r^S]r_S^r`S_raS`rbSarcSbrdScreSdrfSergSfrhSgriShrjSirkSjrlSkrmSlrnSmroSnrpSorqSprrSqrsSrrtSsruStrvSurwSvrxSwrySxrzSyr{Szr|S{r}S|r~S}rS~rSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSrSGrSGrGSGrGSGrGSGrGSGrGSGrGSGrGSGrGSGr GSGr GS Gr GS Gr GS Gr GS GrGS GrGSGrGSGrGSGrGSGrGSGrGSGrGSGrGSGrGSGrGSGrGSGrGSGrGSGrGSGrGSGrGSGrGSGr GSGr!GS Gr"GS!Gr#GS"Gr$GS#Gr%GS$Gr&GS%Gr'GS&Gr(GS'Gr)GS(Gr*GS)Gr+GS*Gr,GS+Gr-GS,Gr.GS-Gr/GS.Gr0GS/Gr1GS0Gr2GS1Gr3GS2Gr4GS3Gr5GS4Gr6GS5Gr7GS6Gr8GS7Gr9GS8Gr:GS9Gr;GS:GrGS=Gr?GS>Gr@GS?GrAGS@GrBGSAGrCGSBGrDGSCGrEGSDGrFGSEGrGGSFGrHGSGGrIGSHGrJGSIGrKGSJGrLGSKGrMGSLGrNGgM(NErrorsz@No component '{name}' found in pipeline. Available names: {opts}aCan'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 custom component, make sure you've added the decorator `@Language.component` (for function components) or `@Language.factory` (for class components). Available factories: {opts}zNot 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.zyCan't set up pipeline component: a factory for '{name}' already exists. Existing factory: {func}. New factory: {new_func}zPipeline component '{name}' returned {returned_type} instead of a Doc. If you're using a custom component, maybe you forgot to return the processed Doc?zInvalid 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}z;'{name}' already exists in pipeline. Existing names: {opts}zCan'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.zWord 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: https://spacy.io/usage/modelsz)Unknown operator: '{op}'. Options: {opts}z9Cannot add pattern for zero tokens to matcher. Key: {key}z_MultitaskObjective target should be function or one of: dep, tag, ent, dep_tag_offset, ent_tag.z5Can only add unicode or bytes. Got type: {value_type}zqCan't retrieve string for hash '{hash_value}'. This usually refers to an issue with the `Vocab` or `StringStore`.znCan't create transition with unknown action ID: {action}. Action IDs are enumerated in spacy/syntax/{src}.pyx.zDCould not find a transition with the name '{name}' in the NER model.aCould 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: python -m spacy debug data --helpz6String is too long: {length} characters. Max is 2**30.zOError accessing token at position {i}: out of bounds in Doc of length {length}.zArguments `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.zJ`words` expects a list of unicode strings, but got bytes instance: {value}z`noun_chunks` requires the dependency parse, which requires a statistical model to be installed and loaded. For more info, see the documentation: https://spacy.io/usage/modelszSentence 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`.z1Invalid token: empty string ('') at position {i}.z2Cannot load into non-empty Doc of length {length}.zPError creating span with start {start} and end {end} for Doc of length {length}.zPError calculating span: Can't find a token starting at character offset {start}.zLError calculating span: Can't find a token ending at character offset {end}.zArray 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/issuesz8Attempt to access token at {i}, max length {max_length}.z7Invalid comparison operator: {op}. Likely a Cython bug?zAError accessing `doc[{i}].nbor({j})`, for doc of length {length}.zkRefusing to write to token.sent_start if its document is parsed, because this may cause inconsistent state.zNInvalid value for token.sent_start: {value}. Must be one of: None, True, FalsezInvalid displaCy render wrapper. Expected callable, got: {obj}a: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.axPickling 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.z)All subtokens must have associated heads.zdThe newly split tokens must match the text of the original token. New orths: {new}. Old text: {old}.zThe 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.a^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.z~Can't set custom extension attributes during retokenization. Expected dict mapping attribute names to values, but got: {value}z`Can't bulk merge spans. Attribute length {attr_len} should be equal to span length ({span_len}).z1Cannot find token to be split. Did it get merged?z9Cannot find head of token to be split. Did it get merged?zUnexpected value: {value}z}Unexpected matcher predicate: '{bad}'. Expected one of: {good}. This is likely a bug in spaCy, so feel free to open an issue.zYou 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`.zThe vectors for entities and probabilities for alias '{alias}' should have equal length, but found {entities_length} and {probabilities_length} respectively.zXThe sum of prior probabilities for alias '{alias}' should not exceed 1, but found {sum}.z7Entity '{entity}' is not defined in the Knowledge Base.z/Knowledge base for component '{name}' is empty.zWThe list of entities, prior probabilities and entity vectors should be of equal length.zNEntity vectors should be of length {required} instead of the provided {found}.zLabels 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.z(Error reading `{param}` from input file.zCould not access {path}.zUnexpected 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/issueszvExpected {ents} KB identifiers but got {ids}. Make sure that each entity in `doc.ents` is assigned to a KB identifier.zmError deserializing model. Check that the config used to create the component matches the model being loaded.zrThe language of the `nlp` object and the `vocab` should be the same, but found '{nlp}' and '{vocab}' respectively.zThe attribute {attr} is not supported for token patterns. Please use the option `validate=True` with the Matcher, PhraseMatcher, EntityRuler or AttributeRuler for more details.zThe value type {vtype} is not supported for token patterns. Please use the option validate=True with Matcher, PhraseMatcher, EntityRuler or AttributeRuler for more details.zOne 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.zThe 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())`.zCan't render negative values for dependency arc start or end. Make sure that you're passing in absolute token indices, not relative token offsets. start: {start}, end: {end}, label: {label}, direction: {dir}z>Can't add table '{name}' to lookups because it already exists.z@Can't find table '{name}' in lookups. Available tables: {tables}z%Can't find language data file: {path}zFound 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/issueszLcumsum was found to be unstable: its last element does not correspond to sumz,x is neither increasing nor decreasing: {x}.z^Only one class present in the gold labels: {label}. ROC AUC score is not defined in that case.ziCan only merge DocBins with the same value for '{param}'. Current DocBin: {current} Other DocBin: {other}zCan't find module: {module}z>Cannot apply transition {name}: invalid for the current state.ziMatcher.add received invalid 'on_match' callback argument: expected callable or None, but got: {arg_type}z5Can't remove rule for unknown match pattern ID: {key}z5Alias '{alias}' is not defined in the Knowledge Base.z$Ill-formed IOB input detected: {tag}aEach 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])`zInvalid 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])`zSpan 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}zkReceived invalid attributes for unknown object {obj}: {attrs}. Only Doc and Token attributes are supported.zpReceived invalid attribute declaration: {attr} Did you forget to define the attribute? For example: `{attr}.???`zoReceived invalid attribute declaration: {attr} Only top-level attributes are supported, for example: {solution}zOnly attributes without underscores are supported in component attribute declarations (because underscore and non-underscore attributes are connected anyways): {attr} -> {solution}zyReceived invalid attribute in component attribute declaration: `{obj}.{attr}` Attribute '{attr}' does not exist on {obj}.z-Only unicode strings are supported as labels.z:Each argument to `Doc.__init__` should be of equal length.zToken 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.zKInvalid head: the head token must be from the same doc as the token itself.z,Unable to resize vectors in place with cupy.zUnable to resize vectors in place if the resized vector dimension ({new_dim}) is not the same as the current vector dimension ({curr_dim}).z?Unable to aligned mismatched text '{text}' and words '{words}'.z0Matcher can be called on {good} only, got {got}.zkRefusing to write to `token.is_sent_end`. Sentence boundaries can only be fixed with `token.is_sent_start`.z9Row out of bounds, unable to add row {row} for key {key}.ziUnable to return {n} most similar vectors for the current vectors table, which contains {n_rows} vectors.z6Unable to merge 0-length span at `doc[{start}:{end}]`.zCan't set {attr} from Span.z;Unsupported {name} mode '{mode}'. Supported modes: {modes}.zpIf the {name} embedding layer is not updated during training, make sure to include it in 'annotating components'z_The vocab only supports {method} for vectors of type spacy.vectors.Vectors, not {vectors_type}.zdThe PretrainVectors objective currently only supports default or floret vectors, not {mode} vectors.z]The 'textcat' component labels should only have values of 0 or 1, but found value of '{val}'.zaUnsupported component factory name '{name}'. The character '.' is not permitted in factory names.zWUnable to set doc.ents. Check that the 'ents_filter' does not permit overlapping spans.z.Invalid {obj}: {obj} is not from the same doc.zUError accessing span at position {i}: out of bounds in span group of length {length}.z8Entry '{name}' not found in edit tree lemmatizer labels.zFThe {mode} vector table does not support this operation. {alternative}z+The floret vector table cannot be modified.zCan't truncate floret vectors.zUNo 'keys' should be provided when initializing floret vectors with 'minn' and 'maxn'.z4'hash_count' must be between 1-4 for floret vectors.z/'maxn' must be greater than or equal to 'minn'.zJThe complete vector table 'data' is required to initialize floret vectors.zA SpanGroup is not functional after the corresponding Doc has been garbage collected. To keep using the spans, make sure that the corresponding Doc object is still available in the scope of your function.z5Expected a string or 'Doc' as input, but got: {type}.a9The 'textcat' component requires at least two labels because it uses mutually exclusive classes where exactly one label is True for each doc. For binary classification tasks, you can use two labels with 'textcat' (LABEL / NOT_LABEL) or alternatively, you can use the 'textcat_multilabel' component with one label.zFound 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}.zThe 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.aCould 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'.z {tok2vec} listeners: path '{path}' not found in config for component '{name}'.zCan't replace {name} -> {tok2vec} listeners: the paths to replace ({paths}) don't match the available listeners in the model ({n_listeners}).zCan'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.zCan'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.z_Cannot add the alias '{alias}' to the Knowledge base. Each alias should be a meaningful string.zXAlias '{alias}' could not be added to the Knowledge base. This is likely a bug in spaCy.zBUnknown function registry: '{name}'. Available names: {available}a,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. Available names: {available}zKThe 'noun_chunks' syntax iterator is not implemented for language '{lang}'.zThe '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}zThere was an error using the static vectors. Ensure that the vectors of the vocab are properly initialized, or set 'include_static_vectors' to False.zField '{field}' should be a dot-notation string referring to the relevant section in the config, but found type {type} instead.zCan'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.zCan'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.zCould not run the full pipeline for evaluation. If you specified frozen components, make sure they were already initialized and trained. Full pipeline: {pipeline}zFailed 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.zThe sentence-per-line IOB/IOB2 file is not formatted correctly. Try checking whitespace and delimiters. See https://spacy.io/api/cli#convertzThe token-per-line NER file is not formatted correctly. Try checking whitespace and delimiters. See https://spacy.io/api/cli#convertzCannot initialize StaticVectors layer: nO dimension unset. This dimension refers to the output width, after the linear projection has been applied.zsCannot initialize StaticVectors layer: nM dimension unset. This dimension refers to the width of the vectors table.z^Unexpected `loss` value in pretraining objective: '{found}'. Supported values are: {supported}z5Can't set `spaces` without `words` in `Doc.__init__`.zCExpected {name} in parser internals. This is likely a bug in spaCy.zAEncountered NaN value when computing loss for component '{name}'.z3Invalid feature: {feat}. Must be a token attribute.zFailed to initialize lemmatizer. Missing lemmatizer table(s) found for mode '{mode}'. Required tables: {tables}. Found: {found}.zeCorpus path can't be None. Maybe you forgot to define it in your .cfg file or override it on the CLI?zExecuting {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?zCan'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.zGCan't log score for '{name}' in table: not a valid score ({score_type})zsReceived invalid value {value} for `state_type` in TransitionBasedParser: only 'parser' or 'ner' are valid options.zReceived invalid value for vocab: {vocab} ({vocab_type}). Valid values are an instance of `spacy.vocab.Vocab` or True to create one (default).zA 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}zThe textcat's `positive_label` setting '{pos_label}' does not match any label in the training data or provided during initialization. Available labels: {labels}zThe 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.zfComponent '{name}' has been initialized with an output dimension of {nO} - cannot add any more labels.aIt 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 and include the same config override values you would specify for the 'spacy train' command.zThe '{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/issuesznInvalid color values for displaCy visualizer: expected dictionary mapping label names to colors but got: {obj}aIt 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.zkCan't write to frozen list. Maybe you're trying to modify a computed property or default function argument?zvAn InMemoryLookupKB can only be serialized to/from from a directory, but the provided argument {loc} points to a file.zKCouldn't read InMemoryLookupKB from {loc}. The path does not seem to exist.zReceived invalid get_examples callback in `{method}`. Expected function that returns an iterable of Example objects but got: {obj}zEncountered {parent} subclass without `{parent}.{method}` method in component '{name}'. If you want to use this method, make sure it's overwritten on the subclass.zFound NaN values in scores.axCan'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: nlp = spacy.load("{full}") For more details on the available models, see the models directory: https://spacy.io/models and if you want to create a blank model, use spacy.blank: nlp = spacy.blank("{name}")zExecuting `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?zExecuting `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?zqCan't copy pipeline component '{name}' from source '{model}': not found in pipeline. Available components: {opts}zaCan't copy pipeline component '{name}' from source. Expected loaded nlp object, but got: {source}zj`Matcher.add` received invalid `greedy` argument: expected a string value from {expected} but got: '{arg}'zX`Matcher.add` received invalid 'patterns' argument: expected a list, but got: {arg_type}zUnable 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}.zCThe section '{name}' is not a valid section in the provided config.z@Mismatched IDs received by the Tok2Vec listener: {id1} vs. {id2}zPThe Tok2Vec listener did not receive any valid input from an upstream component.zCan'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.z_Can't find component '{name}' in [components] block in the config. Available components: {opts}aWriting 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.a 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.zECan't insert component {dir} index {idx}. Existing components: {opts}zKNo config data found for component '{name}'. This is likely a bug in spaCy.aFound 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. {config}zMReceived incorrect {style} for pipe '{name}'. Expected dict, got: {cfg_type}.zCan'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}`.a!The pipeline component factory for '{name}' needs to have the following named arguments, which are passed in by spaCy: - nlp: receives the current nlp object and lets you access the vocab - name: the name of the component instance, can be used to identify the component, output losses etc.zIt 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.a`nlp.add_pipe` now takes the string name of the registered component factory, not a callable component. Expected string, but got {component} (name: '{name}'). - If you created your component with `nlp.create_pipe('name')`: remove nlp.create_pipe and call `nlp.add_pipe('name')` instead. - If you passed in a component like `TextCategorizer()`: call `nlp.add_pipe` with the string name instead, e.g. `nlp.add_pipe('textcat')`. - 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.zMNo {meta} meta information found for '{name}'. This is likely a bug in spaCy.a`nlp.replace_pipe` now takes the string name of the registered component factory, not a callable component. Expected string, but got {component}. - If you created your component withwith `nlp.create_pipe('name')`: remove `nlp.create_pipe` and call `nlp.replace_pipe('{name}', 'name')` instead. - If you passed in a component like `TextCategorizer()`: call `nlp.replace_pipe` with the string name instead, e.g. `nlp.replace_pipe('{name}', 'textcat')`. - 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')`.zJExpected string values for field '{field}', but received {types} instead. zHCan not execute command '{str_command}'. Do you have '{tool}' installed?zqFound incompatible lengths in `Doc.from_array`: {array_length} for the array and {doc_length} for the Doc itself.z6`Example.__init__` got None for '{arg}'. Requires Doc.zUnexpected type for NER datazUnknown {obj} attribute: {key}zSThe method `Example.from_dict` expects a {type} as {n} argument, but received None.zCan 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/issueszCThe {name} method takes a list of Example objects, but got: {types}ztEach link annotation should refer to a dictionary with at most one identifier mapping to 1.0, and all others to 0.0.zTThe offsets of the annotations for `links` could not be aligned to token boundaries.z]The `Token.ent_iob` attribute should be an integer indexing into {values}, but found {value}.z:Invalid key(s) for '{dict}': {key}. Available keys: {keys}aInvalid 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. {config}zDCan't load model from config file: no [nlp] section found. {config}zCould not create any training batches: check your input. Are the train and dev paths defined? Is `discard_oversize` set appropriately? a`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. zThe 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.zThe function `select_pipes` was called with `enable`={enable} and `disable`={disable} but that information is conflicting for the `nlp` pipeline with components {names}.zThe 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. {config}zTokenizer special cases are not allowed to modify the text. This would map '{chunk}' to '{orth}' given token attributes '{token_attrs}'.zOUnable to merge the Doc objects because they do not all share the same `Vocab`.zThe 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: nlp.tokenizer.initialize(pkuseg_model="default")zzTarget token outside of matched span for match with tokens '{span}' and offset '{index}' matched by patterns '{patterns}'.zSpan index out of range.z%Unsupported lemmatizer mode '{mode}'.zMissing 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?zUnable to set attribute '{attr}' in tokenizer exception for '{chunk}'. Tokenizer exceptions are only allowed to specify ORTH and NORM.z.Unsupported DependencyMatcher operator '{op}'.zInvalid pattern: each pattern should be a list of dicts. Check that you are providing a list of patterns as `List[List[dict]]`.zUnable to set entity information for token {i} which is included in more than one span in entities, blocked, missing or outside.zMUnsupported default '{default}' in `doc.set_ents`. Available options: {modes}zmEntity spans and blocked/missing/outside spans should be provided to `doc.set_ents` as lists of Span objects.zInvalid 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))`.zError 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 .`.zxCan't initialize model from config: no {value} found. For more information, run: python -m spacy debug config config.cfgzThe 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: {node}zA 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)`.zKnowledge base for component '{name}' is not set. Make sure either `nel.initialize` or `nel.set_kb` is called with a `kb_loader` function.z`noun_chunks` requires the pos tagging, which requires a statistical model to be installed and loaded. For more info, see the documentation: https://spacy.io/usage/modelsziNo `epoch_resume` value specified and could not infer one from filename. Specify an epoch to resume from.zh`pos` value "{pp}" is not a valid Universal Dependencies tag. Non-UD tags should use the `tag` property.z@Words must be of type str or int, but input is of type '{wtype}'zCCouldn't read EntityRuler from the {path}. This file doesn't exist.zNA pattern with {attr_type} '{label}' is not present in '{component}' patterns.zgCannot intify the value '{value}' as an IOB string. The only supported values are: 'I', 'O', 'B' and ''z)Edit tree has an invalid format: {errors}z6AlignmentArray only supports slicing with a step of 1.z>AlignmentArray only supports indexing using an int or a slice.z$Edit tree cannot be applied to form.z"Edit tree identifier out of range.z0Could not find gold transition - see logs above.z8`{var}` should not be {forbidden}, but received {value}.z+Dimension {name} invalid -- only nO, nF, nPz'Node index {i} out of bounds ({length})z(Token index {i} out of bounds ({length})zCannot index into NoneNodez!Invalid attribute value '{attr}'.zInvalid JSON input: {message}ziThe {obj} start or end annotations (start: {start}, end: {end}) could not be aligned to token boundaries.zyDoc.from_json requires all tokens to have the same attributes. Some tokens do not contain annotation for: {partial_attrs}z:Expected a string, Doc, or bytes as input, but got: {type}a`enable={enable}` and `disable={disable}` are inconsistent with each other. If you only passed one of `enable` or `disable`, the other argument is specified in your pipeline's configuration. In that case pass an empty list for the previously not specified argument to avoid this error.zlExpected None or a value in range [{range_start}, {range_end}] for entity linker threshold, but got {value}.z=Expected `candidates_batch_size` to be >= 1, but got: {value}zEncountered {parent} subclass without `{parent}.{method}` method in '{name}'. If you want to use this method, make sure it's overwritten on the subclass.z{cls_name} is an abstract class and cannot be instantiated. If you are looking for spaCy's default knowledge base, use `InMemoryLookupKB`.zF`find_threshold()` only supports components with a `scorer` attribute.z^Got '{unexpected}' as console progress bar type, but expected one of the following: {expected}z{No available port found for displaCy on host {host}. Please specify an available port with `displacy.serve(doc, port=port)`zPort {port} is already in use. Please specify an available port with `displacy.serve(doc, port=port)` or use `auto_select_port=True` to pick an available port automatically.zc'allow_overlap' can only be False when max_positive is 1, but found 'max_positive': {max_positive}.zUnable to copy spans: the character offsets for the span at index {i} in the span group do not align with the tokenization in the target doc.z|Both 'min_length' and 'max_length' should be larger than 0, but found 'min_length': {min_length}, 'max_length': {max_length}zjThe text, including whitespace, must match between reference and predicted docs when training {component}.z~The 'replace_listener' callback expects {num_params} parameters, but only callbacks with one or three parameters are supportedzKThe `TextCatBOW` architecture expects a length of at least 1, was {length}.zThe `TextCatReduce` architecture must be used with at least one reduction. 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