128 lines
61 KiB
Plaintext
128 lines
61 KiB
Plaintext
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y) é Né )ÚLiteralc ó ‡ — e Zd Zˆ fd„Zˆ xZS )ÚErrorsWithCodesc ón •— t ‰| � |« }|j d« r|S dj ||¬« S )NÚ__z[{code}] {msg})ÚcodeÚmsg)ÚsuperÚ__getattribute__Ú
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Ú __class__s €úMC:\Users\garci\AppData\Roaming\Python\Python312\site-packages\spacy/errors.pyr z ErrorsWithCodes.__getattribute__ s9 ø€ ܉gÑ& tÓ,ˆØ �?‰?˜4Ô ØˆJà#×*Ñ*°¸#Ð*Ó>Ð >ó )Ú__name__Ú
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ø„ ÷?ð ?r r c óì — t dd¬« t dd¬« dD ]- } t dt j j | ¬« ¬« Œ/ t dt j ¬« t dd¬« y ) NÚignoreznumpy.dtype size changed)Ú error_msgznumpy.ufunc size changed)ÚmatcherÚ entity_rulerÚ
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span_rulerÚonce)Únamez[W114])Úfilter_warningÚWarningsÚW036r ÚW108)Úpipes r Úsetup_default_warningsr$ s\ € ä�8Ð'AÕBÜ�8Ð'AÕBó :ˆÜ�v¬¯ © ×)=Ñ)=À4Ð)=Ó)HÖIð :ô �6¤X§]¡]Õ3ô �6 XÖ.r Úaction)ÚdefaultÚerrorr ÚalwaysÚmoduler r c óD — t j | t |« ¬« y)zÁCustomize how spaCy should handle a certain warning.
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error_msg (str): e.g. "W006", or a full error message
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action (str): "default", "error", "ignore", "always", "module" or "once"
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)ÚmessageN)ÚwarningsÚfilterwarningsÚ_escape_warning_msg)r% r s r r r s € ô
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×јFÔ,?À Ó,JÖKr c óF — | j dd« j dd« S )zJTo filter with warnings.filterwarnings, the [] brackets need to be escapedú[z\[ú]z\])Úreplace)r
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s r r. r. + s € à �;‰;�s˜EÓ "× *Ñ *¨3°Ó 6Ð6r c óè — e Zd ZdZdZdZdZdZdZdZ dZ
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ZdZdZdZdZdZdZdZdZdZdZdZdZdZ dZ d Z d Z d Z d Z!d Z"d!Z#d"Z$d#Z%d$Z&d%Z'd&Z(d'Z)d(Z*d)Z+d*Z,d+Z-d,Z.d-Z/d.Z0d/Z1d0Z2d1Z3d2Z4d3Z5d4Z6d5Z7d6Z8d7Z9y8)9r a! 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:
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https://spacy.io/usage/modelszÚNo 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.a¸ 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.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.a¢ 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))`.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.zôUnable 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.z•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.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.zïSome 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.aE 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}
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Load the table in your config with:
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[initialize.lookups]
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@misc = "spacy.LookupsDataLoader.v1"
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lang = ${{nlp.lang}}
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tables = ["lexeme_norm"]
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zODiscarding subpattern '{pattern}' due to an unrecognized attribute or operator.z:The component '{name}' does not have any patterns defined.aw Component '{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-componentsaD 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-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?a‚ 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}aW 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 validatezRThe method `nlp.disable_pipes` is now deprecated - use `nlp.select_pipes` instead.z¤Skipping 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.zÊThe 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.zþThe 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.zøJupyter 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
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Available keys: {keys}z¸Term '{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}'.zøUnable 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.zºArgument `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.z›The 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 successfullyN):r r r ÚW005ÚW006ÚW007ÚW008ÚW011ÚW012ÚW017ÚW018ÚW021ÚW024ÚW026ÚW027ÚW028ÚW030ÚW033ÚW035r! ÚW086ÚW087ÚW088ÚW089ÚW090ÚW091ÚW092ÚW093ÚW094ÚW095ÚW096ÚW100ÚW101ÚW102ÚW103ÚW104ÚW105ÚW107r" ÚW109ÚW110ÚW111ÚW112ÚW113ÚW114ÚW115ÚW116ÚW117ÚW118ÚW119ÚW120ÚW121ÚW122ÚW123ÚW124ÚW125ÚW126ÚW127© r r r r 2 sƒ „ ð
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�Z�d�Z�d�Z�d�Z�d�Z�d�Z�d�Z�d�Z�d�Z�d�Z�d�Z�d�Z�d�Z�d�Z �d�Z �d �Z �d �Z �d �Z �d �Z!�d �Z"�d!�Z#�d"�Z$�d#�Z%�d$�Z&�d%�Z'�d&�Z(�d'�Z)�d(�Z*�d)�Z+�d*�Z,�d+�Z-�d,�Z.�d-�Z/�d.�Z0�d/�Z1�d0�Z2�d1�Z3�d2�Z4�d3�Z5�d4�Z6�d5�Z7�d6�Z8�d7�Z9�d8�Z:�d9�Z;�d:�Z<�d;�Z=�d<�Z>�d=�Z?�d>�Z@�d?�ZA�d@�ZB�dA�ZC�dB�ZD�dC�ZE�dD�ZF�dE�ZG�dF�ZH�dG�ZI�dH�ZJ�dI�ZK�dJ�ZL�yK(L ÚErrorsz@No component '{name}' found in pipeline. Available names: {opts}aŽ 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 custom component, make sure you've added the decorator `@Language.component` (for function components) or `@Language.factory` (for class components).
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Available factories: {opts}zÇNot 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}z˜Pipeline component '{name}' returned {returned_type} instead of a Doc. If you're using a custom component, maybe you forgot to return the processed Doc?zôInvalid 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}z÷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.zÌWord 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:
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https://spacy.io/usage/modelsz)Unknown operator: '{op}'. Options: {opts}z9Cannot add pattern for zero tokens to matcher.
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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.a 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:
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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}.zàArguments `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:
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https://spacy.io/usage/modelszöSentence 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}.zµArray 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, Falsez<Possibly infinite loop encountered while looking for {attr}.zlCan't retrieve unregistered extension attribute '{name}'. Did you forget to call the `set_extension` method?zuCan't assign a value to unregistered extension attribute '{name}'. Did you forget to call the `set_extension` method?zLCan't import language {lang} or any matching language from spacy.lang: {err}zfCan't find model '{name}'. It doesn't seem to be a Python package or a valid path to a data directory.z"Can't find model directory: {path}z!Could not read {name} from {path}z6No valid '{setting}' setting found in model meta.json.z:Invalid ORTH value in exception:
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Key: {key}
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Orths: {orths}zhInvalid tokenizer exception: ORTH values combined don't match original string.
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Key: {key}
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Orths: {orths}z[Stepped slices not supported in Span objects. Try: `list(tokens)[start:stop:step]` instead.z(Could not retrieve vector for key {key}.z9One (and only one) keyword arg must be set. Got: {kwargs}zRCannot add new key to vectors: the table is full. Current shape: ({rows}, {cols}).zÆCannot 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`.zTInvalid value for `flag_id`: {value}. Flag IDs must be between 1 and 63 (inclusive).a. 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:
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Query string: {string}
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Orth cached: {orth}
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Orth ID: {orth_id}zeOnly one of the vector table's width and shape can be specified. Got width {width} and shape {shape}.z‚Invalid BILUO tag sequence: Got a tag starting with {start} without a preceding 'B' (beginning of an entity). Tag sequence:
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{tags}zInvalid BILUO tag: '{tag}'.zeError creating lexeme: specified orth ID ({orth}) does not match the one in the vocab ({vocab_orth}).z«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.zsError interpreting compiled match pattern: patterns are expected to end with the attribute {attr}. Got: {bad_attr}.ztError computing states in beam: number of predicted beams ({pbeams}) does not equal number of gold beams ({gbeams}).z%Duplicate state found in beam: {key}.zjError getting gradient in beam: number of histories ({n_hist}) does not equal number of losses ({losses}).z‚Error deprojectivizing parse: number of heads ({n_heads}), projective heads ({n_proj_heads}) and labels ({n_labels}) do not match.z|Error setting extension: only one of `default`, `method`, or `getter` (plus optional `setter`) is allowed. Got: {nr_defined}z=Error assigning label ID {label} to span: not in StringStore.z*Can't create lexeme for string '{string}'.z Unknown displaCy style: {style}.a¬ 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)`.zrExtensions can't have a setter argument without a getter argument. Check the keyword arguments on `set_extension`.z{Extension '{name}' already exists on {obj}. To overwrite the existing extension, set `force=True` on `{obj}.set_extension`.zOInvalid extension attribute {name}: expected callable or None, but got: {value}z=token.ent_iob values make invalid sequence: I without B
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{seq}z4Error reading line {line_num} in vectors file {loc}.zsCan't write to frozen dictionary. This is likely an internal error. Are you writing to a default function argument?znInvalid object passed to displaCy: Can only visualize `Doc` or Span objects, or dicts if set to `manual=True`.zeInvalid pattern: expected token pattern (list of dicts) or phrase pattern (string) but got:
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{pattern}z8Invalid pattern: expected both RIGHT_ID and RIGHT_ATTRS.z{Invalid pattern: the first node of pattern should be an anchor node. The node should only contain RIGHT_ID and RIGHT_ATTRS.z`Nodes other than the anchor node should all contain {required}, but these are missing: {missing}zcRIGHT_ID should be a new node and LEFT_ID should already have have been declared in previous edges.zßCan'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:
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https://spacy.io/api/top-level#util.filter_spanszáTrying 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.zPCan't find `doc._.{attr}` attribute specified in the underscore settings: {opts}zDValue of custom attribute `{attr}` is not JSON-serializable: {value}zKComponent '{name}' could not be run. Did you forget to call `initialize()`?z>Invalid 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.ax 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.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}.z¾The 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.zÜYou 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`.z�The 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}.z±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.z(Error reading `{param}` from input file.zCould not access {path}.z²Unexpected 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.z°The attribute {attr} is not supported for token patterns. Please use the option `validate=True` with the Matcher, PhraseMat
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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}z¥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/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}'.
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Current DocBin: {current}
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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}a 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])`zµInvalid 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])`zÔSpan 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}
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Did you forget to define the attribute? For example: `{attr}.???`zoReceived invalid attribute declaration: {attr}
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Only top-level attributes are supported, for example: {solution}z´Only 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}`
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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.zÿToken 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.z‹Unable 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.z Can'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.zÌA 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}.a9 The '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.z©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}.zÂThe 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.a 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'.z<Error encountered in nlp.pipe with multiprocessing:
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{error}z•Unable to copy tokenizer from base model due to different tokenizer settings: current tokenizer config "{curr_config}" vs. base model "{base_config}"z±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/issueszXCould not initialize the tok2vec model from component '{component}' and layer '{layer}'.z“To use the PretrainVectors objective, make sure that static vectors are loaded. In the config, these are defined by the initialize.vectors setting.zÔUnexpected 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.zõThe 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.zpentity_linker.set_kb received an invalid 'kb_loader' argument: expected a callable function, but got: {arg_type}zfCan't replace {name} -> {tok2vec} listeners: path '{path}' not found in config for component '{name}'.z�Can't replace {name} -> {tok2vec} listeners: the paths to replace ({paths}) don't match the available listeners in the model ({n_listeners}).zîCan'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.zÕCan'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}'.
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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.
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Available names: {available}zKThe 'noun_chunks' syntax iterator is not implemented for language '{lang}'.zØThe '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}z•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.zField '{field}' should be a dot-notation string referring to the relevant section in the config, but found type {type} instead.zØCan'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.zåCan'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.z¢Could not run the full pipeline for evaluation. If you specified frozen components, make sure they were already initialized and trained. Full pipeline: {pipeline}zúFailed 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.zŒThe sentence-per-line IOB/IOB2 file is not formatted correctly. Try checking whitespace and delimiters. See https://spacy.io/api/cli#convertz„The token-per-line NER file is not formatted correctly. Try checking whitespace and delimiters. See https://spacy.io/api/cli#convertz“Cannot 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.z€Failed 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?z£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?zëCan'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.zŽReceived invalid value for vocab: {vocab} ({vocab_type}). Valid values are an instance of `spacy.vocab.Vocab` or True to create one (default).zªA 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}z The textcat's `positive_label` setting '{pos_label}' does not match any label in the training data or provided during initialization. Available labels: {labels}z¨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.zfComponent '{name}' has been initialized with an output dimension of {nO} - cannot add any more labels.a 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 deb
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nlp = spacy.load("{full}")
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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}")z¶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?zñExecuting `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}zõUnable 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.zÊCan'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}a 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.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.a 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.
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