flair.data

class flair.data.BoundingBox(left, top, right, bottom)View on GitHub

Bases: tuple

left: str

Alias for field number 0

top: int

Alias for field number 1

right: int

Alias for field number 2

bottom: int

Alias for field number 3

class flair.data.Dictionary(add_unk=True)View on GitHub

Bases: object

This class holds a dictionary that maps strings to IDs, used to generate one-hot encodings of strings.

remove_item(item)View on GitHub

add_item(item)View on GitHub

Add string - if already in dictionary returns its ID. if not in dictionary, it will get a new ID.

Parameters:

item (str) – a string for which to assign an id.

Return type:

int

Returns: ID of string

get_idx_for_item(item)View on GitHub

Returns the ID of the string, otherwise 0.

Parameters:

item (str) – string for which ID is requested

Return type:

int

Returns: ID of string, otherwise 0

get_idx_for_items(items)View on GitHub

Returns the IDs for each item of the list of string, otherwise 0 if not found.

Parameters:

items (List[str]) – List of string for which IDs are requested

Return type:

List[int]

Returns: List of ID of strings

get_items()View on GitHub

Return type:

List[str]

get_item_for_index(idx)View on GitHub

set_start_stop_tags()View on GitHub

is_span_prediction_problem()View on GitHub

Return type:

bool

start_stop_tags_are_set()View on GitHub

Return type:

bool

save(savefile)View on GitHub

classmethod load_from_file(filename)View on GitHub

classmethod load(name)View on GitHub

class flair.data.Label(data_point, value, score=1.0)View on GitHub

Bases: object

This class represents a label.

Each label has a value and optionally a confidence score. The score needs to be between 0.0 and 1.0. Default value for the score is 1.0.

set_value(value, score=1.0)View on GitHub

property value: str

property score: float

to_dict()View on GitHub

property shortstring

property labeled_identifier

property unlabeled_identifier

class flair.data.DataPointView on GitHub

Bases: object

This is the parent class of all data points in Flair.

Examples for data points are Token, Sentence, Image, etc. Each DataPoint must be embeddable (hence the abstract property embedding() and methods to() and clear_embeddings()). Also, each DataPoint may have Labels in several layers of annotation (hence the functions add_label(), get_labels() and the property ‘label’)

abstract property embedding

set_embedding(name, vector)View on GitHub

get_embedding(names=None)View on GitHub

Return type:

Tensor

get_each_embedding(embedding_names=None)View on GitHub

Return type:

List[Tensor]

to(device, pin_memory=False)View on GitHub

clear_embeddings(embedding_names=None)View on GitHub

has_label(type)View on GitHub

Return type:

bool

add_metadata(key, value)View on GitHub

Return type:

None

get_metadata(key)View on GitHub

Return type:

Any

has_metadata(key)View on GitHub

Return type:

bool

add_label(typename, value, score=1.0)View on GitHub

set_label(typename, value, score=1.0)View on GitHub

remove_labels(typename)View on GitHub

get_label(label_type=None, zero_tag_value='O')View on GitHub

get_labels(typename=None)View on GitHub

property labels: List[Label]

abstract property unlabeled_identifier

abstract property start_position: int

abstract property end_position: int

abstract property text

property tag

property score

class flair.data.Token(text, head_id=None, whitespace_after=1, start_position=0, sentence=None)View on GitHub

Bases: _PartOfSentence

This class represents one word in a tokenized sentence.

Each token may have any number of tags. It may also point to its head in a dependency tree.

property idx: int

property text: str

property unlabeled_identifier: str

add_tags_proba_dist(tag_type, tags)View on GitHub

get_tags_proba_dist(tag_type)View on GitHub

Return type:

List[Label]

get_head()View on GitHub

property start_position: int

property end_position: int

property embedding

add_label(typename, value, score=1.0)View on GitHub

set_label(typename, value, score=1.0)View on GitHub

to_dict(tag_type=None)View on GitHub

class flair.data.Span(tokens)View on GitHub

Bases: _PartOfSentence

This class represents one textual span consisting of Tokens.

property start_position: int

property end_position: int

property text: str

property unlabeled_identifier: str

property embedding

to_dict(tag_type=None)View on GitHub

class flair.data.Relation(first, second)View on GitHub

Bases: _PartOfSentence

property tag

property text

property unlabeled_identifier: str

property start_position: int

property end_position: int

property embedding

to_dict(tag_type=None)View on GitHub

class flair.data.Sentence(text, use_tokenizer=True, language_code=None, start_position=0)View on GitHub

Bases: DataPoint

A Sentence is a list of tokens and is used to represent a sentence or text fragment.

__init__(text, use_tokenizer=True, language_code=None, start_position=0)View on GitHub

Class to hold all metadata related to a text.

Metadata can be tokens, labels, predictions, language code, etc.

Parameters:

• text (Union[str, List[str], List[Token]]) – original string (sentence), or a pre tokenized list of tokens.

• use_tokenizer (Union[bool, Tokenizer]) – Specify a custom tokenizer to split the text into tokens. The Default is flair.tokenization.SegTokTokenizer. If use_tokenizer is set to False, flair.tokenization.SpaceTokenizer will be used instead. The tokenizer will be ignored, if text refers to pretokenized tokens.

• language_code (Optional[str]) – Language of the sentence. If not provided, langdetect will be called when the language_code is accessed for the first time.

• start_position (int) – Start char offset of the sentence in the superordinate document.

property unlabeled_identifier

get_relations(label_type=None)View on GitHub

Return type:

List[Relation]

get_spans(label_type=None)View on GitHub

Return type:

List[Span]

get_token(token_id)View on GitHub

Return type:

Optional[Token]

property embedding

to(device, pin_memory=False)View on GitHub

clear_embeddings(embedding_names=None)View on GitHub

left_context(context_length, respect_document_boundaries=True)View on GitHub

Return type:

List[Token]

right_context(context_length, respect_document_boundaries=True)View on GitHub

Return type:

List[Token]

to_tagged_string(main_label=None)View on GitHub

Return type:

str

property text

to_tokenized_string()View on GitHub

Return type:

str

to_plain_string()View on GitHub

infer_space_after()View on GitHub

Heuristics in case you wish to infer whitespace_after values for tokenized text.

This is useful for some old NLP tasks (such as CoNLL-03 and CoNLL-2000) that provide only tokenized data with no info of original whitespacing. :return:

to_original_text()View on GitHub

Return type:

str

to_dict(tag_type=None)View on GitHub

get_span(start, stop)View on GitHub

property start_position: int

property end_position: int

get_language_code()View on GitHub

Return type:

str

next_sentence()View on GitHub

Get the next sentence in the document.

This only works if context is set through dataloader or elsewhere :return: next Sentence in document if set, otherwise None

previous_sentence()View on GitHub

Get the previous sentence in the document.

works only if context is set through dataloader or elsewhere :return: previous Sentence in document if set, otherwise None

is_context_set()View on GitHub

Determines if this sentence has a context of sentences before or after set.

Return True or False depending on whether context is set (for instance in dataloader or elsewhere) :rtype: bool :return: True if context is set, else False

copy_context_from_sentence(sentence)View on GitHub

Return type:

None

classmethod set_context_for_sentences(sentences)View on GitHub

Return type:

None

get_labels(label_type=None)View on GitHub

remove_labels(typename)View on GitHub

class flair.data.DataPair(first, second)View on GitHub

Bases: DataPoint, Generic[DT, DT2]

to(device, pin_memory=False)View on GitHub

clear_embeddings(embedding_names=None)View on GitHub

property embedding

property unlabeled_identifier

property start_position: int

property end_position: int

property text

class flair.data.Image(data=None, imageURL=None)View on GitHub

Bases: DataPoint

property embedding

property start_position: int

property end_position: int

property text: str

property unlabeled_identifier: str

class flair.data.Corpus(train=None, dev=None, test=None, name='corpus', sample_missing_splits=True)View on GitHub

Bases: Generic[T_co]

property train: Dataset[T_co] | None

property dev: Dataset[T_co] | None

property test: Dataset[T_co] | None

downsample(percentage=0.1, downsample_train=True, downsample_dev=True, downsample_test=True)View on GitHub

filter_empty_sentences()View on GitHub

filter_long_sentences(max_charlength)View on GitHub

make_vocab_dictionary(max_tokens=-1, min_freq=1)View on GitHub

Creates a dictionary of all tokens contained in the corpus.

By defining max_tokens you can set the maximum number of tokens that should be contained in the dictionary. If there are more than max_tokens tokens in the corpus, the most frequent tokens are added first. If min_freq is set to a value greater than 1 only tokens occurring more than min_freq times are considered to be added to the dictionary.

Parameters:

• max_tokens – the maximum number of tokens that should be added to the dictionary (-1 = take all tokens)

• min_freq – a token needs to occur at least min_freq times to be added to the dictionary (-1 = there is no limitation)

Return type:

Dictionary

Returns: dictionary of tokens

obtain_statistics(label_type=None, pretty_print=True)View on GitHub

Print statistics about the class distribution and sentence sizes.

only labels of sentences are taken into account

Return type:

Union[dict, str]

make_label_dictionary(label_type, min_count=-1, add_unk=False, add_dev_test=False)View on GitHub

Creates a dictionary of all labels assigned to the sentences in the corpus.

Return type:

Dictionary

Returns:

dictionary of labels

add_label_noise(label_type, labels, noise_share=0.2, split='train', noise_transition_matrix=None)View on GitHub

Generates uniform label noise distribution in the chosen dataset split.

Parameters:

• label_type (str) – the type of labels for which the noise should be simulated.

• labels (List[str]) – an array with unique labels of said type (retrievable from label dictionary).

• noise_share (float) – the desired share of noise in the train split.

• split (str) – in which dataset split the noise is to be simulated.

• noise_transition_matrix (Optional[Dict[str, List[float]]]) – provides pre-defined probabilities for label flipping based on the initial label value (relevant for class-dependent label noise simulation).

get_label_distribution()View on GitHub

get_all_sentences()View on GitHub

Return type:

ConcatDataset

make_tag_dictionary(tag_type)View on GitHub

Create a tag dictionary of a given label type.

Parameters:

tag_type (str) – the label type to gather the tag labels

Return type:

Dictionary

Returns: A Dictionary containing the labeled tags, including “O” and “<START>” and “<STOP>”

Deprecated since version 0.8: Use ‘make_label_dictionary’ instead.

class flair.data.MultiCorpus(corpora, task_ids=None, name='multicorpus', **corpusargs)View on GitHub

Bases: Corpus

class flair.data.FlairDataset(*args, **kwds)View on GitHub

Bases: Dataset

abstract is_in_memory()View on GitHub

Return type:

bool

class flair.data.ConcatFlairDataset(datasets, ids)View on GitHub

Bases: Dataset

Dataset as a concatenation of multiple datasets.

This class is useful to assemble different existing datasets.

Parameters:

datasets (sequence) – List of datasets to be concatenated

static cumsum(sequence)View on GitHub

datasets: List[Dataset]

cumulative_sizes: List[int]

property cummulative_sizes

flair.data.iob2(tags)View on GitHub

Converts the tags to the IOB2 format.

Check that tags have a valid IOB format. Tags in IOB1 format are converted to IOB2.

flair.data.randomly_split_into_two_datasets(dataset, length_of_first)View on GitHub

flair.data.get_spans_from_bio(bioes_tags, bioes_scores=None)View on GitHub

Return type:

List[Tuple[List[int], float, str]]