nlp_architect.data package

Subpackages

Submodules

nlp_architect.data.conll module

class nlp_architect.data.conll.ConllEntry(eid, form, lemma, pos, cpos, feats=None, parent_id=None, relation=None, deps=None, misc=None)[source]

Bases: object

nlp_architect.data.conll.normalize(word)[source]

nlp_architect.data.fasttext_emb module

class nlp_architect.data.fasttext_emb.Dictionary(id2word, word2id, lang)[source]

Bases: object

Merges word2idx and idx2word dictionaries :param id2word dictionary: :param word2id dictionary: :param language of the dictionary:

Usage:
dico.index(word) - returns an index dico[index] - returns the word
check_valid()[source]

Check that the dictionary is valid.

index(word)[source]

Returns the index of the specified word.

class nlp_architect.data.fasttext_emb.FastTextEmb(path, language, vocab_size, emb_dim=300)[source]

Bases: object

Downloads FastText Embeddings for a given language to the given path. :param path: Local path to copy embeddings :type path: str :param language: Embeddings language :type language: str :param vocab_size: Size of vocabulary :type vocab_size: int

Returns:Returns a dictionary and reverse dictionary Returns a numpy array with embeddings in emb_sizexvocab_size shape
load_embeddings()[source]
read_embeddings(filepath)[source]
nlp_architect.data.fasttext_emb.get_eval_data(eval_path, src_lang, tgt_lang)[source]

Downloads evaluation cross lingual dictionaries to the eval_path :param eval_path: Path where cross-lingual dictionaries are downloaded :param src_lang: Source Language :param tgt_lang: Target Language

Returns:Path to where cross lingual dictionaries are downloaded

nlp_architect.data.glue_tasks module

class nlp_architect.data.glue_tasks.ColaProcessor[source]

Bases: nlp_architect.data.utils.DataProcessor

Processor for the CoLA data set (GLUE version).

get_dev_examples(data_dir)[source]

Gets a collection of `InputExample`s for the dev set.

get_labels()[source]

Gets the list of labels for this data set.

get_test_examples(data_dir)[source]

Gets a collection of `InputExample`s for the test set.

get_train_examples(data_dir)[source]

Gets a collection of `InputExample`s for the train set.

class nlp_architect.data.glue_tasks.InputFeatures(input_ids, input_mask, segment_ids, label_id, valid_ids=None)[source]

Bases: object

A single set of features of data.

class nlp_architect.data.glue_tasks.MnliMismatchedProcessor[source]

Bases: nlp_architect.data.glue_tasks.MnliProcessor

Processor for the MultiNLI Mismatched data set (GLUE version).

get_dev_examples(data_dir)[source]

Gets a collection of `InputExample`s for the dev set.

get_test_examples(data_dir)[source]

Gets a collection of `InputExample`s for the test set.

class nlp_architect.data.glue_tasks.MnliProcessor[source]

Bases: nlp_architect.data.utils.DataProcessor

Processor for the MultiNLI data set (GLUE version).

get_dev_examples(data_dir)[source]

Gets a collection of `InputExample`s for the dev set.

get_labels()[source]

Gets the list of labels for this data set.

get_test_examples(data_dir)[source]

Gets a collection of `InputExample`s for the test set.

get_train_examples(data_dir)[source]

Gets a collection of `InputExample`s for the train set.

class nlp_architect.data.glue_tasks.MrpcProcessor[source]

Bases: nlp_architect.data.utils.DataProcessor

Processor for the MRPC data set (GLUE version).

get_dev_examples(data_dir)[source]

Gets a collection of `InputExample`s for the dev set.

get_labels()[source]

Gets the list of labels for this data set.

get_test_examples(data_dir)[source]

Gets a collection of `InputExample`s for the test set.

get_train_examples(data_dir)[source]

Gets a collection of `InputExample`s for the train set.

class nlp_architect.data.glue_tasks.QnliProcessor[source]

Bases: nlp_architect.data.utils.DataProcessor

Processor for the QNLI data set (GLUE version).

get_dev_examples(data_dir)[source]

Gets a collection of `InputExample`s for the dev set.

get_labels()[source]

Gets the list of labels for this data set.

get_test_examples(data_dir)[source]

Gets a collection of `InputExample`s for the test set.

get_train_examples(data_dir)[source]

Gets a collection of `InputExample`s for the train set.

class nlp_architect.data.glue_tasks.QqpProcessor[source]

Bases: nlp_architect.data.utils.DataProcessor

Processor for the QQP data set (GLUE version).

get_dev_examples(data_dir)[source]

Gets a collection of `InputExample`s for the dev set.

get_labels()[source]

Gets the list of labels for this data set.

get_test_examples(data_dir)[source]

Gets a collection of `InputExample`s for the test set.

get_train_examples(data_dir)[source]

Gets a collection of `InputExample`s for the train set.

class nlp_architect.data.glue_tasks.RteProcessor[source]

Bases: nlp_architect.data.utils.DataProcessor

Processor for the RTE data set (GLUE version).

get_dev_examples(data_dir)[source]

Gets a collection of `InputExample`s for the dev set.

get_labels()[source]

Gets the list of labels for this data set.

get_test_examples(data_dir)[source]

Gets a collection of `InputExample`s for the test set.

get_train_examples(data_dir)[source]

Gets a collection of `InputExample`s for the train set.

class nlp_architect.data.glue_tasks.Sst2Processor[source]

Bases: nlp_architect.data.utils.DataProcessor

Processor for the SST-2 data set (GLUE version).

get_dev_examples(data_dir)[source]

Gets a collection of `InputExample`s for the dev set.

get_labels()[source]

Gets the list of labels for this data set.

get_test_examples(data_dir)[source]

Gets a collection of `InputExample`s for the test set.

get_train_examples(data_dir)[source]

Gets a collection of `InputExample`s for the train set.

class nlp_architect.data.glue_tasks.StsbProcessor[source]

Bases: nlp_architect.data.utils.DataProcessor

Processor for the STS-B data set (GLUE version).

get_dev_examples(data_dir)[source]

Gets a collection of `InputExample`s for the dev set.

get_labels()[source]

Gets the list of labels for this data set.

get_test_examples(data_dir)[source]

Gets a collection of `InputExample`s for the test set.

get_train_examples(data_dir)[source]

Gets a collection of `InputExample`s for the train set.

class nlp_architect.data.glue_tasks.WnliProcessor[source]

Bases: nlp_architect.data.utils.DataProcessor

Processor for the WNLI data set (GLUE version).

get_dev_examples(data_dir)[source]

Gets a collection of `InputExample`s for the dev set.

get_labels()[source]

Gets the list of labels for this data set.

get_test_examples(data_dir)[source]

Gets a collection of `InputExample`s for the test set.

get_train_examples(data_dir)[source]

Gets a collection of `InputExample`s for the train set.

nlp_architect.data.glue_tasks.convert_examples_to_features(examples, label_list, max_seq_length, tokenizer, output_mode, cls_token_at_end=False, pad_on_left=False, cls_token='[CLS]', sep_token='[SEP]', pad_token=0, sequence_a_segment_id=0, sequence_b_segment_id=1, cls_token_segment_id=1, pad_token_segment_id=0, mask_padding_with_zero=True)[source]

Loads a data file into a list of InputBatch`s `cls_token_at_end define the location of the CLS token:

  • False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]
  • True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]

cls_token_segment_id define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)

nlp_architect.data.glue_tasks.get_glue_task(task_name: str, data_dir: str = None)[source]

Return a GLUE task object :param task_name: name of GLUE task :type task_name: str :param data_dir: path to dataset, if not provided will be taken from

GLUE_DIR env. variable
nlp_architect.data.glue_tasks.get_metric_fn(task_name)[source]

nlp_architect.data.intent_datasets module

class nlp_architect.data.intent_datasets.IntentDataset(sentence_length=50, word_length=12)[source]

Bases: object

Intent extraction dataset base class

Parameters:sentence_length (int) – max sentence length
char_vocab

word character vocabulary

Type:dict
char_vocab_size

char vocabulary size

Type:int
intent_size

intent label vocabulary size

Type:int
intents_vocab

intent labels vocabulary

Type:dict
label_vocab_size

label vocabulary size

Type:int
tags_vocab

labels vocabulary

Type:dict
test_set

test set

Type:tuple of numpy.ndarray
train_set

train set

Type:tuple of numpy.ndarray
word_vocab

tokens vocabulary

Type:dict
word_vocab_size

vocabulary size

Type:int
class nlp_architect.data.intent_datasets.SNIPS(path, sentence_length=30, word_length=12)[source]

Bases: nlp_architect.data.intent_datasets.IntentDataset

SNIPS dataset class

Parameters:
  • path (str) – dataset path
  • sentence_length (int, optional) – max sentence length
  • word_length (int, optional) – max word length
files = ['train', 'test']
test_files = ['AddToPlaylist/validate_AddToPlaylist.json', 'BookRestaurant/validate_BookRestaurant.json', 'GetWeather/validate_GetWeather.json', 'PlayMusic/validate_PlayMusic.json', 'RateBook/validate_RateBook.json', 'SearchCreativeWork/validate_SearchCreativeWork.json', 'SearchScreeningEvent/validate_SearchScreeningEvent.json']
train_files = ['AddToPlaylist/train_AddToPlaylist_full.json', 'BookRestaurant/train_BookRestaurant_full.json', 'GetWeather/train_GetWeather_full.json', 'PlayMusic/train_PlayMusic_full.json', 'RateBook/train_RateBook_full.json', 'SearchCreativeWork/train_SearchCreativeWork_full.json', 'SearchScreeningEvent/train_SearchScreeningEvent_full.json']
class nlp_architect.data.intent_datasets.TabularIntentDataset(train_file, test_file, sentence_length=30, word_length=12)[source]

Bases: nlp_architect.data.intent_datasets.IntentDataset

Tabular Intent/Slot tags dataset loader. Compatible with many sequence tagging datasets (ATIS, CoNLL, etc..) data format must be int tabular format where: - one word per line with tag annotation and intent type separated by tabs <token> <tag_label> <intent>

  • sentences are separated by an empty line
Parameters:
  • train_file (str) – path to train set file
  • test_file (str) – path to test set file
  • sentence_length (int) – max sentence length
  • word_length (int) – max word length
files = ['train', 'test']

nlp_architect.data.ptb module

Data loader for penn tree bank dataset

class nlp_architect.data.ptb.PTBDataLoader(word_dict, seq_len=100, data_dir='/home/runner/data', dataset='WikiText-103', batch_size=32, skip=30, split_type='train', loop=True)[source]

Bases: object

Class that defines data loader

decode_line(tokens)[source]

Decode a given line from index to word :param tokens: List of indexes

Returns:str, a sentence
get_batch()[source]

Get one batch of the data :returns: None

load_series(path)[source]

Load all the data into an array :param path: str, location of the input data file

Returns:

reset()[source]

Resets the sample count to zero, re-shuffles data :returns: None

class nlp_architect.data.ptb.PTBDictionary(data_dir='/home/runner/data', dataset='WikiText-103')[source]

Bases: object

Class for generating a dictionary of all words in the PTB corpus

add_word(word)[source]

Method for adding a single word to the dictionary :param word: str, word to be added

Returns:None
load_dictionary()[source]

Populate the corpus with words from train, test and valid splits of data :returns: None

save_dictionary()[source]

Save dictionary to file :returns: None

nlp_architect.data.sequence_classification module

class nlp_architect.data.sequence_classification.SequenceClsInputExample(guid: str, text: str, text_b: str = None, label: str = None)[source]

Bases: nlp_architect.data.utils.InputExample

A single training/test example for simple sequence classification.

nlp_architect.data.sequential_tagging module

class nlp_architect.data.sequential_tagging.CONLL2000(data_path, sentence_length=None, max_word_length=None, extract_chars=False, lowercase=True)[source]

Bases: object

CONLL 2000 POS/chunking task data set (numpy)

Parameters:
  • data_path (str) – directory containing CONLL2000 files
  • sentence_length (int, optional) – number of time steps to embed the data. None value will not truncate vectors
  • max_word_length (int, optional) – max word length in characters. None value will not truncate vectors
  • extract_chars (boolean, optional) – Yield Char RNN features.
  • lowercase (bool, optional) – lower case sentence words
char_vocab

character Vocabulary

chunk_vocab

chunk label Vocabulary

dataset_files = {'test': 'test.txt', 'train': 'train.txt'}
pos_vocab

pos label Vocabulary

test_set

get the test set

train_set

get the train set

word_vocab

word Vocabulary

class nlp_architect.data.sequential_tagging.SequentialTaggingDataset(train_file, test_file, max_sentence_length=30, max_word_length=20, tag_field_no=2)[source]

Bases: object

Sequential tagging dataset loader. Loads train/test files with tabular separation.

Parameters:
  • train_file (str) – path to train file
  • test_file (str) – path to test file
  • max_sentence_length (int, optional) – max sentence length
  • max_word_length (int, optional) – max word length
  • tag_field_no (int, optional) – index of column to use a y-samples
char_vocab

characters vocabulary

char_vocab_size

character vocabulary size

test_set

Get the test set

train_set

Get the train set

word_vocab

words vocabulary

word_vocab_size

word vocabulary size

y_labels

return y labels

class nlp_architect.data.sequential_tagging.TokenClsInputExample(guid: str, text: str, tokens: List[str], shapes: List[int] = None, label: List[str] = None)[source]

Bases: nlp_architect.data.utils.InputExample

A single training/test example for simple sequence token classification.

class nlp_architect.data.sequential_tagging.TokenClsProcessor(data_dir, tag_col: int = -1, ignore_token=None)[source]

Bases: nlp_architect.data.utils.DataProcessor

Sequence token classification Processor dataset loader. Loads a directory with train.txt/test.txt/dev.txt files in tab separeted format (one token per line - conll style). Label dictionary is given in labels.txt file.

get_dev_examples(filename='dev.txt')[source]

Gets a collection of `InputExample`s for the dev set.

get_labels()[source]

Gets the list of labels for this data set.

static get_labels_filename()[source]
get_test_examples(filename='test.txt')[source]

Gets a collection of `InputExample`s for the test set.

get_train_examples(filename='train.txt')[source]

Gets a collection of `InputExample`s for the train set.

get_vocabulary(examples: nlp_architect.data.sequential_tagging.TokenClsInputExample = None)[source]

nlp_architect.data.utils module

class nlp_architect.data.utils.DataProcessor[source]

Bases: object

Base class for data converters for sequence/token classification data sets.

get_dev_examples()[source]

Gets a collection of `InputExample`s for the dev set.

get_labels()[source]

Gets the list of labels for this data set.

get_test_examples()[source]

Gets a collection of `InputExample`s for the test set.

get_train_examples()[source]

Gets a collection of `InputExample`s for the train set.

class nlp_architect.data.utils.InputExample(guid: str, text, label=None)[source]

Bases: abc.ABC

Base class for a single training/dev/test example

class nlp_architect.data.utils.Task(name: str, processor: nlp_architect.data.utils.DataProcessor, data_dir: str, task_type: str)[source]

Bases: object

A task definition class :param name: the name of the task :type name: str :param processor: a DataProcessor class containing a dataset loader :type processor: DataProcessor :param data_dir: path to the data source :type data_dir: str :param task_type: the task type (classification/regression/tagging) :type task_type: str

get_dev_examples()[source]
get_labels()[source]
get_test_examples()[source]
get_train_examples()[source]
nlp_architect.data.utils.get_cached_filepath(data_dir, model_name, seq_length, task_name, set_type='train')[source]

get cached file name

Parameters:
  • {str} -- data directory string (data_dir) –
  • {str} -- model name (model_name) –
  • {int} -- max sequence length (seq_length) –
  • {str} -- name of task (task_name) –
Keyword Arguments:
 

{str} -- set type (set_type) – {“train”})
Returns:

str – cached filename
nlp_architect.data.utils.read_column_tagged_file(filename: str, tag_col: int = -1, ignore_token: str = None)[source]

Reads column tagged (CONLL) style file (tab separated and token per line) tag_col is the column number to use as tag of the token (defualts to the last in line) :param filename: input file path :type filename: str :param tag_col: the column contains the labels :type tag_col: int :param ignore_token: a str token to exclude :type ignore_token: str

return format : [ [‘token’, ‘TAG’], [‘token’, ‘TAG2’],… ]

nlp_architect.data.utils.read_tsv(input_file, quotechar=None)[source]

Reads a tab separated value file.

nlp_architect.data.utils.sample_label_unlabeled(samples: List[nlp_architect.data.utils.InputExample], no_labeled: int, no_unlabeled: int)[source]

Randomly sample 2 sets of samples from a given collection of InputExamples (used for semi-supervised models)

nlp_architect.data.utils.split_column_dataset(first_count: int, second_count: int, out_folder, dataset, first_filename, second_filename, tag_col=-1)[source]

Splits a single column tagged dataset into two files according to the amount of examples requested to be included in each file. first_count (int) : the amount of examples to include in the first split file second_count (int) : the amount of examples to include in the second split file out_folder (str) : the folder in which the result files will be stored dataset (str) : the path to the original data file first_filename (str) : the name of the first split file second_filename (str) : the name of the second split file tag_col (int) : the index of the tag column

nlp_architect.data.utils.write_column_tagged_file()[source]

Module contents