How to load a prepared dataset#

This part of the tutorial shows how you can load a corpus for training a model.

The Corpus Object#

The Corpus represents a dataset that you use to train a model. It consists of a list of train sentences, a list of dev sentences, and a list of test sentences, which correspond to the training, validation and testing split during model training.

The following example snippet instantiates the Universal Dependency Treebank for English as a corpus object:

from flair.datasets import UD_ENGLISH
corpus = UD_ENGLISH()

The first time you call this snippet, it triggers a download of the Universal Dependency Treebank for English onto your hard drive. It then reads the train, test and dev splits into the Corpus which it returns. Check the length of the three splits to see how many Sentences are there:

# print the number of Sentences in the train split
print(len(corpus.train))

# print the number of Sentences in the test split
print(len(corpus.test))

# print the number of Sentences in the dev split
print(len(corpus.dev))

You can also access the Sentence objects in each split directly. For instance, let us look at the first Sentence in the training split of the English UD:

# get the first Sentence in the training split
sentence = corpus.test[0]

# print with all annotations
print(sentence)

# print only with POS annotations (better readability)
print(sentence.to_tagged_string('pos'))

The sentence is fully tagged with syntactic and morphological information. With the latter line, you print out only the POS tags:

Sentence: "What if Google Morphed Into GoogleOS ?" → ["What"/WP, "if"/IN, "Google"/NNP, "Morphed"/VBD, "Into"/IN, "GoogleOS"/NNP, "?"/.]

So the corpus is tagged and ready for training.

Helper functions#

A Corpus contains a bunch of useful helper functions. For instance, you can downsample the data by calling Corpus.downsample() and passing a ratio. So, if you normally get a corpus like this:

from flair.datasets import UD_ENGLISH
corpus = UD_ENGLISH()

then you can downsample the corpus, simply like this:

from flair.datasets import UD_ENGLISH
downsampled_corpus = UD_ENGLISH().downsample(0.1)

If you print both corpora, you see that the second one has been downsampled to 10% of the data.

print("--- 1 Original ---")
print(corpus)

print("--- 2 Downsampled ---")
print(downsampled_corpus)

This should print:

--- 1 Original ---
Corpus: 12543 train + 2002 dev + 2077 test sentences

--- 2 Downsampled ---
Corpus: 1255 train + 201 dev + 208 test sentences

Creating label dictionaries#

For many learning tasks you need to create a “dictionary” that contains all the labels you want to predict. You can generate this dictionary directly out of the Corpus by calling the method Corpus.make_label_dictionary and passing the desired label_type.

For instance, the UD_ENGLISH corpus instantiated above has multiple layers of annotation like regular POS tags (‘pos’), universal POS tags (‘upos’), morphological tags (‘tense’, ‘number’..) and so on. Create label dictionaries for universal POS tags by passing label_type='upos' like this:

# create label dictionary for a Universal Part-of-Speech tagging task
upos_dictionary = corpus.make_label_dictionary(label_type='upos')

# print dictionary
print(upos_dictionary)

This will print out the created dictionary:

Dictionary with 17 tags: PROPN, PUNCT, ADJ, NOUN, VERB, DET, ADP, AUX, PRON, PART, SCONJ, NUM, ADV, CCONJ, X, INTJ, SYM

Dictionaries for other label types#

If you don’t know the label types in a corpus, just call Corpus.make_label_dictionary with any random label name (e.g. corpus.make_label_dictionary(label_type='abcd')). This will print out statistics on all label types in the corpus:

The corpus contains the following label types: 'lemma' (in 12543 sentences), 'upos' (in 12543 sentences), 'pos' (in 12543 sentences), 'dependency' (in 12543 sentences), 'number' (in 12036 sentences), 'verbform' (in 10122 sentences), 'prontype' (in 9744 sentences), 'person' (in 9381 sentences), 'mood' (in 8911 sentences), 'tense' (in 8747 sentences), 'degree' (in 7148 sentences), 'definite' (in 6851 sentences), 'case' (in 6486 sentences), 'gender' (in 2824 sentences), 'numtype' (in 2771 sentences), 'poss' (in 2516 sentences), 'voice' (in 1085 sentences), 'typo' (in 399 sentences), 'extpos' (in 185 sentences), 'abbr' (in 168 sentences), 'reflex' (in 98 sentences), 'style' (in 31 sentences), 'foreign' (in 5 sentences)

This means that you can create dictionaries for any of these label types for the UD_ENGLISH corpus. Let’s create dictionaries for regular part of speech tags and a morphological number tagging task:

# create label dictionary for a regular POS tagging task
pos_dictionary = corpus.make_label_dictionary(label_type='pos')

# create label dictionary for a morphological number tagging task
tense_dictionary = corpus.make_label_dictionary(label_type='number')

If you print these dictionaries, you will find that the POS dictionary contains 50 tags and the number dictionary only 2 for this corpus (singular and plural).

Dictionaries for other corpora types#

The method Corpus.make_label_dictionary can be used for any corpus, including text classification corpora:

# create label dictionary for a text classification task
from flair.datasets import TREC_6
corpus = TREC_6()
corpus.make_label_dictionary('question_class')

The MultiCorpus Object#

If you want to train multiple tasks at once, you can use the MultiCorpus object. To initiate the MultiCorpus you first need to create any number of Corpus objects. Afterwards, you can pass a list of Corpus to the MultiCorpus object. For instance, the following snippet loads a combination corpus consisting of the English, German and Dutch Universal Dependency Treebanks.

from flair.datasets import UD_ENGLISH, UD_GERMAN, UD_DUTCH
english_corpus = UD_ENGLISH()
german_corpus = UD_GERMAN()
dutch_corpus = UD_DUTCH()

# make a multi corpus consisting of three UDs
from flair.data import MultiCorpus
multi_corpus = MultiCorpus([english_corpus, german_corpus, dutch_corpus])

The MultiCorpus inherits from [Corpus`](#flair.data.Corpus), so you can use it like any other corpus to train your models.

Datasets included in Flair#

Flair supports many datasets out of the box. It usually automatically downloads and sets up the data the first time you call the corresponding constructor ID. The datasets are split into multiple modules, however they all can be imported from flair.datasets too. You can look up the respective modules to find the possible datasets.

The following datasets are supported:

Task	Module
Named Entity Recognition	flair.datasets.sequence_labeling
Text Classification	flair.datasets.document_classification
Text Regression	flair.datasets.document_classification
Biomedical Named Entity Recognition	flair.datasets.biomedical
Entity Linking	flair.datasets.entity_linking
Relation Extraction	flair.datasets.relation_extraction
Sequence Labeling	flair.datasets.sequence_labeling
Glue Benchmark	flair.datasets.text_text and flair.datasets.document_classification
Universal Proposition Banks	flair.datasets.treebanks
Universal Dependency Treebanks	flair.datasets.treebanks
OCR-Layout-NER	flair.datasets.ocr

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