How to load a custom dataset#

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

loading a ColumnCorpus#

In cases you want to train over a sequence labeling dataset that is not in the above list, you can load them with the ColumnCorpus object. Most sequence labeling datasets in NLP use some sort of column format in which each line is a word and each column is one level of linguistic annotation. See for instance this sentence:

George N B-PER
Washington N I-PER
went V O
to P O
Washington N B-LOC

Sam N B-PER
Houston N I-PER
stayed V O
home N O

The first column is the word itself, the second coarse PoS tags, and the third BIO-annotated NER tags. Empty line separates sentences. To read such a dataset, define the column structure as a dictionary and instantiate a ColumnCorpus.

from flair.data import Corpus
from flair.datasets import ColumnCorpus

# define columns
columns = {0: 'text', 1: 'pos', 2: 'ner'}

# this is the folder in which train, test and dev files reside
data_folder = '/path/to/data/folder'

# init a corpus using column format, data folder and the names of the train, dev and test files
corpus: Corpus = ColumnCorpus(data_folder, columns,
                              train_file='train.txt',
                              test_file='test.txt',
                              dev_file='dev.txt')

This gives you a Corpus object that contains the train, dev and test splits, each has a list of Sentence. So, to check how many sentences there are in the training split, do

len(corpus.train)

You can also access a sentence and check out annotations. Lets assume that the training split is read from the example above, then executing these commands

print(corpus.train[0].to_tagged_string('ner'))
print(corpus.train[1].to_tagged_string('pos'))

will print the sentences with different layers of annotation:

George <B-PER> Washington <I-PER> went to Washington <B-LOC> .

Sam <N> Houston <N> stayed <V> home <N>

Reading a text classification dataset#

If you want to use your own text classification dataset, there are currently two methods to go about this: load specified text and labels from a simple CSV file or format your data to the FastText format.

Load from simple CSV file#

Many text classification datasets are distributed as simple CSV files in which each row corresponds to a data point and columns correspond to text, labels, and other metadata. You can load a CSV format classification dataset using CSVClassificationCorpus by passing in a column format (like in ColumnCorpus above). This column format indicates which column(s) in the CSV holds the text and which field(s) the label(s). By default, Python’s CSV library assumes that your files are in Excel CSV format, but you can specify additional parameters if you use custom delimiters or quote characters.

Note: You will need to save your split CSV data files in the data_folder path with each file titled appropriately i.e. train.csv test.csv dev.csv. This is because the corpus initializers will automatically search for the train, dev, test splits in a folder.

from flair.data import Corpus
from flair.datasets import CSVClassificationCorpus

# this is the folder in which train, test and dev files reside
data_folder = '/path/to/data'

# column format indicating which columns hold the text and label(s)
column_name_map = {4: "text", 1: "label_topic", 2: "label_subtopic"}

# load corpus containing training, test and dev data and if CSV has a header, you can skip it
corpus: Corpus = CSVClassificationCorpus(data_folder,
                                         column_name_map,
                                         skip_header=True,
                                         delimiter='\t',    # tab-separated files
)

FastText format#

If using CSVClassificationCorpus is not practical, you may format your data to the FastText format, in which each line in the file represents a text document. A document can have one or multiple labels that are defined at the beginning of the line starting with the prefix __label__. This looks like this:

__label__<label_1> <text>
__label__<label_1> __label__<label_2> <text>

As previously mentioned, to create a Corpus for a text classification task, you need to have three files (train, dev, and test) in the above format located in one folder. This data folder structure could, for example, look like this for the IMDB task:

/resources/tasks/imdb/train.txt
/resources/tasks/imdb/dev.txt
/resources/tasks/imdb/test.txt

Now create a CSVClassificationCorpus by pointing to this folder (/resources/tasks/imdb). Thereby, each line in a file is converted to a Sentence object annotated with the labels.

Important

A text in a line can have multiple sentences. Thus, a Sentence object can actually consist of multiple sentences.

from flair.data import Corpus
from flair.datasets import ClassificationCorpus

# this is the folder in which train, test and dev files reside
data_folder = '/path/to/data/folder'

# load corpus containing training, test and dev data
corpus: Corpus = ClassificationCorpus(data_folder,
                                      test_file='test.txt',
                                      dev_file='dev.txt',
                                      train_file='train.txt',
                                      label_type='topic',
                                      )

Note again that our corpus initializers have methods to automatically look for train, dev and test splits in a folder. So in most cases you don’t need to specify the file names yourself. Often, this is enough:

# this is the folder in which train, test and dev files reside
data_folder = '/path/to/data/folder'

# load corpus by pointing to folder. Train, dev and test gets identified automatically.
corpus: Corpus = ClassificationCorpus(data_folder,
                                      label_type='topic',
                                      )

Since the FastText format does not have columns, you must manually define a name for the annotations. In this example we chose label_type='topic' to denote that we are loading a corpus with topic labels.

Next#

Next, learn how to train a sequence tagger.

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