collatable 0.3.2

Constructing batched tensors for any machine learning tasks

Collatable

Constructing batched tensors for any machine learning tasks

Installation

pip install collatable

Examples

The following scripts show how to tokenize/index/collate your dataset with collatable:

Text Classification

import collatable
from collatable import Instance, LabelField, MetadataField, TextField
from collatable.extras.indexer import LabelIndexer, TokenIndexer

dataset = [
    ("this is awesome", "positive"),
    ("this is a bad movie", "negative"),
    ("this movie is an awesome movie", "positive"),
    ("this movie is too bad to watch", "negative"),
]

# Set up indexers for tokens and labels
PAD_TOKEN = "<PAD>"
UNK_TOKEN = "<UNK>"
token_indexer = TokenIndexer[str](specials=[PAD_TOKEN, UNK_TOKEN], default=UNK_TOKEN)
label_indexer = LabelIndexer[str]()

# Load training dataset
instances = []
with token_indexer.context(train=True), label_indexer.context(train=True):
    for id_, (text, label) in enumerate(dataset):
        # Prepare each field with the corresponding field class
        text_field = TextField(
            text.split(),
            indexer=token_indexer,
            padding_value=token_indexer[PAD_TOKEN],
        )
        label_field = LabelField(
            label,
            indexer=label_indexer,
        )
        metadata_field = MetadataField({"id": id_})
        # Combine these fields into instance
        instance = Instance(
            text=text_field,
            label=label_field,
            metadata=metadata_field,
        )
        instances.append(instance)

# Collate instances and build batch
output = collatable.collate(instances)
print(output)

Execution result:

{'metadata': [{'id': 0}, {'id': 1}, {'id': 2}, {'id': 3}],
 'text': {
    'token_ids': array([[ 2,  3,  4,  0,  0,  0,  0],
                        [ 2,  3,  5,  6,  7,  0,  0],
                        [ 2,  7,  3,  8,  4,  7,  0],
                        [ 2,  7,  3,  9,  6, 10, 11]]),
    'mask': array([[ True,  True,  True, False, False, False, False],
                   [ True,  True,  True,  True,  True, False, False],
                   [ True,  True,  True,  True,  True,  True, False],
                   [ True,  True,  True,  True,  True,  True,  True]])},
 'label': array([0, 1, 0, 1], dtype=int32)}

Sequence Labeling

import collatable
from collatable import Instance, SequenceLabelField, TextField
from collatable.extras.indexer import LabelIndexer, TokenIndexer

dataset = [
    (["my", "name", "is", "john", "smith"], ["O", "O", "O", "B", "I"]),
    (["i", "lived", "in", "japan", "three", "years", "ago"], ["O", "O", "O", "U", "O", "O", "O"]),
]

# Set up indexers for tokens and labels
PAD_TOKEN = "<PAD>"
token_indexer = TokenIndexer[str](specials=(PAD_TOKEN,))
label_indexer = LabelIndexer[str]()

# Load training dataset
instances = []
with token_indexer.context(train=True), label_indexer.context(train=True):
    for tokens, labels in dataset:
        text_field = TextField(tokens, indexer=token_indexer, padding_value=token_indexer[PAD_TOKEN])
        label_field = SequenceLabelField(labels, text_field, indexer=label_indexer)
        instance = Instance(text=text_field, label=label_field)
        instances.append(instance)

output = collatable.collate(instances)
print(output)

Execution result:

{'label': array([[0, 0, 0, 1, 2, 0, 0],
                 [0, 0, 0, 3, 0, 0, 0]]),
 'text': {
    'token_ids': array([[ 1,  2,  3,  4,  5,  0,  0],
                        [ 6,  7,  8,  9, 10, 11, 12]]),
    'mask': array([[ True,  True,  True,  True,  True, False, False],
                   [ True,  True,  True,  True,  True,  True,  True]])}}

Relation Extraction

import collatable
from collatable.extras.indexer import LabelIndexer, TokenIndexer
from collatable import AdjacencyField, Instance, ListField, SpanField, TextField

PAD_TOKEN = "<PAD>"
token_indexer = TokenIndexer[str](specials=(PAD_TOKEN,))
label_indexer = LabelIndexer[str]()

instances = []
with token_indexer.context(train=True), label_indexer.context(train=True):
    text = TextField(
        ["john", "smith", "was", "born", "in", "new", "york", "and", "now", "lives", "in", "tokyo"],
        indexer=token_indexer,
        padding_value=token_indexer[PAD_TOKEN],
    )
    spans = ListField([SpanField(0, 2, text), SpanField(5, 7, text), SpanField(11, 12, text)])
    relations = AdjacencyField([(0, 1), (0, 2)], spans, labels=["born-in", "lives-in"], indexer=label_indexer)
    instance = Instance(text=text, spans=spans, relations=relations)
    instances.append(instance)

    text = TextField(
        ["tokyo", "is", "the", "capital", "of", "japan"],
        indexer=token_indexer,
        padding_value=token_indexer[PAD_TOKEN],
    )
    spans = ListField([SpanField(0, 1, text), SpanField(5, 6, text)])
    relations = AdjacencyField([(0, 1)], spans, labels=["capital-of"], indexer=label_indexer)
    instance = Instance(text=text, spans=spans, relations=relations)
    instances.append(instance)

output = collatable.collate(instances)
print(output)

Execution result:

{'text': {
    'token_ids': array([[ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10,  5, 11],
                        [11, 12, 13, 14, 15, 16,  0,  0,  0,  0,  0,  0]]),
    'mask': array([[ True,  True,  True,  True,  True,  True,  True,  True,  True, True,  True,  True],
                   [ True,  True,  True,  True,  True,  True, False, False, False, False, False, False]])},
 'spans': array([[[ 0,  2],
                  [ 5,  7],
                  [11, 12]],
                 [[ 0,  1],
                  [ 5,  6],
                  [-1, -1]]]),
 'relations': array([[[-1,  0,  1],
                      [-1, -1, -1],
                      [-1, -1, -1]],
                     [[-1,  2, -1],
                      [-1, -1, -1],
                      [-1, -1, -1]]], dtype=int32)}