sequence-label

sequence-label is a Python library that streamlines the process of creating tensors for sequence labels and reconstructing sequence labels data from tensors. Whether you're working on named entity recognition, part-of-speech tagging, or any other sequence labeling task, this library offers a convenient utility to simplify your workflow.

Basic Usage

Import the necessary dependencies:

from transformers import AutoTokenizer

from sequence_label import LabelSet, SequenceLabel
from sequence_label.transformers import create_alignments

Start by creating sequence labels using the SequenceLabel.from_dict method. Define your text and associated labels:

text1 = "Tokyo is the capital of Japan."
label1 = SequenceLabel.from_dict(
    tags=[
        {"start": 0, "end": 5, "label": "LOC"},
        {"start": 24, "end": 29, "label": "LOC"},
    ],
    size=len(text1),
)

text2 = "The Monster Naoya Inoue is the who's who of boxing."
label2 = SequenceLabel.from_dict(
    tags=[{"start": 12, "end": 23, "label": "PER"}],
    size=len(text2),
)

texts = [text1, text2]
labels = [label1, label2]

Next, tokenize your texts and create the alignments using the create_alignments method. alignments is a tuple of instances of LabelAlignment that aligns sequence labels with the tokenized result:

tokenizer = AutoTokenizer.from_pretrained("roberta-base")
batch_encoding = tokenizer(texts)

alignments = create_alignments(
    batch_encoding=batch_encoding,
    lengths=list(map(len, texts)),
    padding_token=tokenizer.pad_token
)

Now, create a label_set that will allow you to create tensors from sequence labels and reconstruct sequence labels from tensors. Use the label_set.encode_to_tag_indices method to create tag_indices:

label_set = LabelSet(
    labels={"ORG", "LOC", "PER", "MISC"},
    padding_index=-1,
)

tag_indices = label_set.encode_to_tag_indices(
    labels=labels,
    alignments=alignments,
)

Finally, use the label_set.decode method to reconstruct the sequence labels from tag_indices and alignments:

labels2 = label_set.decode(
    tag_indices=tag_indices, alignments=alignments,
)

assert labels == labels2

Installation

pip install sequence-label