pytorch-partial-tagger 0.1.18

Sequence Tagger for Partially Annotated Dataset in PyTorch

pytorch-partial-tagger

pytorch-partial-tagger is a Python library for building a sequence tagger, specifically for the common NLP task Named Entity Recognition, with a partially annotated dataset in PyTorch. You can build your own tagger using a distantly-supervised dataset obtained from unlabled text data and a dictionary that maps surface names to their entity type. The algorithm of this library is based on Effland and Collins. (2021).

Usage

Import all dependencies first:

import torch
from sequence_label import SequenceLabel

from partial_tagger.metric import Metric
from partial_tagger.utils import create_trainer

Prepare your own datasets. Each item of dataset must have a pair of a string and a sequence label. A string represents text that you want to assign a label, which is defined as text below. A sequence label represent a set of a character-based tag, which has a start, a length, and a label, which are defined as label below. A start represents a position in the text where a tag starts. A length represents a distance in the text between the beginning of a tag and the end of a tag. A label represents what you want to assign to a span of the text defined by a start and a length.

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

train_dataset = [(text, label), ...]
validation_dataset = [...]
test_dataset = [...]

Here, you will train your tagger and evaluate its performance. You will train it through an instance of Trainer, which you get by calling create_trainer. After a training, you will get an instance of Recognizer which predicts character-based tags from given texts. You will evaluate the performance of your tagger using an instance of Metric as follows.

device = torch.device("cuda")

trainer = create_trainer()
recognizer = trainer(train_dataset, validation_dataset, device)

texts, ground_truths = zip(*test_dataset)

batch_size = 15
predictions = recognizer(texts, batch_size, device)

metric = Metric()
metric(predictions, ground_truths)

print(metric.get_scores())  # Display F1-score, Precision, Recall

Installation

pip install pytorch-partial-tagger

Documentation

For details about the pytorch-partial-tagger API, see the documentation.

References

• Thomas Effland and Michael Collins. 2021. Partially Supervised Named Entity Recognition via the Expected Entity Ratio Loss. Transactions of the Association for Computational Linguistics, 9:1320–1335.