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https://github.com/yasufumy/pytorch-partial-tagger
Sequence Tagger for Partially Annotated Dataset in PyTorch
https://github.com/yasufumy/pytorch-partial-tagger
machine-learning named-entity-recognition natural-language-processing nlp pytorch semi-supervised-learning torch
Last synced: 11 days ago
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Sequence Tagger for Partially Annotated Dataset in PyTorch
- Host: GitHub
- URL: https://github.com/yasufumy/pytorch-partial-tagger
- Owner: yasufumy
- License: mit
- Created: 2023-05-30T08:11:31.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-11-23T15:13:23.000Z (about 1 year ago)
- Last Synced: 2024-12-20T06:18:20.679Z (12 days ago)
- Topics: machine-learning, named-entity-recognition, natural-language-processing, nlp, pytorch, semi-supervised-learning, torch
- Language: Python
- Homepage: https://pytorch-partial-tagger.readthedocs.io/en/latest/
- Size: 127 KB
- Stars: 1
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# pytorch-partial-tagger
[](https://colab.research.google.com/drive/17GcpKmYn49bMM-mCZhiuRu8dkPZ0WXuD?usp=sharing)
`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:
```py
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.
```py
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.
```py
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
```bash
pip install pytorch-partial-tagger
```
## Documentation
For details about the `pytorch-partial-tagger` API, see the [documentation](https://pytorch-partial-tagger.readthedocs.io/en/latest/).
## References
- Thomas Effland and Michael Collins. 2021. [Partially Supervised Named Entity Recognition via the Expected Entity Ratio Loss](https://aclanthology.org/2021.tacl-1.78/). _Transactions of the Association for Computational Linguistics_, 9:1320–1335.