A High-level Library for Named Entity Recognition in Python
- Source Code: https://github.com/flxst/nerblackbox
- Documentation: https://flxst.github.io/nerblackbox
- Paper: https://aclanthology.org/2023.nlposs-1.20
- PyPI: https://pypi.org/project/nerblackbox
pip install nerblackbox
Take a dataset from one of many available sources. Then train, evaluate and apply a language model in a few simple steps.
- Choose a dataset from HuggingFace (HF), the Local Filesystem (LF), an Annotation Tool (AT) server, or a Built-in (BI) dataset
dataset = Dataset("conll2003", source="HF") # HuggingFace
dataset = Dataset("my_dataset", source="LF") # Local Filesystem
dataset = Dataset("swe_nerc", source="BI") # Built-in- Set up the dataset
dataset.set_up()- Define the training by choosing a pretrained model and a dataset
training = Training("my_training", model="bert-base-cased", dataset="conll2003")- Run the training and get the performance of the fine-tuned model
training.run()
training.get_result(metric="f1", level="entity", phase="test")
# 0.9045- Load the model
model = Model.from_training("my_training")- Evaluate the model
results = model.evaluate_on_dataset("ehealth_kd", phase="test")
results["micro"]["entity"]["f1"]
# 0.9045- Load the model
model = Model.from_training("my_training")- Let the model predict
model.predict("The United Nations has never recognised Jakarta's move.")
# [[
# {'char_start': '4', 'char_end': '18', 'token': 'United Nations', 'tag': 'ORG'},
# {'char_start': '40', 'char_end': '47', 'token': 'Jakarta', 'tag': 'LOC'}
# ]]There is much more to it than that! See the documentation to get started.
Data
- Integration of Datasets from Multiple Sources (HuggingFace, Annotation Tools, ..)
- Support for Multiple Dataset Types (Standard, Pretokenized)
- Support for Multiple Annotation Schemes (IO, BIO, BILOU)
- Text Encoding
Training
- Adaptive Fine-tuning
- Hyperparameter Search
- Multiple Runs with Different Random Seeds
- Detailed Analysis of Training Results
Evaluation
- Evaluation of Any Model on Any Dataset
Inference
- Versatile Model Inference (Entity/Word Level, Probabilities, ..)
Other
- Full Compatibility with HuggingFace
- GPU Support
- Language Agnosticism
See the documentation for details.
@inproceedings{stollenwerk-2023-nerblackbox,
title = "nerblackbox: A High-level Library for Named Entity Recognition in Python",
author = "Stollenwerk, Felix",
editor = "Tan, Liling and
Milajevs, Dmitrijs and
Chauhan, Geeticka and
Gwinnup, Jeremy and
Rippeth, Elijah",
booktitle = "Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)",
month = dec,
year = "2023",
address = "Singapore, Singapore",
publisher = "Empirical Methods in Natural Language Processing",
url = "https://aclanthology.org/2023.nlposs-1.20",
pages = "174--178",
abstract = "We present **nerblackbox**, a python library to facilitate the use of state-of-the-art transformer-based models for named entity recognition. It provides simple-to-use yet powerful methods to access data and models from a wide range of sources, for fully automated model training and evaluation as well as versatile model inference. While many technical challenges are solved and hidden from the user by default, **nerblackbox** also offers fine-grained control and a rich set of customizable features. It is thus targeted both at application-oriented developers as well as machine learning experts and researchers.",
}