a high-level library for named entity recognition in python
Project description
A High-level Library for Named Entity Recognition in Python.
Resources
Source Code: https://github.com/flxst/nerblackbox
Documentation: https://flxst.github.io/nerblackbox
Installation
pip install nerblackbox
About
Take a dataset from one of many available sources. Then train, evaluate and apply a language model in a few simple steps.
1. Data
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()
2. Training
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
3. Evaluation
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
4. Inference
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.
Features
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.
Citation
@misc{nerblackbox, author = {Stollenwerk, Felix}, title = {nerblackbox: a high-level library for named entity recognition in python}, year = {2021}, url = {https://github.com/flxst/nerblackbox}, }
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