NERDA 0.0.40

A Framework for Finetuning Transformers for Named Entity Recognition

NERDA [WIP]

Not only is NERDA a mesmerizing muppet-like character. NERDA is also a python package, that offers a slick easy-to-use interface for fine-tuning pretrained transformers for Named Entity Recognition (=NER) tasks.

NERDAis built on huggingface transformers and the popular pytorch framework.

Installation guide

NERDA can be installed from PyPI with

pip install NERDA

If you want the development version then install directly from GitHub.

Named-Entity Recogntion tasks

Named-entity recognition (NER) (also known as (named) entity identification, entity chunking, and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.¹

Example Task:

Task

Identify person names and organizations in text:

Jim bought 300 shares of Acme Corp.

Solution

Named Entity	Type
'Jim'	Person
'Acme Corp.'	Organization

Read more about NER on Wikipedia.

Train Your Own NERDA Model

GOAL: We want to fine-tune a multilingual BERT model for NER in Danish.

Load package.

from NERDA.models import NERDA

Instantiate a NERDA model (with default settings) for the DaNE Danish NER data set.

model = NERDA(dataset_training = get_dane_data('train'),
              dataset_validation = get_dane_data('dev'),
              transformer = 'bert-base-multilingual-uncased')

The model can then be trained/fine-tuned by invoking the train method, e.g.

model.train()

Note: this will take some time depending on the dimensions of your machine. With a decent AWS EC2 instance it will take below 15 minutes.

After the model has been trained, the model can be used for predicting named entities in new texts.

# (Danish) text to identify named entities in.
# = 'Old MacDonald had a farm'
text = 'Jens Hansen har en bondegård'
model.predict_text(text)

.. It is as simple as that!

Please note, that the NERDA model configuration above was instantiated with all default settings. You can however customize your NERDA model in a lot of ways:

• Use your own data set (in whatever language you desire)
• Choose whatever transformer you like
• Set all of the hyperparameters for the model
• You can even apply your own Network Architecture

Read more about advanced usage of NERDA in the detailed documentation.

Use a Precooked NERDA model

We have precooked a number of NERDA models, that you can download and use right off the shelf.

Here is an example.

Instantiate multingual BERT model, that has been finetuned for NER in Danish, BERT_ML_DaNE.

from NERDA.precooked import BERT_ML_DaNE()
model = BERT_ML_DaNE()

Down(load) network from web:

model.download_network()
model.load_network()

You can now predict named entities in new texts

# (Danish) text to identify named entities in.
# = 'Old MacDonald had a farm'
text = 'Jens Hansen har en bondegård'
model.predict_text(text)

List of Precooked Models

The table below shows the precooked NERDA models publicly available for download.

Model	Language	Transformer	F1-score
DA_BERT_ML	Danish	Multilingual BERT	xx.x
DA_ELECTRA_DA	Danish	Danish ELECTRA	yy.y
EN_BERT_ML	English	Multilingual BERT	zz.z

Note, that we have not spent a lot of time on actually fine-tuning the models, so there could be room for improvement. If you are able to improve the models, we will be happy to hear from you and include your NERDA model.

Performance

The table below summarizes the performance as measured by F1-scores of the model configurations, that NERDA ships with.

Level	MBERT	DABERT	ELECTRA	XLMROBERTA	DISTILMBERT
B-PER	0.92	0.93	0.92	0.94	0.89
I-PER	0.97	0.99	0.97	0.99	0.96
B-ORG	0.68	0.79	0.65	0.78	0.66
I-ORG	0.67	0.79	0.72	0.77	0.61
B-LOC	0.86	0.85	0.79	0.87	0.80
I-LOC	0.33	0.32	0.44	0.24	0.29
B-MISC	0.73	0.74	0.61	0.77	0.70
I-MISC	0.70	0.86	0.65	0.91	0.61
AVG_MICRO	0.81	0.85	0.79	0.86	0.78
AVG_MACRO	0.73	0.78	0.72	0.78	0.69

'NERDA'?

'NERDA' originally stands for 'Named Entity Recognition for DAnish'. However, this is somewhat misleading, since the functionality is no longer limited to Danish. On the contrary it generalizes to all other languages, i.e. NERDA supports fine-tuning of transformer-based models for NER tasks for any arbitrary language.

Read more

The detailed documentation for NERDA including code references and examples can be accessed here.

Contact

We hope, that you will find NERDA useful.

Please direct any questions and feedbacks to us!

If you want to contribute (which we encourage you to), open a PR.

If you encounter a bug or want to suggest an enhancement, please open an issue.