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fine-tune transformer-based models for named entity recognition

Project description

A python package to fine-tune transformer-based models for Named Entity Recognition (NER).

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Transformer-based models like BERT have had a game-changing impact on Natural Language Processing.

In order to utilize the publicly accessible pretrained models for Named Entity Recognition, one needs to retrain (or “fine-tune”) them using labeled text.

nerblackbox makes this easy.

You give it

  • a Dataset (labeled text)
  • a Pretrained Model (transformers)

and you get

  • the best Fine-tuned Model
  • its Performance on the dataset


pip install nerblackbox


Fine-tuning can be done in a few simple steps using an “experiment configuration file”

# cat <experiment_name>.ini
dataset_name = swedish_ner_corpus
pretrained_model_name = af-ai-center/bert-base-swedish-uncased

and either the Command Line Interface (CLI) or the Python API:

nerbb run_experiment <experiment_name>          # fine-tune
nerbb get_experiment_results <experiment_name>  # get results/performance
nerbb predict <experiment_name> <text_input>    # apply best model

# Python API
nerbb = NerBlackBox()
nerbb.run_experiment(<experiment_name>)         # fine-tune
nerbb.get_experiment_results(<experiment_name>) # get results/performance
nerbb.predict(<experiment_name>, <text_input>)  # apply best model

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