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finbert-ner

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bert
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metadata
license: mit
language:
  - fi
metrics:
  - f1
  - accuracy
library_name: transformers
pipeline_tag: token-classification

Finnish named entity recognition ** WORK IN PROGRESS **

The model performs named entity recognition from text input in Finnish. It was trained by fine-tuning bert-base-finnish-cased-v1, using 10 named entity categories. Training data contains the Turku OntoNotes Entities Corpus as well as an annotated dataset consisting of Finnish document daa from the 1970s onwards, digitized by the National Archives of Finland. Since the latter dataset contains also sensitive data, it has not been made publicly available.

Intended uses & limitations

The model has been trained to recognize the following named entities from a text in Finnish:

  • PERSON (person names)
  • ORG (organizations)
  • LOC (locations)
  • GPE (geopolitical locations)
  • PRODUCT (products)
  • EVENT (events)
  • DATE (dates)
  • JON (Finnish journal numbers (diaarinumero))
  • FIBC (Finnish business identity codes (y-tunnus))
  • NORP (nationality, religious and political groups)

Some entities, like EVENT, LOC and JON, are less common in the training data than the others, which means that recognition accuracy for these entities also tends to be lower.

The training data is relatively recent, so that the model might face difficulties when the input contains for example old names or writing styles.

How to use

The easiest way to use the model is by utilizing the Transformers pipeline for token classification:

from transformers import pipeline

model_checkpoint = "Kansallisarkisto/finbert-ner"
token_classifier = pipeline(
    "token-classification", model=model_checkpoint, aggregation_strategy="simple"
)
token_classifier("'Helsingistä tuli Suomen suuriruhtinaskunnan pääkaupunki vuonna 1812.")

Training data

Some of the entities (for instance WORK_OF_ART, LAW, MONEY) that have been annotated in the Turku OntoNotes Entities Corpus dataset were filtered out from the dataset used for training the model. In addition to this dataset, OCR'd and annotated content of digitized documents from Finnish public administration was also used for model training. The number of entities belonging to the different entity classes contained in training, validation and test datasets are listed below:

Number of entity types in the data

Dataset O PERSON ORG LOC GPE PRODUCT EVENT DATE JON FIBC NORP
Train 0 0 0 0 0 0 0 0 0 0 0
Val 0 0 0 0 0 0 0 0 0 0 0
Test 0 0 0 0 0 0 0 0 0 0 0

Training procedure

This model was trained using a NVIDIA RTX A6000 GPU with the following hyperparameters:

The training code with instructions is available here.