---
language:
- 'no'
- nb
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikiann
model-index:
- name: bert-base-multilingual-cased-finetuned-norsk-ner
  results: []
pipeline_tag: token-classification
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Model Card: bert-base-multilingual-cased-finetuned-norsk-ner (Fine-Tuned with WikiANN)

## Overview

- **Model Name:** bert-base-multilingual-cased-finetuned-norsk-ner
- **Model Type:** Named Entity Recognition (NER)
- **Language:** Multilingual with focus on Norwegian (Norsk)
- **Fine-Tuned with:** [WikiANN](https://huggingface.co/datasets/wikiann) dataset

## Description

The `bert-base-multilingual-cased-finetuned-norsk-ner` is a pre-trained BERT (Bidirectional Encoder Representations from Transformers) model that has been fine-tuned for Named Entity Recognition (NER) in the Norwegian language (Norsk). This model has been fine-tuned using the WikiANN dataset, which includes annotated named entities from various languages, including Norwegian.

Named Entity Recognition is the task of identifying and classifying named entities in text, such as persons, organizations, locations, dates, and more. This model can be used to extract valuable information from Norwegian text with a focus on NER.

## Intended Use

The `bert-base-multilingual-cased-finetuned-norsk-ner` model, fine-tuned with the WikiANN dataset, is designed for Named Entity Recognition (NER) applications in Norwegian text. It is particularly well-suited for identifying and classifying various types of named entities within Norwegian language content, including the following categories:

- **Persons (PER):** Recognizing individuals' names, both at the beginning and within their names.
- **Organizations (ORG):** Identifying organization names, distinguishing between the beginning and inside of these names.
- **Locations (LOC):** Recognizing location names, including both the beginning and interior of these names.
- **Miscellaneous (MISC):** Handling miscellaneous entities or categories within text.

## Labels

|   Label   |   Description                           |
|-----------|-----------------------------------------|
|   MISC    |   Miscellaneous entities or categories. |
|   B-PER   |   Beginning of a person's name.         |
|   I-PER   |   Inside of a person's name.            |
|   B-ORG   |   Beginning of an organization name.    |
|   I-ORG   |   Inside of an organization name.       |
|   B-LOC   |   Beginning of a location name.         |
|   I-LOC   |   Inside of a location name.            |


## Usage
```python
from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
import pandas as pd

tokenizer = AutoTokenizer.from_pretrained("Kushtrim/bert-base-multilingual-cased-finetuned-norsk-ner")

model = AutoModelForTokenClassification.from_pretrained("Kushtrim/bert-base-multilingual-cased-finetuned-norsk-ner")
ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy='first')

text = "Sett inn tekst her"

results = ner(text)

pd.DataFrame.from_records(results)
```