path: root/python-nerda.spec

%global _empty_manifest_terminate_build 0
Name:		python-NERDA
Version:	1.0.0
Release:	1
Summary:	A Framework for Finetuning Transformers for Named-Entity Recognition
License:	MIT License
URL:		https://github.com/ebanalyse/NERDA
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/4e/80/3f7ae5e94a16f0dace64996b7eab7ee437b303872654d6705a13654bd132/NERDA-1.0.0.tar.gz
BuildArch:	noarch

Requires:	python3-torch
Requires:	python3-transformers
Requires:	python3-sklearn
Requires:	python3-nltk
Requires:	python3-pandas
Requires:	python3-progressbar
Requires:	python3-pyconll

%description
# NERDA <img src="https://raw.githubusercontent.com/ebanalyse/NERDA/main/logo.png" align="right" height=250/>

![Build status](https://github.com/ebanalyse/NERDA/workflows/build/badge.svg)
[![codecov](https://codecov.io/gh/ebanalyse/NERDA/branch/main/graph/badge.svg?token=OB6LGFQZYX)](https://codecov.io/gh/ebanalyse/NERDA)
![PyPI](https://img.shields.io/pypi/v/NERDA.svg)
![PyPI - Downloads](https://img.shields.io/pypi/dm/NERDA?color=green)
![License](https://img.shields.io/badge/license-MIT-blue.svg)

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. 

You can also utilize `NERDA` to access a selection of *precooked* `NERDA` models, 
 that you can use right off the shelf for NER tasks.

`NERDA` is built on `huggingface` `transformers` and the popular `pytorch`
 framework.

## Installation guide
`NERDA` can be installed from [PyPI](https://pypi.org/project/NERDA/) with 

```
pip install NERDA
```

If you want the development version then install directly from [GitHub](https://github.com/ebanalyse/NERDA).

## 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.<sup>[1]</sup>

[1]: https://en.wikipedia.org/wiki/Named-entity_recognition

### 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](https://en.wikipedia.org/wiki/Named-entity_recognition).

## Train Your Own `NERDA` Model

Say, we want to fine-tune a pretrained [Multilingual BERT](https://huggingface.co/bert-base-multilingual-uncased) transformer for NER in English.

Load package.

```python
from NERDA.models import NERDA
```

Instantiate a `NERDA` model (*with default settings*) for the 
[`CoNLL-2003`](https://www.clips.uantwerpen.be/conll2003/ner/) 
English NER data set. 

```python
from NERDA.datasets import get_conll_data
model = NERDA(dataset_training = get_conll_data('train'),
              dataset_validation = get_conll_data('valid'),
              transformer = 'bert-base-multilingual-uncased')
```

By default the network architecture is analogous to that of the models in [Hvingelby et al. 2020](http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.565.pdf). 

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

```python
model.train()
```

**Note**: this will take some time depending on the dimensions of your machine 
(if you want to skip training, you can go ahead and use one of the models, 
that we have already precooked for you in stead).

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

```python
# text to identify named entities in.
text = 'Old MacDonald had a farm'
model.predict_text(text)
([['Old', 'MacDonald', 'had', 'a', 'farm']], [['B-PER', 'I-PER', 'O', 'O', 'O']])
```
This means, that the model identified 'Old MacDonald' as a *PER*son.

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 (finetune a transformer for any given language)
- 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](https://ebanalyse.github.io/NERDA/workflow).

## Use a Precooked `NERDA` model

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

Here is an example.

Instantiate a multilingual BERT model, that has been finetuned for NER in Danish,
`DA_BERT_ML`.

```python
from NERDA.precooked import DA_BERT_ML
model = DA_BERT_ML()
```

Down(load) network from web:

```python
model.download_network()
model.load_network()
```

You can now predict named entities in new (Danish) texts

```python
# (Danish) text to identify named entities in:
# 'Jens Hansen har en bondegård' = 'Old MacDonald had a farm'
text = 'Jens Hansen har en bondegård'
model.predict_text(text)
([['Jens', 'Hansen', 'har', 'en', 'bondegård']], [['B-PER', 'I-PER', 'O', 'O', 'O']])
```

### List of Precooked Models

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

| **Model**       | **Language** | **Transformer**   | **Dataset** | **F1-score** |  
|-----------------|--------------|-------------------|---------|-----|
| `DA_BERT_ML`    | Danish       | [Multilingual BERT](https://huggingface.co/bert-base-multilingual-uncased) | [DaNE](https://github.com/alexandrainst/danlp/blob/master/docs/docs/datasets.md#dane) | 82.8  | 
`DA_ELECTRA_DA` | Danish       | [Danish ELECTRA](https://huggingface.co/Maltehb/-l-ctra-danish-electra-small-uncased) | [DaNE](https://github.com/alexandrainst/danlp/blob/master/docs/docs/datasets.md#dane) | 79.8             |
| `EN_BERT_ML`    | English      | [Multilingual BERT](https://huggingface.co/bert-base-multilingual-uncased)| [CoNLL-2003](https://www.clips.uantwerpen.be/conll2003/ner/) | 90.4              |
| `EN_ELECTRA_EN` | English       | [English ELECTRA](https://huggingface.co/google/electra-small-discriminator) | [CoNLL-2003](https://www.clips.uantwerpen.be/conll2003/ner/) | 89.1             |

**F1-score** is the micro-averaged F1-score across entity tags and is 
evaluated on the respective test sets (that have not been used for training nor
validation of the models).

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.

### Model Performance

The table below summarizes the performance (F1-scores) of the precooked `NERDA` models.

| **Level**     | `DA_BERT_ML` | `DA_ELECTRA_DA` | `EN_BERT_ML` | `EN_ELECTRA_EN` |
|---------------|--------------|-----------------|--------------|-----------------|
| B-PER         | 93.8         | 92.0            | 96.0         | 95.1            |      
| I-PER         | 97.8         | 97.1            | 98.5         | 97.9            |   
| B-ORG         | 69.5         | 66.9            | 88.4         | 86.2            |     
| I-ORG         | 69.9         | 70.7            | 85.7         | 83.1            |   
| B-LOC         | 82.5         | 79.0            | 92.3         | 91.1            |     
| I-LOC         | 31.6         | 44.4            | 83.9         | 80.5            |     
| B-MISC        | 73.4         | 68.6            | 81.8         | 80.1            |     
| I-MISC        | 86.1         | 63.6            | 63.4         | 68.4            |   
| **AVG_MICRO** | 82.8         | 79.8            | 90.4         | 89.1            |      
| **AVG_MACRO** | 75.6         | 72.8            | 86.3         | 85.3            |

## '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 transformers for NER tasks for any arbitrary 
language.

## Background
`NERDA` is developed as a part of [Ekstra Bladet](https://ekstrabladet.dk/)’s activities on Platform Intelligence in News (PIN). PIN is an industrial research project that is carried out in collaboration between the [Technical University of Denmark](https://www.dtu.dk/), [University of Copenhagen](https://www.ku.dk/) and [Copenhagen Business School](https://www.cbs.dk/) with funding from [Innovation Fund Denmark](https://innovationsfonden.dk/). The project runs from 2020-2023 and develops recommender systems and natural language processing systems geared for news publishing, some of which are open sourced like `NERDA`.

## Shout-outs
- Thanks to [Alexandra Institute](https://alexandra.dk/) for with the [`danlp`](https://github.com/alexandrainst/danlp) package to have encouraged us to develop this package.
- Thanks to [Malte Højmark-Bertelsen](https://www.linkedin.com/in/malte-h%C3%B8jmark-bertelsen-9a618017b/) and [Kasper Junge](https://www.linkedin.com/in/kasper-juunge/?originalSubdomain=dk) for giving feedback on `NERDA`.

## Read more
The detailed documentation for `NERDA` including code references and
extended workflow examples can be accessed [here](https://ebanalyse.github.io/NERDA/).

## Cite this work

```
@inproceedings{nerda,
  title = {NERDA},
  author = {Kjeldgaard, Lars and Nielsen, Lukas},
  year = {2020},
  publisher = {{GitHub}},
  url = {https://github.com/ebanalyse/NERDA}
}
```

## Contact
We hope, that you will find `NERDA` useful.

Please direct any questions and feedbacks to
[us](mailto:lars.kjeldgaard@eb.dk)!

If you want to contribute (which we encourage you to), open a
[PR](https://github.com/ebanalyse/NERDA/pulls).

If you encounter a bug or want to suggest an enhancement, please 
[open an issue](https://github.com/ebanalyse/NERDA/issues).





%package -n python3-NERDA
Summary:	A Framework for Finetuning Transformers for Named-Entity Recognition
Provides:	python-NERDA
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-NERDA
# NERDA <img src="https://raw.githubusercontent.com/ebanalyse/NERDA/main/logo.png" align="right" height=250/>

![Build status](https://github.com/ebanalyse/NERDA/workflows/build/badge.svg)
[![codecov](https://codecov.io/gh/ebanalyse/NERDA/branch/main/graph/badge.svg?token=OB6LGFQZYX)](https://codecov.io/gh/ebanalyse/NERDA)
![PyPI](https://img.shields.io/pypi/v/NERDA.svg)
![PyPI - Downloads](https://img.shields.io/pypi/dm/NERDA?color=green)
![License](https://img.shields.io/badge/license-MIT-blue.svg)

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. 

You can also utilize `NERDA` to access a selection of *precooked* `NERDA` models, 
 that you can use right off the shelf for NER tasks.

`NERDA` is built on `huggingface` `transformers` and the popular `pytorch`
 framework.

## Installation guide
`NERDA` can be installed from [PyPI](https://pypi.org/project/NERDA/) with 

```
pip install NERDA
```

If you want the development version then install directly from [GitHub](https://github.com/ebanalyse/NERDA).

## 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.<sup>[1]</sup>

[1]: https://en.wikipedia.org/wiki/Named-entity_recognition

### 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](https://en.wikipedia.org/wiki/Named-entity_recognition).

## Train Your Own `NERDA` Model

Say, we want to fine-tune a pretrained [Multilingual BERT](https://huggingface.co/bert-base-multilingual-uncased) transformer for NER in English.

Load package.

```python
from NERDA.models import NERDA
```

Instantiate a `NERDA` model (*with default settings*) for the 
[`CoNLL-2003`](https://www.clips.uantwerpen.be/conll2003/ner/) 
English NER data set. 

```python
from NERDA.datasets import get_conll_data
model = NERDA(dataset_training = get_conll_data('train'),
              dataset_validation = get_conll_data('valid'),
              transformer = 'bert-base-multilingual-uncased')
```

By default the network architecture is analogous to that of the models in [Hvingelby et al. 2020](http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.565.pdf). 

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

```python
model.train()
```

**Note**: this will take some time depending on the dimensions of your machine 
(if you want to skip training, you can go ahead and use one of the models, 
that we have already precooked for you in stead).

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

```python
# text to identify named entities in.
text = 'Old MacDonald had a farm'
model.predict_text(text)
([['Old', 'MacDonald', 'had', 'a', 'farm']], [['B-PER', 'I-PER', 'O', 'O', 'O']])
```
This means, that the model identified 'Old MacDonald' as a *PER*son.

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 (finetune a transformer for any given language)
- 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](https://ebanalyse.github.io/NERDA/workflow).

## Use a Precooked `NERDA` model

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

Here is an example.

Instantiate a multilingual BERT model, that has been finetuned for NER in Danish,
`DA_BERT_ML`.

```python
from NERDA.precooked import DA_BERT_ML
model = DA_BERT_ML()
```

Down(load) network from web:

```python
model.download_network()
model.load_network()
```

You can now predict named entities in new (Danish) texts

```python
# (Danish) text to identify named entities in:
# 'Jens Hansen har en bondegård' = 'Old MacDonald had a farm'
text = 'Jens Hansen har en bondegård'
model.predict_text(text)
([['Jens', 'Hansen', 'har', 'en', 'bondegård']], [['B-PER', 'I-PER', 'O', 'O', 'O']])
```

### List of Precooked Models

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

| **Model**       | **Language** | **Transformer**   | **Dataset** | **F1-score** |  
|-----------------|--------------|-------------------|---------|-----|
| `DA_BERT_ML`    | Danish       | [Multilingual BERT](https://huggingface.co/bert-base-multilingual-uncased) | [DaNE](https://github.com/alexandrainst/danlp/blob/master/docs/docs/datasets.md#dane) | 82.8  | 
`DA_ELECTRA_DA` | Danish       | [Danish ELECTRA](https://huggingface.co/Maltehb/-l-ctra-danish-electra-small-uncased) | [DaNE](https://github.com/alexandrainst/danlp/blob/master/docs/docs/datasets.md#dane) | 79.8             |
| `EN_BERT_ML`    | English      | [Multilingual BERT](https://huggingface.co/bert-base-multilingual-uncased)| [CoNLL-2003](https://www.clips.uantwerpen.be/conll2003/ner/) | 90.4              |
| `EN_ELECTRA_EN` | English       | [English ELECTRA](https://huggingface.co/google/electra-small-discriminator) | [CoNLL-2003](https://www.clips.uantwerpen.be/conll2003/ner/) | 89.1             |

**F1-score** is the micro-averaged F1-score across entity tags and is 
evaluated on the respective test sets (that have not been used for training nor
validation of the models).

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.

### Model Performance

The table below summarizes the performance (F1-scores) of the precooked `NERDA` models.

| **Level**     | `DA_BERT_ML` | `DA_ELECTRA_DA` | `EN_BERT_ML` | `EN_ELECTRA_EN` |
|---------------|--------------|-----------------|--------------|-----------------|
| B-PER         | 93.8         | 92.0            | 96.0         | 95.1            |      
| I-PER         | 97.8         | 97.1            | 98.5         | 97.9            |   
| B-ORG         | 69.5         | 66.9            | 88.4         | 86.2            |     
| I-ORG         | 69.9         | 70.7            | 85.7         | 83.1            |   
| B-LOC         | 82.5         | 79.0            | 92.3         | 91.1            |     
| I-LOC         | 31.6         | 44.4            | 83.9         | 80.5            |     
| B-MISC        | 73.4         | 68.6            | 81.8         | 80.1            |     
| I-MISC        | 86.1         | 63.6            | 63.4         | 68.4            |   
| **AVG_MICRO** | 82.8         | 79.8            | 90.4         | 89.1            |      
| **AVG_MACRO** | 75.6         | 72.8            | 86.3         | 85.3            |

## '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 transformers for NER tasks for any arbitrary 
language.

## Background
`NERDA` is developed as a part of [Ekstra Bladet](https://ekstrabladet.dk/)’s activities on Platform Intelligence in News (PIN). PIN is an industrial research project that is carried out in collaboration between the [Technical University of Denmark](https://www.dtu.dk/), [University of Copenhagen](https://www.ku.dk/) and [Copenhagen Business School](https://www.cbs.dk/) with funding from [Innovation Fund Denmark](https://innovationsfonden.dk/). The project runs from 2020-2023 and develops recommender systems and natural language processing systems geared for news publishing, some of which are open sourced like `NERDA`.

## Shout-outs
- Thanks to [Alexandra Institute](https://alexandra.dk/) for with the [`danlp`](https://github.com/alexandrainst/danlp) package to have encouraged us to develop this package.
- Thanks to [Malte Højmark-Bertelsen](https://www.linkedin.com/in/malte-h%C3%B8jmark-bertelsen-9a618017b/) and [Kasper Junge](https://www.linkedin.com/in/kasper-juunge/?originalSubdomain=dk) for giving feedback on `NERDA`.

## Read more
The detailed documentation for `NERDA` including code references and
extended workflow examples can be accessed [here](https://ebanalyse.github.io/NERDA/).

## Cite this work

```
@inproceedings{nerda,
  title = {NERDA},
  author = {Kjeldgaard, Lars and Nielsen, Lukas},
  year = {2020},
  publisher = {{GitHub}},
  url = {https://github.com/ebanalyse/NERDA}
}
```

## Contact
We hope, that you will find `NERDA` useful.

Please direct any questions and feedbacks to
[us](mailto:lars.kjeldgaard@eb.dk)!

If you want to contribute (which we encourage you to), open a
[PR](https://github.com/ebanalyse/NERDA/pulls).

If you encounter a bug or want to suggest an enhancement, please 
[open an issue](https://github.com/ebanalyse/NERDA/issues).





%package help
Summary:	Development documents and examples for NERDA
Provides:	python3-NERDA-doc
%description help
# NERDA <img src="https://raw.githubusercontent.com/ebanalyse/NERDA/main/logo.png" align="right" height=250/>

![Build status](https://github.com/ebanalyse/NERDA/workflows/build/badge.svg)
[![codecov](https://codecov.io/gh/ebanalyse/NERDA/branch/main/graph/badge.svg?token=OB6LGFQZYX)](https://codecov.io/gh/ebanalyse/NERDA)
![PyPI](https://img.shields.io/pypi/v/NERDA.svg)
![PyPI - Downloads](https://img.shields.io/pypi/dm/NERDA?color=green)
![License](https://img.shields.io/badge/license-MIT-blue.svg)

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. 

You can also utilize `NERDA` to access a selection of *precooked* `NERDA` models, 
 that you can use right off the shelf for NER tasks.

`NERDA` is built on `huggingface` `transformers` and the popular `pytorch`
 framework.

## Installation guide
`NERDA` can be installed from [PyPI](https://pypi.org/project/NERDA/) with 

```
pip install NERDA
```

If you want the development version then install directly from [GitHub](https://github.com/ebanalyse/NERDA).

## 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.<sup>[1]</sup>

[1]: https://en.wikipedia.org/wiki/Named-entity_recognition

### 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](https://en.wikipedia.org/wiki/Named-entity_recognition).

## Train Your Own `NERDA` Model

Say, we want to fine-tune a pretrained [Multilingual BERT](https://huggingface.co/bert-base-multilingual-uncased) transformer for NER in English.

Load package.

```python
from NERDA.models import NERDA
```

Instantiate a `NERDA` model (*with default settings*) for the 
[`CoNLL-2003`](https://www.clips.uantwerpen.be/conll2003/ner/) 
English NER data set. 

```python
from NERDA.datasets import get_conll_data
model = NERDA(dataset_training = get_conll_data('train'),
              dataset_validation = get_conll_data('valid'),
              transformer = 'bert-base-multilingual-uncased')
```

By default the network architecture is analogous to that of the models in [Hvingelby et al. 2020](http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.565.pdf). 

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

```python
model.train()
```

**Note**: this will take some time depending on the dimensions of your machine 
(if you want to skip training, you can go ahead and use one of the models, 
that we have already precooked for you in stead).

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

```python
# text to identify named entities in.
text = 'Old MacDonald had a farm'
model.predict_text(text)
([['Old', 'MacDonald', 'had', 'a', 'farm']], [['B-PER', 'I-PER', 'O', 'O', 'O']])
```
This means, that the model identified 'Old MacDonald' as a *PER*son.

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 (finetune a transformer for any given language)
- 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](https://ebanalyse.github.io/NERDA/workflow).

## Use a Precooked `NERDA` model

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

Here is an example.

Instantiate a multilingual BERT model, that has been finetuned for NER in Danish,
`DA_BERT_ML`.

```python
from NERDA.precooked import DA_BERT_ML
model = DA_BERT_ML()
```

Down(load) network from web:

```python
model.download_network()
model.load_network()
```

You can now predict named entities in new (Danish) texts

```python
# (Danish) text to identify named entities in:
# 'Jens Hansen har en bondegård' = 'Old MacDonald had a farm'
text = 'Jens Hansen har en bondegård'
model.predict_text(text)
([['Jens', 'Hansen', 'har', 'en', 'bondegård']], [['B-PER', 'I-PER', 'O', 'O', 'O']])
```

### List of Precooked Models

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

| **Model**       | **Language** | **Transformer**   | **Dataset** | **F1-score** |  
|-----------------|--------------|-------------------|---------|-----|
| `DA_BERT_ML`    | Danish       | [Multilingual BERT](https://huggingface.co/bert-base-multilingual-uncased) | [DaNE](https://github.com/alexandrainst/danlp/blob/master/docs/docs/datasets.md#dane) | 82.8  | 
`DA_ELECTRA_DA` | Danish       | [Danish ELECTRA](https://huggingface.co/Maltehb/-l-ctra-danish-electra-small-uncased) | [DaNE](https://github.com/alexandrainst/danlp/blob/master/docs/docs/datasets.md#dane) | 79.8             |
| `EN_BERT_ML`    | English      | [Multilingual BERT](https://huggingface.co/bert-base-multilingual-uncased)| [CoNLL-2003](https://www.clips.uantwerpen.be/conll2003/ner/) | 90.4              |
| `EN_ELECTRA_EN` | English       | [English ELECTRA](https://huggingface.co/google/electra-small-discriminator) | [CoNLL-2003](https://www.clips.uantwerpen.be/conll2003/ner/) | 89.1             |

**F1-score** is the micro-averaged F1-score across entity tags and is 
evaluated on the respective test sets (that have not been used for training nor
validation of the models).

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.

### Model Performance

The table below summarizes the performance (F1-scores) of the precooked `NERDA` models.

| **Level**     | `DA_BERT_ML` | `DA_ELECTRA_DA` | `EN_BERT_ML` | `EN_ELECTRA_EN` |
|---------------|--------------|-----------------|--------------|-----------------|
| B-PER         | 93.8         | 92.0            | 96.0         | 95.1            |      
| I-PER         | 97.8         | 97.1            | 98.5         | 97.9            |   
| B-ORG         | 69.5         | 66.9            | 88.4         | 86.2            |     
| I-ORG         | 69.9         | 70.7            | 85.7         | 83.1            |   
| B-LOC         | 82.5         | 79.0            | 92.3         | 91.1            |     
| I-LOC         | 31.6         | 44.4            | 83.9         | 80.5            |     
| B-MISC        | 73.4         | 68.6            | 81.8         | 80.1            |     
| I-MISC        | 86.1         | 63.6            | 63.4         | 68.4            |   
| **AVG_MICRO** | 82.8         | 79.8            | 90.4         | 89.1            |      
| **AVG_MACRO** | 75.6         | 72.8            | 86.3         | 85.3            |

## '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 transformers for NER tasks for any arbitrary 
language.

## Background
`NERDA` is developed as a part of [Ekstra Bladet](https://ekstrabladet.dk/)’s activities on Platform Intelligence in News (PIN). PIN is an industrial research project that is carried out in collaboration between the [Technical University of Denmark](https://www.dtu.dk/), [University of Copenhagen](https://www.ku.dk/) and [Copenhagen Business School](https://www.cbs.dk/) with funding from [Innovation Fund Denmark](https://innovationsfonden.dk/). The project runs from 2020-2023 and develops recommender systems and natural language processing systems geared for news publishing, some of which are open sourced like `NERDA`.

## Shout-outs
- Thanks to [Alexandra Institute](https://alexandra.dk/) for with the [`danlp`](https://github.com/alexandrainst/danlp) package to have encouraged us to develop this package.
- Thanks to [Malte Højmark-Bertelsen](https://www.linkedin.com/in/malte-h%C3%B8jmark-bertelsen-9a618017b/) and [Kasper Junge](https://www.linkedin.com/in/kasper-juunge/?originalSubdomain=dk) for giving feedback on `NERDA`.

## Read more
The detailed documentation for `NERDA` including code references and
extended workflow examples can be accessed [here](https://ebanalyse.github.io/NERDA/).

## Cite this work

```
@inproceedings{nerda,
  title = {NERDA},
  author = {Kjeldgaard, Lars and Nielsen, Lukas},
  year = {2020},
  publisher = {{GitHub}},
  url = {https://github.com/ebanalyse/NERDA}
}
```

## Contact
We hope, that you will find `NERDA` useful.

Please direct any questions and feedbacks to
[us](mailto:lars.kjeldgaard@eb.dk)!

If you want to contribute (which we encourage you to), open a
[PR](https://github.com/ebanalyse/NERDA/pulls).

If you encounter a bug or want to suggest an enhancement, please 
[open an issue](https://github.com/ebanalyse/NERDA/issues).





%prep
%autosetup -n NERDA-1.0.0

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-NERDA -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.0-1
- Package Spec generated