### Install Swedish BERT Models Environment

Source: https://github.com/kungbib/swedish-bert-models/blob/master/README.md

This snippet outlines the steps to set up a Python virtual environment and install the necessary dependencies for running the Swedish BERT models. It includes cloning the repository, creating a virtual environment, and installing requirements.

```bash
# git clone https://github.com/Kungbib/swedish-bert-models
# cd swedish-bert-models
# python3 -m venv venv
# source venv/bin/activate
# pip install --upgrade pip
# pip install -r requirements.txt
```

--------------------------------

### NER Output Example

Source: https://github.com/kungbib/swedish-bert-models/blob/master/README.md

Example of the expected output from the NER pipeline, showing identified entities and their scores.

```python
[
  { 'word': 'Kalle', 'score': 0.9998126029968262, 'entity': 'PER' },
  { 'word': 'Pelle', 'score': 0.9998126029968262, 'entity': 'PER' },
  { 'word': 'Kalle',   'score': 0.9814832210540771, 'entity': 'ORG' },
  { 'word': 'och',   'score': 0.9814832210540771, 'entity': 'ORG' },
  { 'word': 'Pelle',   'score': 0.9814832210540771, 'entity': 'ORG' }
]
```

--------------------------------

### Reconstructed Tokens Output Example

Source: https://github.com/kungbib/swedish-bert-models/blob/master/README.md

Example output after reconstructing sub-tokens, showing correctly merged words and their associated entities.

```python
[
  { 'word': 'Engelbert',     'score': 0.99..., 'entity': 'PRS'},
  { 'word': 'Rolls',         'score': 0.99..., 'entity': 'OBJ'},
  { 'word': '-',             'score': 0.99..., 'entity': 'OBJ'},
  { 'word': 'Royce',         'score': 0.99..., 'entity': 'OBJ'},
  { 'word': 'Tele2',         'score': 0.99..., 'entity': 'LOC'},
  { 'word': 'Arena',         'score': 0.99..., 'entity': 'LOC'},
  { 'word': 'Djurgården',    'score': 0.99..., 'entity': 'ORG'},
  { 'word': 'IF',            'score': 0.99..., 'entity': 'ORG'},
  { 'word': 'VM',            'score': 0.99..., 'entity': 'EVN'},
  { 'word': 'klockan',       'score': 0.99..., 'entity': 'TME'},
  { 'word': 'två',           'score': 0.99..., 'entity': 'TME'},
  { 'word': 'på',            'score': 0.99..., 'entity': 'TME'},
  { 'word': 'kvällen',       'score': 0.54..., 'entity': 'TME'}
]
```

--------------------------------

### BibTeX Citation for Swedish BERT Models

Source: https://github.com/kungbib/swedish-bert-models/blob/master/README.md

This snippet provides the BibTeX formatted citation for the project, including title, authors, year, and arXiv details.

```bibtex
@misc{swedish-bert,
    title={Playing with Words at the National Library of Sweden -- Making a Swedish BERT},
    author={Martin Malmsten and Love Börjeson and Chris Haffenden},
    year={2020},
    eprint={2007.01658},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

--------------------------------

### Load BERT Base Swedish Cased Model and Tokenizer

Source: https://github.com/kungbib/swedish-bert-models/blob/master/README.md

Demonstrates how to load the pre-trained 'bert-base-swedish-cased' model and its corresponding tokenizer using the Huggingface Transformers library in Python. This is a standard BERT base model for Swedish.

```python
from transformers import AutoModel,AutoTokenizer,TFAutoModel

tok = AutoTokenizer.from_pretrained('KB/bert-base-swedish-cased')
model = AutoModel.from_pretrained('KB/bert-base-swedish-cased')
```

--------------------------------

### NER Pipeline with Swedish BERT

Source: https://github.com/kungbib/swedish-bert-models/blob/master/README.md

Instantiates a Named Entity Recognition (NER) pipeline using a Swedish BERT model fine-tuned for NER. Handles potential tokenizer issues with older Transformer versions.

```python
from transformers import pipeline, TFBertForTokenClassification

nlp = pipeline('ner', model='KB/bert-base-swedish-cased-ner', tokenizer='KB/bert-base-swedish-cased-ner')

nlp('Kalle och Pelle startar firman Kalle och Pelle.')

# Specifically using Tensorflow
tf = TFBertForTokenClassification.from_pretrained('KB/bert-base-swedish-cased-ner')
nlp = pipeline('ner', model=tf, tokenizer='KB/bert-base-swedish-cased-ner')
```

--------------------------------

### Load ALBERT Base Model and Tokenizer

Source: https://github.com/kungbib/swedish-bert-models/blob/master/README.md

Loads a pre-trained ALBERT base model and its corresponding tokenizer from Huggingface for Swedish text.

```python
from transformers import AutoModel, AutoTokenizer

tok = AutoTokenizer.from_pretrained('KB/albert-base-swedish-cased-alpha')
model = AutoModel.from_pretrained('KB/albert-base-swedish-cased-alpha')
```

--------------------------------

### Available Swedish BERT and ALBERT Models

Source: https://github.com/kungbib/swedish-bert-models/blob/master/README.md

Lists the different Swedish language models available from KBLab, including their type (BERT, ALBERT, Electra), casing, and experimental status. Some models are fine-tuned for specific tasks like NER and POS tagging.

```text
- bert-base-swedish-cased (*v1.1*) - A BERT trained with the same hyperparameters as first published by Google.
- bert-base-swedish-cased-ner (*experimental*) - a BERT fine-tuned for NER using SUC 3.0.
- bert-base-swedish-cased-pos (*experimental*) - a BERT fine-tuned for POS using SUC 3.0.
- bert-base-swedish-cased-squad-experimental (*experimental*) - A first attempt att a Swedish QA using machine translated SQuAD 2.0 dataset
- albert-base-swedish-cased-alpha (*alpha*) - A first attempt at an ALBERT for Swedish.
- electra-small-swedish-cased - Small Electra models (generator and discriminator) trained for one million steps.
```

--------------------------------

### Load TF BERT Model

Source: https://github.com/kungbib/swedish-bert-models/blob/master/README.md

Loads a pre-trained TensorFlow BERT model from Huggingface for general use.

```python
from transformers import TFAutoModel
model = TFAutoModel.from_pretrained('KB/bert-base-swedish-cased')
```

--------------------------------

### Reconstructing Tokens with '##'

Source: https://github.com/kungbib/swedish-bert-models/blob/master/README.md

Demonstrates how to reconstruct words that have been split into sub-tokens (prefixed with '##') by the BERT tokenizer, particularly for NER tasks.

```python
text = 'Engelbert tar sin Rolls-Royce till Tele2 Arena för att titta på Djurgården IF ' +\
       'som spelar fotboll i VM klockan två på kvällen.'

nlp = pipeline('ner', model='KB/bert-base-swedish-cased-ner', tokenizer='KB/bert-base-swedish-cased-ner', ignore_labels=[])
l = []
t = nlp(text)
in_word=False

for i,token in enumerate(t):
    if token['entity'] == 'O':
        in_word = False
        continue

    if token['word'].startswith('##'):
        # deal with (one level of) orphaned ##-tokens
        if not in_word:
            l += [ t[i-1] ]
            l[-1]['entity'] = token['entity']
        
        l[-1]['word'] += token['word'][2:]
    else:
        l += [ token ]

    in_word = True

print(l)
```

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