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Migrate model card from transformers-repo

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Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/KB/bert-base-swedish-cased-ner/README.md

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+ ---
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+ language: sv
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+ ---
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+
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+ # Swedish BERT Models
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+
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+ The National Library of Sweden / KBLab releases three pretrained language models based on BERT and ALBERT. The models are trained on approximately 15-20GB of text (200M sentences, 3000M tokens) from various sources (books, news, government publications, swedish wikipedia and internet forums) aiming to provide a representative BERT model for Swedish text. A more complete description will be published later on.
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+
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+ The following three models are currently available:
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+
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+ - **bert-base-swedish-cased** (*v1*) - A BERT trained with the same hyperparameters as first published by Google.
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+ - **bert-base-swedish-cased-ner** (*experimental*) - a BERT fine-tuned for NER using SUC 3.0.
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+ - **albert-base-swedish-cased-alpha** (*alpha*) - A first attempt at an ALBERT for Swedish.
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+
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+ All models are cased and trained with whole word masking.
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+
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+ ## Files
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+
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+ | **name** | **files** |
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+ |---------------------------------|-----------|
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+ | bert-base-swedish-cased | [config](https://s3.amazonaws.com/models.huggingface.co/bert/KB/bert-base-swedish-cased/config.json), [vocab](https://s3.amazonaws.com/models.huggingface.co/bert/KB/bert-base-swedish-cased/vocab.txt), [pytorch_model.bin](https://s3.amazonaws.com/models.huggingface.co/bert/KB/bert-base-swedish-cased/pytorch_model.bin) |
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+ | bert-base-swedish-cased-ner | [config](https://s3.amazonaws.com/models.huggingface.co/bert/KB/bert-base-swedish-cased-ner/config.json), [vocab](https://s3.amazonaws.com/models.huggingface.co/bert/KB/bert-base-swedish-cased-ner/vocab.txt) [pytorch_model.bin](https://s3.amazonaws.com/models.huggingface.co/bert/KB/bert-base-swedish-cased-ner/pytorch_model.bin) |
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+ | albert-base-swedish-cased-alpha | [config](https://s3.amazonaws.com/models.huggingface.co/bert/KB/albert-base-swedish-cased-alpha/config.json), [sentencepiece model](https://s3.amazonaws.com/models.huggingface.co/bert/KB/albert-base-swedish-cased-alpha/spiece.model), [pytorch_model.bin](https://s3.amazonaws.com/models.huggingface.co/bert/KB/albert-base-swedish-cased-alpha/pytorch_model.bin) |
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+
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+ TensorFlow model weights will be released soon.
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+
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+ ## Usage requirements / installation instructions
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+
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+ The examples below require Huggingface Transformers 2.4.1 and Pytorch 1.3.1 or greater. For Transformers<2.4.0 the tokenizer must be instantiated manually and the `do_lower_case` flag parameter set to `False` and `keep_accents` to `True` (for ALBERT).
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+
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+ To create an environment where the examples can be run, run the following in an terminal on your OS of choice.
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+
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+ ```
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+ # git clone https://github.com/Kungbib/swedish-bert-models
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+ # cd swedish-bert-models
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+ # python3 -m venv venv
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+ # source venv/bin/activate
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+ # pip install --upgrade pip
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+ # pip install -r requirements.txt
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+ ```
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+
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+ ### BERT Base Swedish
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+
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+ A standard BERT base for Swedish trained on a variety of sources. Vocabulary size is ~50k. Using Huggingface Transformers the model can be loaded in Python as follows:
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+
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+ ```python
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+ from transformers import AutoModel,AutoTokenizer
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+
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+ tok = AutoTokenizer.from_pretrained('KB/bert-base-swedish-cased')
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+ model = AutoModel.from_pretrained('KB/bert-base-swedish-cased')
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+ ```
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+
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+
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+ ### BERT base fine-tuned for Swedish NER
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+
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+ This model is fine-tuned on the SUC 3.0 dataset. Using the Huggingface pipeline the model can be easily instantiated. For Transformer<2.4.1 it seems the tokenizer must be loaded separately to disable lower-casing of input strings:
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+
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+ ```python
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+ from transformers import pipeline
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+
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+ nlp = pipeline('ner', model='KB/bert-base-swedish-cased-ner', tokenizer='KB/bert-base-swedish-cased-ner')
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+
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+ nlp('Idag släpper KB tre språkmodeller.')
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+ ```
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+
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+ Running the Python code above should produce in something like the result below. Entity types used are `TME` for time, `PRS` for personal names, `LOC` for locations, `EVN` for events and `ORG` for organisations. These labels are subject to change.
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+
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+ ```python
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+ [ { 'word': 'Idag', 'score': 0.9998126029968262, 'entity': 'TME' },
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+ { 'word': 'KB', 'score': 0.9814832210540771, 'entity': 'ORG' } ]
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+ ```
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+
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+ The BERT tokenizer often splits words into multiple tokens, with the subparts starting with `##`, for example the string `Engelbert kör Volvo till Herrängens fotbollsklubb` gets tokenized as `Engel ##bert kör Volvo till Herr ##ängens fotbolls ##klubb`. To glue parts back together one can use something like this:
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+
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+ ```python
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+ text = 'Engelbert tar Volvon till Tele2 Arena för att titta på Djurgården IF ' +\
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+ 'som spelar fotboll i VM klockan två på kvällen.'
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+
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+ l = []
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+ for token in nlp(text):
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+ if token['word'].startswith('##'):
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+ l[-1]['word'] += token['word'][2:]
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+ else:
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+ l += [ token ]
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+
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+ print(l)
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+ ```
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+
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+ Which should result in the following (though less cleanly formatted):
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+
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+ ```python
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+ [ { 'word': 'Engelbert', 'score': 0.99..., 'entity': 'PRS'},
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+ { 'word': 'Volvon', 'score': 0.99..., 'entity': 'OBJ'},
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+ { 'word': 'Tele2', 'score': 0.99..., 'entity': 'LOC'},
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+ { 'word': 'Arena', 'score': 0.99..., 'entity': 'LOC'},
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+ { 'word': 'Djurgården', 'score': 0.99..., 'entity': 'ORG'},
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+ { 'word': 'IF', 'score': 0.99..., 'entity': 'ORG'},
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+ { 'word': 'VM', 'score': 0.99..., 'entity': 'EVN'},
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+ { 'word': 'klockan', 'score': 0.99..., 'entity': 'TME'},
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+ { 'word': 'två', 'score': 0.99..., 'entity': 'TME'},
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+ { 'word': 'på', 'score': 0.99..., 'entity': 'TME'},
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+ { 'word': 'kvällen', 'score': 0.54..., 'entity': 'TME'} ]
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+ ```
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+
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+ ### ALBERT base
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+
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+ The easiest way to do this is, again, using Huggingface Transformers:
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+ ```python
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+ from transformers import AutoModel,AutoTokenizer
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+
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+ tok = AutoTokenizer.from_pretrained('KB/albert-base-swedish-cased-alpha'),
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+ model = AutoModel.from_pretrained('KB/albert-base-swedish-cased-alpha')
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+ ```
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+
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+ ## Acknowledgements ❤️
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+
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+ - Resources from Stockholms University, Umeå University and Swedish Language Bank at Gothenburg University were used when fine-tuning BERT for NER.
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+ - Model pretraining was made partly in-house at the KBLab and partly (for material without active copyright) with the support of Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).
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+ - Models are hosted on S3 by Huggingface 🤗
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+