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--- |
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tags: |
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- flair |
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- token-classification |
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- sequence-tagger-model |
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language: da |
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datasets: |
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- DaNE |
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widget: |
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- text: "Jens Peter Hansen kommer fra Danmark" |
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--- |
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# Danish NER in Flair (default model) |
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This is the standard 4-class NER model for Danish that ships with [Flair](https://github.com/flairNLP/flair/). |
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F1-Score: **81.78** (DaNER) |
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Predicts 4 tags: |
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| **tag** | **meaning** | |
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|---------------------------------|-----------| |
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| PER | person name | |
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| LOC | location name | |
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| ORG | organization name | |
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| MISC | other name | |
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Based on Transformer embeddings and LSTM-CRF. |
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--- |
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# Demo: How to use in Flair |
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Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`) |
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```python |
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from flair.data import Sentence |
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from flair.models import SequenceTagger |
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# load tagger |
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tagger = SequenceTagger.load("flair/ner-danish") |
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# make example sentence |
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sentence = Sentence("Jens Peter Hansen kommer fra Danmark") |
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# predict NER tags |
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tagger.predict(sentence) |
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# print sentence |
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print(sentence) |
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# print predicted NER spans |
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print('The following NER tags are found:') |
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# iterate over entities and print |
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for entity in sentence.get_spans('ner'): |
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print(entity) |
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``` |
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This yields the following output: |
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``` |
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Span [1,2,3]: "Jens Peter Hansen" [− Labels: PER (0.9961)] |
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Span [6]: "Danmark" [− Labels: LOC (0.9816)] |
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``` |
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So, the entities "*Jens Peter Hansen*" (labeled as a **person**) and "*Danmark*" (labeled as a **location**) are found in the sentence "*Jens Peter Hansen kommer fra Danmark*". |
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--- |
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### Training: Script to train this model |
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The model was trained by the [DaNLP project](https://github.com/alexandrainst/danlp) using the [DaNE corpus](https://github.com/alexandrainst/danlp/blob/master/docs/docs/datasets.md#danish-dependency-treebank-dane-dane). Check their repo for more information. |
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The following Flair script may be used to train such a model: |
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```python |
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from flair.data import Corpus |
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from flair.datasets import DANE |
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from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings |
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# 1. get the corpus |
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corpus: Corpus = DANE() |
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# 2. what tag do we want to predict? |
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tag_type = 'ner' |
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# 3. make the tag dictionary from the corpus |
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tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type) |
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# 4. initialize each embedding we use |
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embedding_types = [ |
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# GloVe embeddings |
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WordEmbeddings('da'), |
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# contextual string embeddings, forward |
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FlairEmbeddings('da-forward'), |
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# contextual string embeddings, backward |
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FlairEmbeddings('da-backward'), |
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] |
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# embedding stack consists of Flair and GloVe embeddings |
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embeddings = StackedEmbeddings(embeddings=embedding_types) |
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# 5. initialize sequence tagger |
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from flair.models import SequenceTagger |
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tagger = SequenceTagger(hidden_size=256, |
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embeddings=embeddings, |
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tag_dictionary=tag_dictionary, |
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tag_type=tag_type) |
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# 6. initialize trainer |
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from flair.trainers import ModelTrainer |
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trainer = ModelTrainer(tagger, corpus) |
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# 7. run training |
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trainer.train('resources/taggers/ner-danish', |
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train_with_dev=True, |
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max_epochs=150) |
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``` |
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--- |
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### Cite |
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Please cite the following papers when using this model. |
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``` |
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@inproceedings{akbik-etal-2019-flair, |
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title = "{FLAIR}: An Easy-to-Use Framework for State-of-the-Art {NLP}", |
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author = "Akbik, Alan and |
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Bergmann, Tanja and |
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Blythe, Duncan and |
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Rasul, Kashif and |
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Schweter, Stefan and |
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Vollgraf, Roland", |
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booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)", |
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year = "2019", |
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url = "https://www.aclweb.org/anthology/N19-4010", |
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pages = "54--59", |
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} |
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``` |
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And check the [DaNLP project](https://github.com/alexandrainst/danlp) for more information. |
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--- |
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### Issues? |
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The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/). |
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