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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: sv |
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datasets: |
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- SUC 3.0 |
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widget: |
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- text: "Hampus bor i Skåne och har levererat denna model idag." |
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--- |
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Published with ❤️ from [londogard](https://londogard.com). |
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## Swedish NER in Flair (SUC 3.0) |
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F1-Score: **85.6** (SUC 3.0) |
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Predicts 8 tags: |
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|**Tag**|**Meaning**| |
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|---|---| |
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| PRS| person name | |
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| ORG | organisation name| |
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| TME | time unit | |
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| WRK | building name | |
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| LOC | location name | |
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| EVN | event name | |
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| MSR | measurement unit | |
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| OBJ | object (like "Rolls-Royce" is a object in the form of a special car) | |
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Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) 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("londogard/flair-swe-ner") |
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# make example sentence |
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sentence = Sentence("Hampus bor i Skåne och har levererat denna model idag.") |
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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 [0]: "Hampus" [− Labels: PRS (1.0)] |
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Span [3]: "Skåne" [− Labels: LOC (1.0)] |
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Span [9]: "idag" [− Labels: TME(1.0)] |
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``` |
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So, the entities "_Hampus_" (labeled as a **PRS**), "_Skåne_" (labeled as a **LOC**), "_idag_" (labeled as a **TME**) are found in the sentence "_Hampus bor i Skåne och har levererat denna model idag._". |
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--- |
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**Please mention londogard if using this models.** |
