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--- |
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language: is |
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license: apache-2.0 |
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widget: |
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- text: "Kristin manneskja getur ekki lagt frásagnir af Jesú Kristi á hilluna vegna þess að hún sé búin að lesa þær ." |
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- text: "Til hvers að kjósa flokk , sem þykist vera Jafnaðarmannaflokkur rétt fyrir kosningar , þegar að það er hægt að kjósa sannnan jafnaðarmannaflokk , sjálfan Jafnaðarmannaflokk Íslands - Samfylkinguna ." |
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- text: "Það sannaðist svo eftirminnilega á plötunni Það þarf fólk eins og þig sem kom út fyrir þremur árum , en á henni hann Fálka úr Keflavík og Gáluna , son sinn , til að útsetja lög hans og spila inn ." |
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- text: "Lögin hafa áður komið út sem aukalög á smáskífum af Hail to the Thief , en á disknum er líka myndband og fleira efni fyrir tölvur ." |
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- text: "Britney gerði honum viðvart og hann ók henni á UCLA-sjúkrahúsið í Santa Monica en það er í nágrenni hljóðversins ." |
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--- |
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# IcelandicNER BERT |
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This model was fine-tuned on the MIM-GOLD-NER dataset for the Icelandic language. |
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The [MIM-GOLD-NER](http://hdl.handle.net/20.500.12537/42) corpus was developed at [Reykjavik University](https://en.ru.is/) in 2018–2020 that covered eight types of entities: |
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- Date |
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- Location |
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- Miscellaneous |
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- Money |
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- Organization |
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- Percent |
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- Person |
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- Time |
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## Dataset Information |
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| | Records | B-Date | B-Location | B-Miscellaneous | B-Money | B-Organization | B-Percent | B-Person | B-Time | I-Date | I-Location | I-Miscellaneous | I-Money | I-Organization | I-Percent | I-Person | I-Time | |
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|:------|----------:|---------:|-------------:|------------------:|----------:|-----------------:|------------:|-----------:|---------:|---------:|-------------:|------------------:|----------:|-----------------:|------------:|-----------:|---------:| |
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| Train | 39988 | 3409 | 5980 | 4351 | 729 | 5754 | 502 | 11719 | 868 | 2112 | 516 | 3036 | 770 | 2382 | 50 | 5478 | 790 | |
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| Valid | 7063 | 570 | 1034 | 787 | 100 | 1078 | 103 | 2106 | 147 | 409 | 76 | 560 | 104 | 458 | 7 | 998 | 136 | |
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| Test | 8299 | 779 | 1319 | 935 | 153 | 1315 | 108 | 2247 | 172 | 483 | 104 | 660 | 167 | 617 | 10 | 1089 | 158 | |
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## Evaluation |
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The following tables summarize the scores obtained by model overall and per each class. |
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| entity | precision | recall | f1-score | support | |
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|:-------------:|:---------:|:--------:|:--------:|:-------:| |
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| Date | 0.969466 | 0.978177 | 0.973802 | 779.0 | |
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| Location | 0.955201 | 0.953753 | 0.954476 | 1319.0 | |
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| Miscellaneous | 0.867033 | 0.843850 | 0.855285 | 935.0 | |
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| Money | 0.979730 | 0.947712 | 0.963455 | 153.0 | |
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| Organization | 0.893939 | 0.897338 | 0.895636 | 1315.0 | |
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| Percent | 1.000000 | 1.000000 | 1.000000 | 108.0 | |
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| Person | 0.963028 | 0.973743 | 0.968356 | 2247.0 | |
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| Time | 0.976879 | 0.982558 | 0.979710 | 172.0 | |
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| micro avg | 0.938158 | 0.938958 | 0.938558 | 7028.0 | |
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| macro avg | 0.950659 | 0.947141 | 0.948840 | 7028.0 | |
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| weighted avg | 0.937845 | 0.938958 | 0.938363 | 7028.0 | |
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## How To Use |
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You use this model with Transformers pipeline for NER. |
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### Installing requirements |
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```bash |
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pip install transformers |
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``` |
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### How to predict using pipeline |
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```python |
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from transformers import AutoTokenizer |
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from transformers import AutoModelForTokenClassification # for pytorch |
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from transformers import TFAutoModelForTokenClassification # for tensorflow |
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from transformers import pipeline |
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model_name_or_path = "grammatek/icelandic-ner-bert" |
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tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) |
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model = AutoModelForTokenClassification.from_pretrained(model_name_or_path) # Pytorch |
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# model = TFAutoModelForTokenClassification.from_pretrained(model_name_or_path) # Tensorflow |
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nlp = pipeline("ner", model=model, tokenizer=tokenizer) |
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example = "Kristin manneskja getur ekki lagt frásagnir af Jesú Kristi á hilluna vegna þess að hún sé búin að lesa þær ." |
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ner_results = nlp(example) |
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print(ner_results) |
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``` |
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## Questions? |
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Post a Github issue on the [IcelandicNER Issues](https://github.com/grammatek/icelandic-ner/issues) repo. |
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