icelandic-ner-bert / README.md
---
language: is
license: apache-2.0
widget:
- text: "Kristin manneskja getur ekki lagt frásagnir af Jesú Kristi á hilluna vegna þess að hún sé búin að lesa þær ."
- 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 ."
- 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 ."
- 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 ."
- text: "Britney gerði honum viðvart og hann ók henni á UCLA-sjúkrahúsið í Santa Monica en það er í nágrenni hljóðversins ."
---
# IcelandicNER BERT
This model was fine-tuned on the MIM-GOLD-NER dataset for the Icelandic language.
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:
- Date
- Location
- Miscellaneous
- Money
- Organization
- Percent
- Person
- Time
## Dataset Information
| | 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 |
|:------|----------:|---------:|-------------:|------------------:|----------:|-----------------:|------------:|-----------:|---------:|---------:|-------------:|------------------:|----------:|-----------------:|------------:|-----------:|---------:|
| Train | 39988 | 3409 | 5980 | 4351 | 729 | 5754 | 502 | 11719 | 868 | 2112 | 516 | 3036 | 770 | 2382 | 50 | 5478 | 790 |
| Valid | 7063 | 570 | 1034 | 787 | 100 | 1078 | 103 | 2106 | 147 | 409 | 76 | 560 | 104 | 458 | 7 | 998 | 136 |
| Test | 8299 | 779 | 1319 | 935 | 153 | 1315 | 108 | 2247 | 172 | 483 | 104 | 660 | 167 | 617 | 10 | 1089 | 158 |
## Evaluation
The following tables summarize the scores obtained by model overall and per each class.
| entity | precision | recall | f1-score | support |
|:-------------:|:---------:|:--------:|:--------:|:-------:|
| Date | 0.969466 | 0.978177 | 0.973802 | 779.0 |
| Location | 0.955201 | 0.953753 | 0.954476 | 1319.0 |
| Miscellaneous | 0.867033 | 0.843850 | 0.855285 | 935.0 |
| Money | 0.979730 | 0.947712 | 0.963455 | 153.0 |
| Organization | 0.893939 | 0.897338 | 0.895636 | 1315.0 |
| Percent | 1.000000 | 1.000000 | 1.000000 | 108.0 |
| Person | 0.963028 | 0.973743 | 0.968356 | 2247.0 |
| Time | 0.976879 | 0.982558 | 0.979710 | 172.0 |
| micro avg | 0.938158 | 0.938958 | 0.938558 | 7028.0 |
| macro avg | 0.950659 | 0.947141 | 0.948840 | 7028.0 |
| weighted avg | 0.937845 | 0.938958 | 0.938363 | 7028.0 |
## How To Use
You use this model with Transformers pipeline for NER.
### Installing requirements
```bash
pip install transformers
```
### How to predict using pipeline
```python
from transformers import AutoTokenizer
from transformers import AutoModelForTokenClassification # for pytorch
from transformers import TFAutoModelForTokenClassification # for tensorflow
from transformers import pipeline
model_name_or_path = "m3hrdadfi/icelandic-ner-bert"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForTokenClassification.from_pretrained(model_name_or_path) # Pytorch
# model = TFAutoModelForTokenClassification.from_pretrained(model_name_or_path) # Tensorflow
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Kristin manneskja getur ekki lagt frásagnir af Jesú Kristi á hilluna vegna þess að hún sé búin að lesa þær ."
ner_results = nlp(example)
print(ner_results)
```
## Questions?
Post a Github issue on the [IcelandicNER Issues](https://github.com/m3hrdadfi/icelandic-ner/issues) repo.