IcelandicNER BERT
This model was fine-tuned on the MIM-GOLD-NER dataset for the Icelandic language. The MIM-GOLD-NER corpus was developed at Reykjavik University 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
pip install transformers
How to predict using pipeline
from transformers import AutoTokenizer
from transformers import AutoModelForTokenClassification # for pytorch
from transformers import TFAutoModelForTokenClassification # for tensorflow
from transformers import pipeline
model_name_or_path = "grammatek/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 repo.