IcelandicNER DistilBERT

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 0.969309 0.973042 0.971172 779.0
Location 0.941221 0.946929 0.944067 1319.0
Miscellaneous 0.848283 0.819251 0.833515 935.0
Money 0.928571 0.934641 0.931596 153.0
Organization 0.874147 0.876806 0.875475 1315.0
Percent 1.000000 1.000000 1.000000 108.0
Person 0.956674 0.972853 0.964695 2247.0
Time 0.965318 0.970930 0.968116 172.0
micro avg 0.926110 0.929141 0.927623 7028.0
macro avg 0.935441 0.936807 0.936079 7028.0
weighted avg 0.925578 0.929141 0.927301 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 = "m3hrdadfi/icelandic-ner-distilbert" 
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.

New

Select AutoNLP in the “Train” menu to fine-tune this model automatically.

Downloads last month
2
Hosted inference API
Token Classification
Examples
Examples
This model can be loaded on the Inference API on-demand.