berturk-sunlp-ner-turkish
Introduction
[berturk-sunlp-ner-turkish] is a NER model that was fine-tuned from the BERTurk-cased model on the SUNLP-NER-Twitter dataset.
Training data
The model was trained on the SUNLP-NER-Twitter dataset (5000 tweets). The dataset can be found at https://github.com/SU-NLP/SUNLP-Twitter-NER-Dataset Named entity types are as follows: Person, Location, Organization, Time, Money, Product, TV-Show
How to use berturk-sunlp-ner-turkish with HuggingFace
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("busecarik/berturk-sunlp-ner-turkish")
model = AutoModelForTokenClassification.from_pretrained("busecarik/berturk-sunlp-ner-turkish")
Model performances on SUNLP-NER-Twitter test set (metric: seqeval)
| Precision | Recall | F1 |
|---|---|---|
| 82.96 | 82.42 | 82.69 |
Classification Report
| Entity | Precision | Recall | F1 |
|---|---|---|---|
| LOCATION | 0.70 | 0.80 | 0.74 |
| MONEY | 0.80 | 0.71 | 0.75 |
| ORGANIZATION | 0.78 | 0.86 | 0.78 |
| PERSON | 0.90 | 0.91 | 0.91 |
| PRODUCT | 0.44 | 0.47 | 0.45 |
| TIME | 0.94 | 0.85 | 0.89 |
| TVSHOW | 0.61 | 0.35 | 0.45 |
You can cite the following paper, if you use this model:
@InProceedings{ark-yeniterzi:2022:LREC,
author = {\c{C}ar\i k, Buse and Yeniterzi, Reyyan},
title = {A Twitter Corpus for Named Entity Recognition in Turkish},
booktitle = {Proceedings of the Language Resources and Evaluation Conference},
month = {June},
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {4546--4551},
url = {https://aclanthology.org/2022.lrec-1.484}
}