---
language: tr
datasets:
- SUNLP-NER-Twitter
---

# 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

```python
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:
You can cite the following [paper](http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.484.pdf), if you use this dataset:

```bibtex
@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}
}
```