---
license: mit
language:
- tr
metrics:
- accuracy
- f1
pipeline_tag: token-classification
tags:
- ner
- turkish-ner
- turkish
- nlp
---

Bu model "https://github.com/stefan-it/turkish-bert" base alınarak geliştirilmiş bir NER(Varlık ismi tanıma) modelidir.


## Eğitim ve validasyon verisi

Fine-tune işlemi için TDD-NER-202112-CC-002 veri seti kullanılmıştır.

@inproceedings{pan-etal-2017-cross,
    title = "Cross-lingual Name Tagging and Linking for 282 Languages",
    author = "Pan, Xiaoman  and
      Zhang, Boliang  and
      May, Jonathan  and
      Nothman, Joel  and
      Knight, Kevin  and
      Ji, Heng",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1178",
    doi = "10.18653/v1/P17-1178",
    pages = "1946--1958"
}


### Hiperparametreler

custom_labels = ["O","B-LOC","I-LOC","B-ORG","I-ORG","B-PER","I-PER"]

model_args = {
    "train_batch_size": 32,
    "eval_batch_size": 32,
    "num_train_epochs": 3,
    "seed":1,
    "save_steps": 625,
    "overwrite_output_dir": True,
    "output_dir": "/content/Model"
}

 ## Eğitim Metrikleri
 
| Epochs | Running Loss |
|--- |--- |
| 1| 0.1152 |
| 2| 0.1091 |
| 3| 0.0586 |

### Nasıl Kullanılacağı

```
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("token-classification", model="Gorengoz/bert-based-Turkish-NER-wikiann")
pipe("Entity X'in müşteri hizmetleri hızlı ve etkili, Entity Y'nin ürün kalitesi çok kötü.",aggregation_strategy = "simple"")

```