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---
license: mit
language:
- tr
metrics:
- accuracy
- f1
- precision
- recall
library_name: transformers
pipeline_tag: text-classification
---
# About the Bert-Bank-Model
It is a Turkish BERT-based model that will analyze people's bank complaints and classify them according to one of eight categories. The classification headings are as follows:
- Diğer
- Hesap İşlemleri
- KKB Skor
- Kargo
- Kart İşlemleri
- Kredi İşlemleri
- Limit
- Müşteri Temsilcisi
246412 thousand complaints were used in model training. The success rates in education are as follows.
| | Kart İşlemleri | Kredi İşlemleri | Hesap İşlemleri | Kargo | Limit | Müşteri Temsilcisi | KKB Skor | accuracy |
| ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ |
| Precision | 0.977292 | 0.971119 | 0.985294 | 0.953096 | 0.98616 | 0.989115 | 0.991824 | 0.982336 |
| Recall | 0.978114 | 0.960714 | 0.985294 | 0.986348 | 0.978590 | 0.982224 | 0.992679 | 0.982336 |
| F1 Score | 0.977703 | 0.965889 | 0.985294 | 0.969437 | 0.983577 | 0.985657 | 0.992251 | 0.982336 |
# Example
```sh
from transformers import AutoTokenizer, TextClassificationPipeline, TFBertForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("elifftosunn/Bert-Bank-Model")
model = TFBertForSequenceClassification.from_pretrained("elifftosunn/Bert-Bank-Model", from_pt=True)
pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer)
print(pipe('QNB Finansbank 1.39 oranlı 50.000 TL yeni müşterilere özel ihtiyaç kredisi 1.92 oranında veriyor amaç hesap açtırmak kampanyanın hiçbir gerçekçiliği yoktur. Resmen milletle dalga geçiyorsunuz. Ne demek oluyor bu. 1,39 dan kredi deyip içeriğine girince 2 katına çıkıyor. Böyle saçma bir banka'))
```
# Result
```sh
[{'label': 'Kredi İşlemleri', 'score': 0.9589990377426147}]
```
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