
---
language:
- tr
tags:
- translation
license: apache-2.0
---

## About the model
It has been trained with 15451 real job advertisement data.

Included classes;

- Uygun İlan
- Is Ilani Degil
- Mustehcen
- Cift Pozisyon


Accordingly, the success rates in education are as follows;

- **Model is Turkish bert-based.**
- **Used StratifiedKFold(5) for validation.**
- results [0.806858621805241, 0.8912621359223301, 0.9440129449838188, 0.9750809061488673, 0.9851132686084142]
  
Mean-Precision: 0.9204655754937342


|        | Uygun İlan | Is Ilani Degil | Mustehcen | Cift Pozisyon | 
| ------ | ------  | ------ | ------  | ------ |
| Precision | 0.986 | 0.996 | 0.966 | 0.970 |
| Recall  | 0.992 | 0.986 | 0.966 | 0.959 |
| F1 Score | 0.989 | 0.991 | 0.966 | 0.965 |
Accuracy : 0.975

## Example

**!IMPORTANT_HINT: The sentence given to pipe must not contain Turkish characters.**

```sh
from transformers import AutoTokenizer, TextClassificationPipeline, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("nanelimon/bert-base-turkish-job-advertisement")
model = AutoModelForSequenceClassification.from_pretrained("nanelimon/bert-base-turkish-job-advertisement")
pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer)


def set_sentence(sentence: str):
    result = sentence.lower().replace('ö', 'o').replace('ı', 'i').replace('ü', 'u').replace('ç', 'c').replace('ğ', 'g').replace('ş', 's')
    return result


print(pipe(set_sentence('Fiziği düzgün 17 yaş kızlar aranıyor')))

```
Result;
```sh
output: [{'label': 'Mustehcen', 'score': 0.9992677569389343}]
```
- label= It shows which class the sent Turkish text belongs to according to the model.
- score= It shows the compliance rate of the Turkish text sent to the label found.

## Authors
- Seyma SARIGIL: seymasargil@gmail.com
- Murat KOKLU: mkoklu@selcuk.edu.tr


## License

apache-2.0

**Free Software, Hell Yeah!**
{"language": ["tr"], "license": "apache-2.0", "tags": ["job advertisement", "turkish bert", "bert-based", "StratifiedKFold"], "pipeline_tag": "text-classification"}
transformers pytorch bert text-classification job advertisement turkish bert bert-based StratifiedKFold tr license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #job advertisement #turkish bert #bert-based #StratifiedKFold #tr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 



---


language:


* tr
tags:
* translation
license: apache-2.0




---


About the model
---------------


It has been trained with 15451 real job advertisement data.


Included classes;


* Uygun İlan
* Is Ilani Degil
* Mustehcen
* Cift Pozisyon


Accordingly, the success rates in education are as follows;


* Model is Turkish bert-based.
* Used StratifiedKFold(5) for validation.
* results [0.806858621805241, 0.8912621359223301, 0.9440129449838188, 0.9750809061488673, 0.9851132686084142]


Mean-Precision: 0.9204655754937342



Example
-------


!IMPORTANT\_HINT: The sentence given to pipe must not contain Turkish characters.


Result;


* label= It shows which class the sent Turkish text belongs to according to the model.
* score= It shows the compliance rate of the Turkish text sent to the label found.


Authors
-------


* Seyma SARIGIL: seymasargil@URL
* Murat KOKLU: mkoklu@URL


License
-------


apache-2.0


Free Software, Hell Yeah!



TAGS
#transformers #pytorch #bert #text-classification #job advertisement #turkish bert #bert-based #StratifiedKFold #tr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #bert #text-classification #job advertisement #turkish bert #bert-based #StratifiedKFold #tr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
