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# berturk-uncased-keyword-discriminator

This model is a fine-tuned version of [dbmdz/bert-base-turkish-uncased](https://huggingface.co/dbmdz/bert-base-turkish-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3989
- Precision: 0.6234
- Recall: 0.6508
- Accuracy: 0.9145
- F1: 0.6368
- Ent/precision: 0.6435
- Ent/accuracy: 0.7120
- Ent/f1: 0.6761
- Con/precision: 0.5834
- Con/accuracy: 0.5475
- Con/f1: 0.5649

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | Accuracy | F1     | Ent/precision | Ent/accuracy | Ent/f1 | Con/precision | Con/accuracy | Con/f1 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|:-------------:|:------------:|:------:|:-------------:|:------------:|:------:|
| 0.2005        | 1.0   | 1875  | 0.2104          | 0.5981    | 0.5978 | 0.9148   | 0.5979 | 0.6280        | 0.6665       | 0.6467 | 0.5383        | 0.4820       | 0.5086 |
| 0.1468        | 2.0   | 3750  | 0.2094          | 0.5996    | 0.6568 | 0.9164   | 0.6269 | 0.6285        | 0.7049       | 0.6645 | 0.5477        | 0.5757       | 0.5614 |
| 0.1124        | 3.0   | 5625  | 0.2372          | 0.6106    | 0.6679 | 0.9154   | 0.6380 | 0.6285        | 0.7270       | 0.6741 | 0.5753        | 0.5684       | 0.5718 |
| 0.0861        | 4.0   | 7500  | 0.2736          | 0.6133    | 0.6707 | 0.9145   | 0.6407 | 0.6281        | 0.7359       | 0.6777 | 0.5830        | 0.5606       | 0.5716 |
| 0.0644        | 5.0   | 9375  | 0.3081          | 0.6115    | 0.6683 | 0.9145   | 0.6386 | 0.6291        | 0.7293       | 0.6755 | 0.5764        | 0.5657       | 0.5710 |
| 0.0498        | 6.0   | 11250 | 0.3449          | 0.6245    | 0.6466 | 0.9149   | 0.6353 | 0.6380        | 0.7097       | 0.6720 | 0.5965        | 0.5401       | 0.5669 |
| 0.0401        | 7.0   | 13125 | 0.3838          | 0.6223    | 0.6545 | 0.9140   | 0.6380 | 0.6449        | 0.7100       | 0.6759 | 0.5790        | 0.5610       | 0.5699 |
| 0.0329        | 8.0   | 15000 | 0.3989          | 0.6234    | 0.6508 | 0.9145   | 0.6368 | 0.6435        | 0.7120       | 0.6761 | 0.5834        | 0.5475       | 0.5649 |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1

{"language": ["tr"], "license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "accuracy", "f1"], "widget": [{"text": "\u0130ngiltere'de d\u00fczenlenen Avrupa Tekvando ve Para Tekvando \u015eampiyonas\u0131\u2019nda mill\u00ee tekvandocular 5 alt\u0131n, 2 g\u00fcm\u00fc\u015f ve 4 bronz olmak \u00fczere 11, mill\u00ee para tekvandocular ise 4 alt\u0131n, 3 g\u00fcm\u00fc\u015f ve 1 bronz olmak \u00fczere 8 madalya kazanarak tak\u0131m halinde Avrupa \u015fampiyonu oldu."}, {"text": "F\u00fcme somon dedik ama asl\u0131nda lox salamuralanm\u0131\u015f somon anlam\u0131na geliyor, f\u00fcme etme opsiyonel. Lox bagel, 1930'larda Eggs Benedict furyas\u0131nda New Yorklu Yahudi cemaati taraf\u0131ndan ko\u015fer bir alternatif olarak \u00e7\u0131kan bir lezzet. G\u00fcn\u00fcm\u00fczde benim hangover y\u00fcre\u011fim d\u00e2hil d\u00fcnyan\u0131n bir\u00e7ok yerinde enfes bir kahvalt\u0131 sandvi\u00e7i."}, {"text": "T\u00fcrkiye'de son aylarda s\u0131kl\u0131kla tart\u0131\u015f\u0131lan konut sat\u0131\u015f\u0131 kar\u015f\u0131l\u0131\u011f\u0131nda yabanc\u0131lara vatanda\u015fl\u0131k verilmesi konusunu beyin g\u00f6\u00e7\u00fc kapsam\u0131nda ele almak m\u00fcmk\u00fcn. Daha \u00f6nce 250 bin dolar olan vatanda\u015fl\u0131k bedeli y\u00fckselen tepkiler \u00fczerine 400 bin dolara \u00e7\u0131kar\u0131lm\u0131\u015ft\u0131. T\u00fcrkiye'den g\u00f6\u00e7 eden iyi e\u011fitimli ki\u015filerin , gittikleri \u00fclkelerde 250 bin dolar tutar\u0131nda yabanc\u0131 yat\u0131r\u0131ma denk oldu\u011fu g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda nitelikli insan g\u00fcc\u00fcn\u00fcn yabanc\u0131lara konut kar\u015f\u0131l\u0131\u011f\u0131nda sat\u0131lan vatanda\u015fl\u0131k bedelin e\u015f oldu\u011funu g\u00f6r\u00fcyoruz. Yurt d\u0131\u015f\u0131na giden her bir vatanda\u015f\u0131n y\u00fcksek teknolojili katma de\u011fer \u00fcreten sekt\u00f6rlere yapaca\u011f\u0131 katk\u0131lar g\u00f6z \u00f6n\u00fcnde bulunduruldu\u011funda bu a\u00e7\u0131\u011f\u0131n in\u015faat sekt\u00f6r\u00fcyle kapat\u0131ld\u0131\u011f\u0131n\u0131 da g\u00f6r\u00fcyoruz. Beyin g\u00f6\u00e7\u00fc konusunda sadece ekonomik perspektiften bak\u0131ld\u0131\u011f\u0131nda bile k\u0131sa vadeli d\u00f6viz kayna\u011f\u0131 yaratmak i\u00e7in kullan\u0131lan vatanda\u015fl\u0131k sat\u0131\u015f\u0131 yerine beyin g\u00f6\u00e7\u00fcn\u00fc \u00f6nleyecek \u00f6nlemler al\u0131nmas\u0131n\u0131n \u00fclkemize \u00e7ok daha faydal\u0131 olaca\u011f\u0131 sonucunu \u00e7\u0131kar\u0131yoruz."}, {"text": "T\u00fcrkiye\u2019de resm\u00ee verilere g\u00f6re, 15 ve daha yukar\u0131 ya\u015ftaki ki\u015filerde mevsim etkisinden ar\u0131nd\u0131r\u0131lm\u0131\u015f i\u015fsiz say\u0131s\u0131, bu y\u0131l\u0131n ilk \u00e7eyre\u011finde bir \u00f6nceki \u00e7eyre\u011fe g\u00f6re 50 bin ki\u015fi artarak 3 milyon 845 bin ki\u015fi oldu. Mevsim etkisinden ar\u0131nd\u0131r\u0131lm\u0131\u015f i\u015fsizlik oran\u0131 ise 0,1 puanl\u0131k art\u0131\u015fla %11,4 seviyesinde ger\u00e7ekle\u015fti. \u0130\u015fsizlik oran\u0131, ilk \u00e7eyrekte ge\u00e7en y\u0131l\u0131n ayn\u0131 \u00e7eyre\u011fine g\u00f6re 1,7 puan azald\u0131."}, {"text": "Boeing\u2019in insans\u0131z uzay arac\u0131 Starliner, birtak\u0131m sorunlara ra\u011fmen Uluslararas\u0131 Uzay \u0130stasyonuna (ISS) ula\u015farak ilk kez ba\u015far\u0131l\u0131 bir \u015fekilde kenetlendi. Arac\u0131n ISS\u2019te be\u015f g\u00fcn kalmas\u0131n\u0131 takiben sorunsuz bir \u015fekilde New Mexico\u2019ya inmesi halinde Boeing, sonbaharda astronotlar\u0131 y\u00f6r\u00fcngeye g\u00f6ndermek i\u00e7in Starliner\u2019\u0131 kullanabilir.\n\nNeden \u00f6nemli? NASA\u2019n\u0131n personal arac\u0131 \u00fcretmeyi durdurmas\u0131ndan kaynakl\u0131 olarak g\u00f6revli astronotlar ve kozmonotlar, ISS\u2019te Rusya\u2019n\u0131n \u00fcretti\u011fi uzay ara\u00e7lar\u0131 ile ta\u015f\u0131n\u0131yordu. Starliner\u2019\u0131n kendini kan\u0131tlamas\u0131 ise bu konuda Rusya\u2019ya olan ba\u011f\u0131ml\u0131l\u0131\u011f\u0131n potansiyel olarak ortadan kalkabilece\u011fi anlam\u0131na geliyor."}], "model-index": [{"name": "berturk-uncased-keyword-discriminator", "results": []}]}
transformers pytorch bert token-classification generated_from_trainer tr license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #tr #license-mit #autotrain_compatible #endpoints_compatible #region-us 

berturk-uncased-keyword-discriminator
=====================================


This model is a fine-tuned version of dbmdz/bert-base-turkish-uncased on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3989
* Precision: 0.6234
* Recall: 0.6508
* Accuracy: 0.9145
* F1: 0.6368
* Ent/precision: 0.6435
* Ent/accuracy: 0.7120
* Ent/f1: 0.6761
* Con/precision: 0.5834
* Con/accuracy: 0.5475
* Con/f1: 0.5649


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #tr #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #tr #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1