**Train-Test Set:**
- https://github.com/L2-Regulasyon/Teknofest2023/blob/main/data/raw/teknofest_train_final.csv
- https://github.com/L2-Regulasyon/Teknofest2023/blob/main/data/external/tweetset.csv

**Model:** "dbmdz/bert-base-turkish-128k-uncased"

**Önişleme**
- Karakterler küçültülmüştür
- Noktalama işaretleri silinmiştir
- Ek ofansif olmayan veri kullanılmıştır
- Ofansif olmayan cümlelerin uzunlukları ofansif olanlara uygun şekilde kırpılmıştır
  
## Tokenizer Parametreleri
```
max_length=64
padding=True
truncation=True
```

## Eğitim Parametreleri
- **Epoch:** 3
- **Learning Rate:** 7e-5
- **Batch-Size:** 64
- **Tokenizer Length:** 64
- **Loss:** BCE
- **Online Hard Example Mining:** Açık
- **Class-Weighting:** Açık (^0.3)
- **Early Stopping:** Kapalı
- **Stratified Batch Sampling:** Açık
- **Gradient Accumulation:** Kapalı
- **LR Scheduler:** Cosine-with-Warmup
- **Warmup Ratio:** 0.1
- **Weight Decay:** 0.01
- **LLRD:** 0.95
- **Label Smoothing:** 0.05
- **Gradient Clipping:** 1.0
- **MLM Pre-Training:** Kapalı


## CV10 Sonuçları
```
              precision    recall  f1-score   support

      INSULT     0.8940    0.8918    0.8929      2393
       OTHER     0.9319    0.9079    0.9197      3528
   PROFANITY     0.9626    0.9533    0.9579      2376
      RACIST     0.9317    0.9666    0.9488      2033
      SEXIST     0.9388    0.9587    0.9486      2081

    accuracy                         0.9316     12411
   macro avg     0.9318    0.9356    0.9336     12411
weighted avg     0.9316    0.9316    0.9315     12411
```
{}
transformers pytorch bert text-classification autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us 

Train-Test Set:
- URL
- URL

Model: "dbmdz/bert-base-turkish-128k-uncased"

Önişleme
- Karakterler küçültülmüştür
- Noktalama işaretleri silinmiştir
- Ek ofansif olmayan veri kullanılmıştır
- Ofansif olmayan cümlelerin uzunlukları ofansif olanlara uygun şekilde kırpılmıştır
  
## Tokenizer Parametreleri


## Eğitim Parametreleri
- Epoch: 3
- Learning Rate: 7e-5
- Batch-Size: 64
- Tokenizer Length: 64
- Loss: BCE
- Online Hard Example Mining: Açık
- Class-Weighting: Açık (^0.3)
- Early Stopping: Kapalı
- Stratified Batch Sampling: Açık
- Gradient Accumulation: Kapalı
- LR Scheduler: Cosine-with-Warmup
- Warmup Ratio: 0.1
- Weight Decay: 0.01
- LLRD: 0.95
- Label Smoothing: 0.05
- Gradient Clipping: 1.0
- MLM Pre-Training: Kapalı


## CV10 Sonuçları

## Tokenizer Parametreleri ## Eğitim Parametreleri
- Epoch: 3
- Learning Rate: 7e-5
- Batch-Size: 64
- Tokenizer Length: 64
- Loss: BCE
- Online Hard Example Mining: Açık
- Class-Weighting: Açık (^0.3)
- Early Stopping: Kapalı
- Stratified Batch Sampling: Açık
- Gradient Accumulation: Kapalı
- LR Scheduler: Cosine-with-Warmup
- Warmup Ratio: 0.1
- Weight Decay: 0.01
- LLRD: 0.95
- Label Smoothing: 0.05
- Gradient Clipping: 1.0
- MLM Pre-Training: Kapalı ## CV10 Sonuçları
TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ## Tokenizer Parametreleri ## Eğitim Parametreleri
- Epoch: 3
- Learning Rate: 7e-5
- Batch-Size: 64
- Tokenizer Length: 64
- Loss: BCE
- Online Hard Example Mining: Açık
- Class-Weighting: Açık (^0.3)
- Early Stopping: Kapalı
- Stratified Batch Sampling: Açık
- Gradient Accumulation: Kapalı
- LR Scheduler: Cosine-with-Warmup
- Warmup Ratio: 0.1
- Weight Decay: 0.01
- LLRD: 0.95
- Label Smoothing: 0.05
- Gradient Clipping: 1.0
- MLM Pre-Training: Kapalı ## CV10 Sonuçları
TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us 
## Tokenizer Parametreleri## Eğitim Parametreleri
- Epoch: 3
- Learning Rate: 7e-5
- Batch-Size: 64
- Tokenizer Length: 64
- Loss: BCE
- Online Hard Example Mining: Açık
- Class-Weighting: Açık (^0.3)
- Early Stopping: Kapalı
- Stratified Batch Sampling: Açık
- Gradient Accumulation: Kapalı
- LR Scheduler: Cosine-with-Warmup
- Warmup Ratio: 0.1
- Weight Decay: 0.01
- LLRD: 0.95
- Label Smoothing: 0.05
- Gradient Clipping: 1.0
- MLM Pre-Training: Kapalı## CV10 Sonuçları