**Train-Test Set:** "intent-multilabel-v1-2.zip"

**Model:** "dbmdz/bert-base-turkish-cased"

## Tokenizer Params
```
max_length=128
padding="max_length"
truncation=True
```

## Training Params
```
evaluation_strategy = "epoch"
save_strategy = "epoch"
per_device_train_batch_size = 16
per_device_eval_batch_size = 16
num_train_epochs = 4
load_best_model_at_end = True
```

## Train-Val Splitting Configuration
```
train_test_split(df_train,
                 test_size=0.1,
                 random_state=1111)
```

## Training Log
```
Epoch	Training Loss	Validation Loss
1	    No log	        0.150276
2	    0.195100	    0.132906
3	    0.107700	    0.128633
4	    0.107700	    0.127795
```

## Threshold Optimization
- **Best Threshold:** 0.1
- **F1 @ Threshold:** 0.734

## Eval Results
```
              precision    recall  f1-score   support

    Alakasiz       0.90      0.87      0.89       734
     Barinma       0.85      0.80      0.83       207
  Elektronik       0.73      0.78      0.75       130
       Giysi       0.83      0.66      0.73        94
    Kurtarma       0.86      0.79      0.82       362
    Lojistik       0.73      0.51      0.60       112
      Saglik       0.74      0.74      0.74       108
          Su       0.64      0.60      0.62        78
       Yagma       0.68      0.55      0.61        31
       Yemek       0.80      0.83      0.81       117

   micro avg       0.84      0.79      0.81      1973
   macro avg       0.78      0.71      0.74      1973
weighted avg       0.84      0.79      0.81      1973
 samples avg       0.84      0.82      0.82      1973
```
{"language": ["tr"], "license": "apache-2.0", "library_name": "transformers", "metrics": ["accuracy"], "pipeline_tag": "text-classification", "widget": [{"text": "HATAY DEFNE \u0130L\u00c7ES\u0130NE yard\u0131mlar gitmiyor \u00d6zellikle \u00e7ad\u0131ra battaniyeye yiyece\u011fe ihtiya\u00e7 var. Antakyan\u0131n d\u0131\u015f\u0131nda oldu\u011fu i\u00e7in t\u00fcm yardimlar  \u0130SKENDERUNA ANTAKYAYA gidiyor. Bu b\u00f6lgeye gitmiyor..DEFNE \u0130L\u00c7ES\u0130NE G\u0130DECEK ERZAK \u00c7ad\u0131r yard\u0131mlar\u0131n\u0131", "example_title": "\u00d6rnek"}]}
transformers pytorch bert text-classification tr license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #tr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

Train-Test Set: "URL"

Model: "dbmdz/bert-base-turkish-cased"

## Tokenizer Params


## Training Params


## Train-Val Splitting Configuration


## Training Log


## Threshold Optimization
- Best Threshold: 0.1
- F1 @ Threshold: 0.734

## Eval Results

## Tokenizer Params ## Training Params ## Train-Val Splitting Configuration ## Training Log ## Threshold Optimization
- Best Threshold: 0.1
- F1 @ Threshold: 0.734 ## Eval Results
TAGS
#transformers #pytorch #bert #text-classification #tr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ## Tokenizer Params ## Training Params ## Train-Val Splitting Configuration ## Training Log ## Threshold Optimization
- Best Threshold: 0.1
- F1 @ Threshold: 0.734 ## Eval Results
TAGS
#transformers #pytorch #bert #text-classification #tr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
## Tokenizer Params## Training Params## Train-Val Splitting Configuration## Training Log## Threshold Optimization
- Best Threshold: 0.1
- F1 @ Threshold: 0.734## Eval Results