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
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