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precision recall f1-score support
Lojistik 0.83 0.86 0.84 22
Elektrik Kaynagi 0.71 0.95 0.81 39
Arama Ekipmani 0.72 0.80 0.76 82
Cenaze 0.50 0.33 0.40 3
Giysi 0.79 0.96 0.87 91
Enkaz Kaldirma 0.99 0.95 0.97 601
Isinma 0.75 0.90 0.82 112
Barınma 0.98 0.95 0.96 292
Tuvalet 0.83 1.00 0.91 5
Su 0.80 0.85 0.83 39
Yemek 0.94 0.95 0.94 138
Saglik 0.80 0.85 0.83 75
micro avg 0.90 0.93 0.92 1499
macro avg 0.80 0.86 0.83 1499
weighted avg 0.91 0.93 0.92 1499
samples avg 0.94 0.95 0.94 1499
1TrainingArguments(
2 fp16=True,
3 evaluation_strategy = "steps",
4 save_strategy = "steps",
5 learning_rate=5.1058553791201954e-05,
6 per_device_train_batch_size=batch_size,
7 per_device_eval_batch_size=batch_size*2,
8 num_train_epochs=4,
9 load_best_model_at_end=True,
10 metric_for_best_model="macro f1",
11 logging_steps = step_size,
12 seed = 42,
13 data_seed = 42,
14 dataloader_num_workers = 0,
15 lr_scheduler_type ="linear",
16 warmup_steps=0,
17 weight_decay=0.06437697487126866,
18 full_determinism = True,
19 group_by_length = True
20)