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--- |
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license: apache-2.0 |
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language: |
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- tr |
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metrics: |
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- accuracy |
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- recall |
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- f1 |
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tags: |
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- deprem-clf-v1 |
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library_name: transformers |
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pipeline_tag: text-classification |
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model-index: |
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- name: deprem_v12 |
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results: |
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- task: |
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type: text-classification |
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dataset: |
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type: deprem_private_dataset_v1_2 |
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name: deprem_private_dataset_v1_2 |
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metrics: |
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- type: recall |
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value: 0.82 |
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verified: false |
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- type: f1 |
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value: 0.76 |
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verified: false |
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widget: |
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- text: >- |
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acil acil acil antakyadan istanbula gitmek için antakya expoya ulaşmaya çalışan 19 kişilik bir aile için şehir içi ulaşım desteği istiyoruz. dışardalar üşüyorlar.iletebileceğiniz numaraları bekliyorum |
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example_title: Örnek |
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--- |
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## Eval Results |
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``` |
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precision recall f1-score support |
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Alakasiz 0.87 0.91 0.89 734 |
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Barinma 0.79 0.89 0.84 207 |
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Elektronik 0.69 0.83 0.75 130 |
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Giysi 0.71 0.81 0.76 94 |
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Kurtarma 0.82 0.85 0.83 362 |
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Lojistik 0.57 0.67 0.62 112 |
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Saglik 0.68 0.85 0.75 108 |
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Su 0.56 0.76 0.64 78 |
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Yagma 0.60 0.77 0.68 31 |
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Yemek 0.71 0.89 0.79 117 |
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micro avg 0.77 0.86 0.81 1973 |
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macro avg 0.70 0.82 0.76 1973 |
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weighted avg 0.78 0.86 0.82 1973 |
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samples avg 0.83 0.88 0.84 1973 |
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``` |
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## Training Params: |
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```python |
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{'per_device_train_batch_size': 32, |
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'per_device_eval_batch_size': 32, |
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'learning_rate': 5.8679699888213376e-05, |
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'weight_decay': 0.03530961718117487, |
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'num_train_epochs': 4, |
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'lr_scheduler_type': 'cosine', |
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'warmup_steps': 40, |
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'seed': 42, |
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'fp16': True, |
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'load_best_model_at_end': True, |
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'metric_for_best_model': 'macro f1', |
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'greater_is_better': True |
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} |
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``` |
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## Threshold: |
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- **Best Threshold:** 0.40 |
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## Class Loss Weights |
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- Same as Anıl's approach: |
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```python |
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[1.0, |
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1.5167249178108022, |
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1.7547338578655642, |
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1.9610520059358458, |
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1.8684086209021484, |
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1.8019018017117145, |
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2.110648663094536, |
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3.081208739200435, |
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1.7994815143101963] |
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``` |