Text Classification
Transformers
PyTorch
Turkish
bert
deprem-clf-v13
Eval Results (legacy)
text-embeddings-inference
Instructions to use deprem-ml/deprem_bert_128k_v13_beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deprem-ml/deprem_bert_128k_v13_beta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="deprem-ml/deprem_bert_128k_v13_beta")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("deprem-ml/deprem_bert_128k_v13_beta") model = AutoModelForSequenceClassification.from_pretrained("deprem-ml/deprem_bert_128k_v13_beta") - Notebooks
- Google Colab
- Kaggle
Eval Results
precision recall f1-score support
Lojistik 0.81 0.79 0.80 38
Elektrik Kaynagi 0.73 0.91 0.81 56
Arama Ekipmani 0.79 0.74 0.76 128
Cenaze 1.00 0.50 0.67 2
Giysi 0.82 0.96 0.89 138
Enkaz Kaldirma 0.94 0.94 0.94 919
Isinma 0.84 0.89 0.86 185
Barınma 0.96 0.96 0.96 483
Tuvalet 0.67 0.80 0.73 10
Su 0.83 0.87 0.85 67
Yemek 0.89 0.96 0.92 202
Saglik 0.80 0.88 0.83 104
Alakasiz 0.90 0.82 0.86 377
micro avg 0.89 0.91 0.90 2709
macro avg 0.84 0.85 0.84 2709
weighted avg 0.90 0.91 0.90 2709
samples avg 0.91 0.92 0.91 2709
Threshold:
- Best Threshold: 0.53
Class Loss Weights
[3.017203135650159,
2.4823691788825464,
1.941736822154725,
6.172646581418988,
1.8759436445637834,
1.0,
1.75011143349181,
1.2730236191357969,
4.849237178079731,
2.4857419672410703,
1.6324480531290084,
2.0033774839735035,
1.3688883733394182]
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Evaluation results
- recall on deprem_private_dataset_v13self-reported0.850
- f1 on deprem_private_dataset_v13self-reported0.840