Instructions to use deprem-ml/deprem-roberta-intent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deprem-ml/deprem-roberta-intent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="deprem-ml/deprem-roberta-intent")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("deprem-ml/deprem-roberta-intent", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,327 Bytes
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license: apache-2.0
language:
- tr
metrics:
- accuracy
library_name: transformers
pipeline_tag: text-classification
---
# Türkçe Multi-label Intent Classification RoBERTa
Depremzedelerin ihtiyaçlarını karşılamak için etiketlenmiş eğitilmiş multi-label RoBERTa modeli. Aşağıda değerlendirme sonuçları var.
**Evaluation**
{'eval_loss': 0.18568251545368838,
'eval_runtime': 2.7693,
'eval_samples_per_second': 254.935,
'eval_steps_per_second': 8.305,
'epoch': 3.0}
**Classification Report**
```
precision recall f1-score support
Alakasiz 0.95 0.87 0.91 781
Barinma 0.86 0.52 0.65 234
Elektronik 0.00 0.00 0.00 171
Giysi 0.89 0.25 0.39 122
Kurtarma 0.86 0.78 0.82 472
Lojistik 0.00 0.00 0.00 123
Saglik 0.78 0.05 0.09 148
Su 0.92 0.11 0.20 96
Yagma 0.00 0.00 0.00 19
Yemek 0.94 0.42 0.58 158
micro avg 0.91 0.55 0.69 2324
macro avg 0.62 0.30 0.36 2324
weighted avg 0.78 0.55 0.61 2324
samples avg 0.69 0.63 0.65 2324
``` |