Text Classification
Transformers
PyTorch
TensorBoard
Turkish
bert
deprem-clf-v1
Eval Results (legacy)
Instructions to use deprem-ml/deprem-loodos-bert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deprem-ml/deprem-loodos-bert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="deprem-ml/deprem-loodos-bert-base-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("deprem-ml/deprem-loodos-bert-base-uncased") model = AutoModelForSequenceClassification.from_pretrained("deprem-ml/deprem-loodos-bert-base-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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---
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license: apache-2.0
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---
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### Deprem NER Training Results
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license: apache-2.0
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language:
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- tr
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tags:
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- deprem-clf-v1
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metrics:
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- accuracy
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- recall
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- f1
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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: 0.8
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verified: false
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- type: f1
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value: 0.75
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verified: false
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---
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### Deprem NER Training Results
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