Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use oglcnarbc/turkish-case-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oglcnarbc/turkish-case-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="oglcnarbc/turkish-case-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("oglcnarbc/turkish-case-classifier") model = AutoModelForSequenceClassification.from_pretrained("oglcnarbc/turkish-case-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from oglcnarbc/turkish-case-classifier: direct link, hf CLI and curl.
- Browser
- Download file 1.89 kB
-
https://huggingface.co/oglcnarbc/turkish-case-classifier/resolve/main/README.md
- Command line
-
hf download hf://oglcnarbc/turkish-case-classifier/README.md
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curl -L -o README.md https://huggingface.co/oglcnarbc/turkish-case-classifier/resolve/main/README.md
1.89 kB
metadata
library_name: transformers
license: mit
base_model: dbmdz/bert-base-turkish-cased
tags:
- generated_from_trainer
model-index:
- name: turkish-case-classifier
results: []
turkish-case-classifier
This model is a fine-tuned version of dbmdz/bert-base-turkish-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.1595
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 3 | 2.2736 |
| No log | 2.0 | 6 | 2.2883 |
| No log | 3.0 | 9 | 2.2578 |
| No log | 4.0 | 12 | 2.2235 |
| No log | 5.0 | 15 | 2.2047 |
| No log | 6.0 | 18 | 2.1906 |
| No log | 7.0 | 21 | 2.1771 |
| No log | 8.0 | 24 | 2.1677 |
| No log | 9.0 | 27 | 2.1624 |
| No log | 10.0 | 30 | 2.1595 |
Framework versions
- Transformers 4.48.0
- Pytorch 2.7.0
- Datasets 3.2.0
- Tokenizers 0.21.1