Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Apoksk1/convbert-base-turkish-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Apoksk1/convbert-base-turkish-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Apoksk1/convbert-base-turkish-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Apoksk1/convbert-base-turkish-cased") model = AutoModelForSequenceClassification.from_pretrained("Apoksk1/convbert-base-turkish-cased") - Notebooks
- Google Colab
- Kaggle
convbert-base-turkish-cased
This model is a fine-tuned version of dbmdz/convbert-base-turkish-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0066
- Accuracy: 0.5121
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3662 | 1.0 | 10254 | 0.3786 | 0.8719 |
| 1.008 | 2.0 | 20508 | 1.0064 | 0.5121 |
| 1.0066 | 3.0 | 30762 | 1.0073 | 0.5121 |
| 1.0092 | 4.0 | 41016 | 1.0069 | 0.5121 |
| 1.0062 | 5.0 | 51270 | 1.0066 | 0.5121 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
- 8
Model tree for Apoksk1/convbert-base-turkish-cased
Base model
dbmdz/convbert-base-turkish-cased