Instructions to use fatihasarmusakci/convbert_sentiment_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fatihasarmusakci/convbert_sentiment_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fatihasarmusakci/convbert_sentiment_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fatihasarmusakci/convbert_sentiment_model") model = AutoModelForSequenceClassification.from_pretrained("fatihasarmusakci/convbert_sentiment_model") - Notebooks
- Google Colab
- Kaggle
convbert_sentiment_model
This model is a fine-tuned version of dbmdz/convbert-base-turkish-mc4-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1657
- Accuracy: 0.963
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: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1963 | 1.0 | 625 | 0.1545 | 0.957 |
| 0.1050 | 2.0 | 1250 | 0.1557 | 0.962 |
| 0.0677 | 3.0 | 1875 | 0.1657 | 0.963 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for fatihasarmusakci/convbert_sentiment_model
Base model
dbmdz/convbert-base-turkish-mc4-cased