Instructions to use paaze/kobert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use paaze/kobert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="paaze/kobert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("paaze/kobert") model = AutoModelForSequenceClassification.from_pretrained("paaze/kobert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
kobert
This model is a fine-tuned version of skt/kobert-base-v1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6930
- Accuracy: 0.51
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_FUSED 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 | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 94 | 0.6932 | 0.49 |
| No log | 2.0 | 188 | 0.7036 | 0.51 |
| No log | 3.0 | 282 | 0.6940 | 0.502 |
| No log | 4.0 | 376 | 0.6939 | 0.49 |
| No log | 5.0 | 470 | 0.6964 | 0.49 |
| 0.6985 | 6.0 | 564 | 0.6930 | 0.51 |
| 0.6985 | 7.0 | 658 | 0.6958 | 0.49 |
| 0.6985 | 8.0 | 752 | 0.6945 | 0.49 |
| 0.6985 | 9.0 | 846 | 0.6937 | 0.49 |
| 0.6985 | 10.0 | 940 | 0.6930 | 0.51 |
Framework versions
- Transformers 5.17.0
- Pytorch 2.11.0+cu130
- Datasets 4.8.5
- Tokenizers 0.23.2
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Model tree for paaze/kobert
Base model
skt/kobert-base-v1