Instructions to use risazhrn/indobert-hotel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use risazhrn/indobert-hotel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="risazhrn/indobert-hotel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("risazhrn/indobert-hotel") model = AutoModelForSequenceClassification.from_pretrained("risazhrn/indobert-hotel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
indobert-hotel
This model is a fine-tuned version of indobenchmark/indobert-base-p1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4736
- Accuracy: 0.9536
- Macro F1: 0.9002
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: 32
- 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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
|---|---|---|---|---|---|
| 0.3092 | 1.0 | 550 | 0.2970 | 0.9281 | 0.8635 |
| 0.1862 | 2.0 | 1100 | 0.4181 | 0.9545 | 0.9007 |
| 0.1354 | 3.0 | 1650 | 0.4773 | 0.9581 | 0.9092 |
| 0.1109 | 4.0 | 2200 | 0.5677 | 0.9572 | 0.9053 |
Framework versions
- Transformers 5.3.0
- Pytorch 2.10.0+cu128
- Datasets 4.6.1
- Tokenizers 0.22.2
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Model tree for risazhrn/indobert-hotel
Base model
indobenchmark/indobert-base-p1