Instructions to use choiruzzia/best_berita_roberta_model_fold_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use choiruzzia/best_berita_roberta_model_fold_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="choiruzzia/best_berita_roberta_model_fold_1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("choiruzzia/best_berita_roberta_model_fold_1") model = AutoModelForSequenceClassification.from_pretrained("choiruzzia/best_berita_roberta_model_fold_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
best_berita_roberta_model_fold_1
This model is a fine-tuned version of ayameRushia/roberta-base-indonesian-sentiment-analysis-smsa on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0964
- Accuracy: 0.9817
- Precision: 0.9812
- Recall: 0.9826
- F1: 0.9818
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.6034 | 1.0 | 601 | 0.5137 | 0.8785 | 0.9043 | 0.8684 | 0.8758 |
| 0.2584 | 2.0 | 1202 | 0.2616 | 0.9434 | 0.9438 | 0.9489 | 0.9435 |
| 0.1286 | 3.0 | 1803 | 0.2470 | 0.9642 | 0.9645 | 0.9668 | 0.9646 |
| 0.0948 | 4.0 | 2404 | 0.0964 | 0.9817 | 0.9812 | 0.9826 | 0.9818 |
| 0.0405 | 5.0 | 3005 | 0.3506 | 0.9551 | 0.9560 | 0.9593 | 0.9558 |
| 0.0038 | 6.0 | 3606 | 0.3762 | 0.9551 | 0.9549 | 0.9597 | 0.9551 |
| 0.0105 | 7.0 | 4207 | 0.2738 | 0.9667 | 0.9657 | 0.9698 | 0.9666 |
| 0.0004 | 8.0 | 4808 | 0.2319 | 0.9742 | 0.9732 | 0.9765 | 0.9743 |
| 0.0018 | 9.0 | 5409 | 0.2551 | 0.9725 | 0.9715 | 0.9750 | 0.9726 |
| 0.0 | 10.0 | 6010 | 0.2604 | 0.9725 | 0.9715 | 0.9750 | 0.9726 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1
- Downloads last month
- 4