Instructions to use hiseulgi/stunting-berita-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hiseulgi/stunting-berita-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hiseulgi/stunting-berita-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hiseulgi/stunting-berita-sentiment") model = AutoModelForSequenceClassification.from_pretrained("hiseulgi/stunting-berita-sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
stunting-berita-sentiment
This model is a fine-tuned version of hiseulgi/stunting-berita-sentiment on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3918
- Accuracy: 0.9542
- Precision: 0.9352
- Recall: 0.9004
- F1: 0.9169
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.4224 | 1.0 | 751 | 0.2728 | 0.9442 | 0.9145 | 0.8601 | 0.8826 |
| 0.0762 | 2.0 | 1502 | 0.4584 | 0.9385 | 0.9547 | 0.8248 | 0.8790 |
| 0.039 | 3.0 | 2253 | 0.3609 | 0.9528 | 0.9566 | 0.8739 | 0.9103 |
| 0.0075 | 4.0 | 3004 | 0.3227 | 0.9628 | 0.9249 | 0.9471 | 0.9357 |
| 0.0016 | 5.0 | 3755 | 0.3918 | 0.9542 | 0.9352 | 0.9004 | 0.9169 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
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