Instructions to use hiseulgi/stunting-medsos-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hiseulgi/stunting-medsos-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hiseulgi/stunting-medsos-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hiseulgi/stunting-medsos-sentiment") model = AutoModelForSequenceClassification.from_pretrained("hiseulgi/stunting-medsos-sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
stunting-medsos-sentiment
This model is a fine-tuned version of ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9685
- Accuracy: 0.8360
- Precision: 0.7971
- Recall: 0.7985
- F1: 0.7976
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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 314 | 0.5221 | 0.8105 | 0.7868 | 0.7345 | 0.7517 |
| 0.5607 | 2.0 | 628 | 0.6322 | 0.8296 | 0.7991 | 0.7670 | 0.7794 |
| 0.5607 | 3.0 | 942 | 0.9147 | 0.8201 | 0.7929 | 0.7655 | 0.7731 |
| 0.2289 | 4.0 | 1256 | 0.9736 | 0.8296 | 0.7929 | 0.7990 | 0.7957 |
| 0.0597 | 5.0 | 1570 | 0.9685 | 0.8360 | 0.7971 | 0.7985 | 0.7976 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
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