Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use aliiil02/bert-base-indonesian-1.5G-sentiment-analysis-smsa-tuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aliiil02/bert-base-indonesian-1.5G-sentiment-analysis-smsa-tuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aliiil02/bert-base-indonesian-1.5G-sentiment-analysis-smsa-tuning")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aliiil02/bert-base-indonesian-1.5G-sentiment-analysis-smsa-tuning") model = AutoModelForSequenceClassification.from_pretrained("aliiil02/bert-base-indonesian-1.5G-sentiment-analysis-smsa-tuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
output
This model is a fine-tuned version of ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7283
- Accuracy: 0.6978
- F1 Score: 0.6979
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: 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 | F1 Score |
|---|---|---|---|---|---|
| 0.4822 | 1.0 | 1952 | 0.7514 | 0.6989 | 0.7001 |
| 0.6036 | 2.0 | 3904 | 0.7232 | 0.7020 | 0.7030 |
| 0.6004 | 3.0 | 5856 | 0.7226 | 0.7020 | 0.7027 |
| 0.5904 | 4.0 | 7808 | 0.7260 | 0.7037 | 0.7046 |
| 0.5919 | 5.0 | 9760 | 0.7250 | 0.7039 | 0.7048 |
| 0.5939 | 6.0 | 11712 | 0.7260 | 0.7053 | 0.7060 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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