digo-prayudha/Indonesian_sentiment
This model is a fine-tuned version of distilbert-base-uncased on sepidmnorozy/Indonesian_sentiment. It achieves the following results on the evaluation set:
- Train Loss: 0.1678
- Validation Loss: 0.2402
- Train Accuracy: 0.9016
- Epoch: 2
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2475, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
Training results
Train Loss | Validation Loss | Train Accuracy | Epoch |
---|---|---|---|
0.4013 | 0.3141 | 0.8667 | 0 |
0.2526 | 0.2923 | 0.8839 | 1 |
0.1678 | 0.2402 | 0.9016 | 2 |
How to use this model in Transformers Library
from transformers import pipeline
model = pipeline("text-classification",model="digo-prayudha/Indonesian_sentiment")
model("Makanannya Enak sekali!")
Framework versions
- Transformers 4.35.2
- TensorFlow 2.14.0
- Datasets 2.15.0
- Tokenizers 0.15.0
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