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digo-prayudha/
Indonesian_sentiment

Name: Indonesian_sentiment
Availability: InStock

transformers

distilbert

text-classification

generated_from_keras_callback

sepidmnorozy/Indonesian_sentiment

distilbert/distilbert-base-uncased

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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

python

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