README.md

metadata

license: apache-2.0
base_model: indolem/indobertweet-base-uncased
tags:
  - generated_from_keras_callback
model-index:
  - name: damand2061/innermore-x-indobertweet-base-uncased
    results: []

damand2061/innermore-x-indobertweet-base-uncased

This model is a fine-tuned version of indolem/indobertweet-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.0022
• Validation Loss: 0.1782
• Train Precision: 0.8152
• Train Recall: 0.7350
• Train F1: 0.7730
• Train Accuracy: 0.9629
• Epoch: 14

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:

• optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0002, 'decay_steps': 420, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
• training_precision: float32

Training results

Train Loss	Validation Loss	Train Precision	Train Recall	Train F1	Train Accuracy	Epoch
0.5919	0.3227	0.6263	0.2650	0.3724	0.9078	0
0.2379	0.1878	0.6948	0.6325	0.6622	0.9434	1
0.1314	0.1674	0.6711	0.6453	0.6580	0.9477	2
0.0852	0.1958	0.6562	0.7179	0.6857	0.9425	3
0.0506	0.1677	0.7907	0.7265	0.7572	0.9539	4
0.0239	0.1493	0.7689	0.7393	0.7538	0.9615	5
0.0194	0.1679	0.8102	0.7479	0.7778	0.9610	6
0.0122	0.1739	0.7328	0.7265	0.7296	0.9563	7
0.0084	0.2116	0.8118	0.6453	0.7190	0.9539	8
0.0059	0.1724	0.8	0.7179	0.7568	0.9591	9
0.0037	0.1744	0.7972	0.7222	0.7578	0.9601	10
0.0029	0.1771	0.7981	0.7265	0.7606	0.9601	11
0.0020	0.1769	0.8047	0.7393	0.7706	0.9620	12
0.0020	0.1773	0.8152	0.7350	0.7730	0.9629	13
0.0022	0.1782	0.8152	0.7350	0.7730	0.9629	14

Framework versions

• Transformers 4.38.2
• TensorFlow 2.15.0
• Datasets 2.18.0
• Tokenizers 0.15.2