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IndoBERT-NER – AI Model by aadhistii | AlphaNeural AI
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aadhistii
/
IndoBERT-NER
like
0
transformers
tf
bert
token-classification
generated_from_keras_callback
id
id_nergrit_corpus
indolem/indobert-base-uncased
finetune
mit
autotrain_compatible
endpoints_compatible
us
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aadhistii/indobert-ner-model
This model is a fine-tuned version of
indolem/indobert-base-uncased
on dataset
id_nergrit_corpus
. It achieves the following results on the evaluation set:
Train Loss: 0.1471
Validation Loss: 0.1801
Train Precision: 0.8077
Train Recall: 0.8437
Train F1: 0.8253
Train Accuracy: 0.9471
Epoch: 2
Model description
Dataset Entities:
'CRD': Cardinal
'DAT': Date
'EVT': Event
'FAC': Facility
'GPE': Geopolitical Entity
'LAW': Law Entity (such as Undang-Undang)
'LOC': Location
'MON': Money
'NOR': Political Organization
'ORD': Ordinal
'ORG': Organization
'PER': Person
'PRC': Percent
'PRD': Product
'QTY': Quantity
'REG': Religion
'TIM': Time
'WOA': Work of Art
'LAN': Language
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': 2e-05, 'decay_steps': 2349, '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
0.5182
0.2042
0.7770
0.8146
0.7954
0.9395
0
0.1907
0.1810
0.8020
0.8344
0.8179
0.9469
1
0.1471
0.1801
0.8077
0.8437
0.8253
0.9471
2
Framework versions
Transformers 4.41.1
TensorFlow 2.15.0
Datasets 2.19.1
Tokenizers 0.19.1
