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abusive_content_identification – AI Model by rahmas | AlphaNeural AI
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abusive_content_identification
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transformers
pytorch
tensorboard
bert
text-classification
generated_from_trainer
apache-2.0
autotrain_compatible
endpoints_compatible
us
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abusive_content_identification
This model is a fine-tuned version of
indolem/indobertweet-base-uncased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.0073
Accuracy: 0.9982
Precision: 0.9963
Recall: 1.0
F1: 0.9981
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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
Precision
Recall
F1
0.0666
1.0
547
0.0149
0.9973
0.9944
1.0
0.9972
0.0086
2.0
1094
0.0073
0.9982
0.9963
1.0
0.9981
Framework versions
Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3
