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ArBERT-finetuned-fnd – AI Model by lamaabdulaziz | AlphaNeural AI
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ArBERT-finetuned-fnd
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transformers
pytorch
tensorboard
bert
text-classification
generated_from_trainer
autotrain_compatible
endpoints_compatible
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ArBERT-finetuned-fnd
This model is a fine-tuned version of
UBC-NLP/ARBERT
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.4896
Macro F1: 0.7637
Accuracy: 0.7738
Precision: 0.7695
Recall: 0.7604
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: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 25
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5
Training results
Training Loss
Epoch
Step
Validation Loss
Macro F1
Accuracy
Precision
Recall
0.5031
1.0
1597
0.4754
0.7547
0.7606
0.7538
0.7559
0.3832
2.0
3194
0.4896
0.7637
0.7738
0.7695
0.7604
0.2571
3.0
4791
0.5890
0.7605
0.7692
0.7634
0.7585
Framework versions
Transformers 4.20.1
Pytorch 1.11.0
Datasets 2.1.0
Tokenizers 0.12.1
