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bert-drug-review-to-condition – AI Model by Farzam123 | AlphaNeural AI
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Farzam123
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bert-drug-review-to-condition
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transformers
tensorboard
safetensors
bert
text-classification
generated_from_trainer
google-bert/bert-base-uncased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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bert-drug-review-to-condition
This model is a fine-tuned version of
bert-base-uncased
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.2619
Accuracy: 0.9497
Precision: 0.9403
Recall: 0.9497
F1: 0.9447
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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3.0
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
Precision
Recall
F1
0.6346
1.0
759
0.3411
0.9279
0.9124
0.9279
0.9187
0.1676
2.0
1518
0.2740
0.9457
0.9341
0.9457
0.9395
0.1021
3.0
2277
0.2619
0.9497
0.9403
0.9497
0.9447
Framework versions
Transformers 4.53.1
Pytorch 2.6.0+cu124
Datasets 4.0.0
Tokenizers 0.21.2
