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bert-qlora-finetuning – AI Model by deepakkr | AlphaNeural AI
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deepakkr
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bert-qlora-finetuning
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peft
safetensors
generated_from_trainer
google-bert/bert-base-uncased
adapter
apache-2.0
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bert-qlora-finetuning
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: 0.2751
Accuracy: 0.8935
F1: 0.8934
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: 0.0002
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1
0.2693
1.0
250
0.2751
0.8935
0.8934
Framework versions
PEFT 0.11.1
Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1
