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results – AI Model by adity12345 | AlphaNeural AI
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peft
safetensors
adapter
lora
transformers
FacebookAI/roberta-base
adapter
mit
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results
This model is a fine-tuned version of
roberta-base
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.2789
Accuracy: 0.9566
Precision: 0.9697
Recall: 0.9598
F1: 0.9648
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: 16
eval_batch_size: 64
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
label_smoothing_factor: 0.1
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
Precision
Recall
F1
0.3138
1.0
7515
0.2915
0.9461
0.9716
0.9403
0.9557
0.3075
2.0
15030
0.2825
0.9541
0.9720
0.9533
0.9626
0.2485
3.0
22545
0.2789
0.9566
0.9697
0.9598
0.9648
Framework versions
PEFT 0.16.0
Transformers 4.53.3
Pytorch 2.6.0+cu124
Datasets 4.0.0
Tokenizers 0.21.2
