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distilbert-base-category-classifier – AI Model by junmeng-sf | AlphaNeural AI
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junmeng-sf
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distilbert-base-category-classifier
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peft
safetensors
adapter
lora
transformers
distilbert/distilbert-base-uncased
adapter
apache-2.0
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distilbert-base-category-classifier
This model is a fine-tuned version of
distilbert-base-uncased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.1482
Accuracy: 0.9518
Precision: 0.9514
Recall: 0.9518
F1: 0.9515
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: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
Precision
Recall
F1
0.5032
1.0
1342
0.2337
0.9277
0.9265
0.9277
0.9267
0.1954
2.0
2684
0.1623
0.9490
0.9487
0.9490
0.9487
0.154
3.0
4026
0.1482
0.9518
0.9514
0.9518
0.9515
Framework versions
PEFT 0.17.1
Transformers 4.55.4
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.21.4
