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distilbert-base-uncased-holdout-hp-fixed – AI Model by lwolfrat | AlphaNeural AI
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lwolfrat
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distilbert-base-uncased-holdout-hp-fixed
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transformers
safetensors
distilbert
text-classification
generated_from_trainer
distilbert/distilbert-base-uncased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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distilbert-base-uncased-holdout-hp-fixed
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: 0.2977
Accuracy: 0.9583
Precision Macro: 0.3194
Recall Macro: 0.3333
F1 Macro: 0.3262
Krippendorffs Alpha: -0.0140
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: 1.104934561478167e-05
train_batch_size: 1
eval_batch_size: 16
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
lr_scheduler_warmup_steps: 67
num_epochs: 2
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
Precision Macro
Recall Macro
F1 Macro
Krippendorffs Alpha
0.4786
1.0
960
0.3066
0.9583
0.3194
0.3333
0.3262
-0.0140
0.4605
2.0
1920
0.2977
0.9583
0.3194
0.3333
0.3262
-0.0140
Framework versions
Transformers 4.53.1
Pytorch 2.7.1+cu126
Datasets 3.6.0
Tokenizers 0.21.2
