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CU_with_BERT – AI Model by AndreaPizzi | AlphaNeural AI
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CU_with_BERT
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transformers
safetensors
distilbert
text-classification
generated_from_trainer
distilbert/distilbert-base-uncased-distilled-squad
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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CU_with_BERT
This model is a fine-tuned version of
distilbert/distilbert-base-uncased-distilled-squad
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.8013
Accuracy: 0.6181
F1: 0.6181
Precision: 0.6181
Recall: 0.6181
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: 1.5e-05
train_batch_size: 32
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 1
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1
Precision
Recall
No log
1.0
187
0.8013
0.6181
0.6181
0.6181
0.6181
Framework versions
Transformers 4.51.3
Pytorch 2.6.0+cu126
Datasets 3.5.0
Tokenizers 0.21.1
