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WITHINAPPS_NDD-dimeshift_test-content – AI Model by lgk03 | AlphaNeural AI
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WITHINAPPS_NDD-dimeshift_test-content
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transformers
tensorboard
safetensors
distilbert
text-classification
generated_from_trainer
distilbert/distilbert-base-uncased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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WITHINAPPS_NDD-dimeshift_test-content
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: 0.1472
Accuracy: 0.9415
F1: 0.9254
Precision: 0.9450
Recall: 0.9415
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: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
F1
Precision
Recall
No log
0.9897
72
0.2019
0.9205
0.8823
0.8473
0.9205
No log
1.9794
144
0.1472
0.9415
0.9254
0.9450
0.9415
Framework versions
Transformers 4.41.1
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1
