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DuplicatiDistillBertCitations – AI Model by Kamer | AlphaNeural AI
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DuplicatiDistillBertCitations
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transformers
pytorch
distilbert
text-classification
generated_from_trainer
distilbert/distilbert-base-uncased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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DuplicatiDistillBertCitations
This model is a fine-tuned version of
distilbert-base-uncased
on the None dataset. It achieves the following results on the evaluation set:
eval_loss: 0.5474
eval_Accuracy: 0.8571
eval_F1_macro: 0.8668
eval_F1_class_0: 0.7476
eval_F1_class_1: 0.8627
eval_F1_class_2: 0.8975
eval_F1_class_3: 0.9362
eval_F1_class_4: 0.9415
eval_F1_class_5: 0.9176
eval_F1_class_6: 0.8864
eval_F1_class_7: 0.9548
eval_F1_class_8: 0.9196
eval_F1_class_9: 0.9424
eval_F1_class_10: 0.6921
eval_F1_class_11: 0.3927
eval_F1_class_12: 0.8407
eval_F1_class_13: 0.9495
eval_F1_class_14: 0.8884
eval_F1_class_15: 0.8514
eval_F1_class_16: 0.8750
eval_F1_class_17: 0.9115
eval_F1_class_18: 0.9647
eval_F1_class_19: 0.9630
eval_runtime: 30.4778
eval_samples_per_second: 166.646
eval_steps_per_second: 20.835
epoch: 1.33
step: 7681
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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10
Framework versions
Transformers 4.32.0
Pytorch 2.0.1+cu117
Datasets 2.14.4
Tokenizers 0.13.3
