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fold_11

Token Classification
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deberta
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Amna100
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metadata
license: mit
base_model: Amna100/PreTraining-MLM
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: fold_11
    results: []

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fold_11

This model is a fine-tuned version of Amna100/PreTraining-MLM on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0112
  • Precision: 0.7083
  • Recall: 0.6886
  • F1: 0.6983
  • Accuracy: 0.9993
  • Roc Auc: 0.9950
  • Pr Auc: 0.9999

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: 5
  • eval_batch_size: 5
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy Roc Auc Pr Auc
0.0313 1.0 632 0.0125 0.7457 0.5494 0.6327 0.9992 0.9881 0.9997
0.0127 2.0 1264 0.0117 0.7078 0.6684 0.6875 0.9993 0.9949 0.9998
0.0061 3.0 1896 0.0112 0.7083 0.6886 0.6983 0.9993 0.9950 0.9999
0.0024 4.0 2528 0.0159 0.8163 0.6076 0.6967 0.9994 0.9893 0.9997
0.0016 5.0 3160 0.0153 0.7652 0.6684 0.7135 0.9993 0.9931 0.9998
0.0008 6.0 3792 0.0170 0.7798 0.6633 0.7168 0.9994 0.9921 0.9998

Framework versions

  • Transformers 4.41.0.dev0
  • Pytorch 2.2.1+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1