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deberta-v3-base-p-tuning-isarcasm

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metadata
license: mit
base_model: microsoft/deberta-v3-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: deberta-v3-base-p-tuning-isarcasm
    results: []

deberta-v3-base-p-tuning-isarcasm

This model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6620
  • Accuracy: 0.4476
  • F1: 0.3603
  • Precision: 0.2266
  • Recall: 0.8790

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: 0.001
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
No log 1.0 108 0.6889 0.3143 0.2941 0.1786 0.8333
No log 2.0 216 0.7020 0.8286 0.0 0.0 0.0
No log 3.0 324 0.6725 0.6286 0.2353 0.1818 0.3333
No log 4.0 432 0.7169 0.1714 0.2927 0.1714 1.0
0.7087 5.0 540 0.6925 0.5429 0.3846 0.25 0.8333
0.7087 6.0 648 0.6991 0.1714 0.2927 0.1714 1.0
0.7087 7.0 756 0.6780 0.8286 0.0 0.0 0.0
0.7087 8.0 864 0.6851 0.8286 0.0 0.0 0.0
0.7087 9.0 972 0.6712 0.8286 0.0 0.0 0.0
0.7055 10.0 1080 0.6767 0.3143 0.3333 0.2 1.0
0.7055 11.0 1188 0.6720 0.5714 0.4000 0.2632 0.8333
0.7055 12.0 1296 0.6710 0.3714 0.3529 0.2143 1.0
0.7055 13.0 1404 0.6676 0.4857 0.3077 0.2 0.6667
0.6916 14.0 1512 0.6735 0.3714 0.3125 0.1923 0.8333
0.6916 15.0 1620 0.6762 0.3714 0.3529 0.2143 1.0
0.6916 16.0 1728 0.6642 0.6286 0.3158 0.2308 0.5
0.6916 17.0 1836 0.6609 0.5143 0.32 0.2105 0.6667
0.6916 18.0 1944 0.6632 0.4571 0.2963 0.1905 0.6667
0.6798 19.0 2052 0.6640 0.4 0.2759 0.1739 0.6667
0.6798 20.0 2160 0.6644 0.4 0.2759 0.1739 0.6667

Framework versions

  • Transformers 4.32.0
  • Pytorch 2.1.1+cu121
  • Datasets 2.14.5
  • Tokenizers 0.13.3