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MiniLM-evidence-types

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metadata
license: mit
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: MiniLM-evidence-types
    results: []

MiniLM-evidence-types

This model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased on the evidence types dataset. It achieved the following results on the evaluation set:

  • Loss: 1.8672
  • Macro f1: 0.3726
  • Weighted f1: 0.7030
  • Accuracy: 0.7161
  • Balanced accuracy: 0.3616

Training and evaluation data

The data set, as well as the code that was used to fine tune this model can be found in the GitHub repository BA-Thesis-Information-Science-Persuasion-Strategies

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Macro f1 Weighted f1 Accuracy Balanced accuracy
1.4106 1.0 250 1.2698 0.1966 0.6084 0.6735 0.2195
1.1437 2.0 500 1.0985 0.3484 0.6914 0.7116 0.3536
0.9714 3.0 750 1.0901 0.2606 0.6413 0.6446 0.2932
0.8382 4.0 1000 1.0197 0.2764 0.7024 0.7237 0.2783
0.7192 5.0 1250 1.0895 0.2847 0.6824 0.6963 0.2915
0.6249 6.0 1500 1.1296 0.3487 0.6888 0.6948 0.3377
0.5336 7.0 1750 1.1515 0.3591 0.6982 0.7024 0.3496
0.4694 8.0 2000 1.1962 0.3626 0.7185 0.7314 0.3415
0.4058 9.0 2250 1.3313 0.3121 0.6920 0.7085 0.3033
0.3746 10.0 2500 1.3993 0.3628 0.6976 0.7047 0.3495
0.3267 11.0 2750 1.5078 0.3560 0.6958 0.7055 0.3464
0.2939 12.0 3000 1.5875 0.3685 0.6968 0.7062 0.3514
0.2677 13.0 3250 1.6470 0.3606 0.6976 0.7070 0.3490
0.2425 14.0 3500 1.7164 0.3714 0.7069 0.7207 0.3551
0.2301 15.0 3750 1.8151 0.3597 0.6975 0.7123 0.3466
0.2268 16.0 4000 1.7838 0.3940 0.7034 0.7123 0.3869
0.201 17.0 4250 1.8328 0.3725 0.6964 0.7062 0.3704
0.1923 18.0 4500 1.8788 0.3708 0.7019 0.7154 0.3591
0.1795 19.0 4750 1.8574 0.3752 0.7031 0.7161 0.3619
0.1713 20.0 5000 1.8672 0.3726 0.7030 0.7161 0.3616

Framework versions

  • Transformers 4.19.2
  • Pytorch 1.11.0+cu113
  • Datasets 2.2.2
  • Tokenizers 0.12.1