finetuned_robert / README.md
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metadata
license: mit
base_model: roberta-large
tags:
  - generated_from_trainer
metrics:
  - f1
  - precision
  - recall
  - accuracy
model-index:
  - name: finetuned_robert
    results: []

finetuned_robert

This model is a fine-tuned version of roberta-large on the topic-keyword inclusion dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2694
  • F1: 0.9041
  • Precision: 0.8354
  • Recall: 0.9851
  • Accuracy: 0.9067

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
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 6

Training results

Training Loss Epoch Step Validation Loss F1 Precision Recall Accuracy
0.7067 0.28 10 0.6843 0.0 0.0 0.0 0.5533
0.7087 0.56 20 0.6786 0.0 0.0 0.0 0.5533
0.6887 0.83 30 0.6543 0.7241 0.8571 0.6269 0.7867
0.6773 1.11 40 0.6069 0.816 0.8793 0.7612 0.8467
0.6073 1.39 50 0.4951 0.7711 0.6465 0.9552 0.7467
0.5731 1.67 60 0.3976 0.8219 0.7595 0.8955 0.8267
0.4806 1.94 70 0.3487 0.8421 0.8485 0.8358 0.86
0.4685 2.22 80 0.5218 0.7811 0.6471 0.9851 0.7533
0.4243 2.5 90 0.8471 0.7322 0.5776 1.0 0.6733
0.3692 2.78 100 0.3453 0.8514 0.7778 0.9403 0.8533
0.4633 3.06 110 0.2813 0.8611 0.8052 0.9254 0.8667
0.3334 3.33 120 0.3090 0.8514 0.7778 0.9403 0.8533
0.3167 3.61 130 0.3531 0.8497 0.7558 0.9701 0.8467
0.2615 3.89 140 0.2679 0.8873 0.84 0.9403 0.8933
0.2672 4.17 150 0.2528 0.8889 0.8312 0.9552 0.8933
0.2103 4.44 160 0.2905 0.8649 0.7901 0.9552 0.8667
0.2208 4.72 170 0.2992 0.8649 0.7901 0.9552 0.8667
0.2267 5.0 180 0.2911 0.8859 0.8049 0.9851 0.8867
0.1623 5.28 190 0.2355 0.9014 0.8533 0.9552 0.9067
0.2148 5.56 200 0.2200 0.9091 0.8553 0.9701 0.9133
0.1537 5.83 210 0.2694 0.9041 0.8354 0.9851 0.9067

Framework versions

  • Transformers 4.40.0.dev0
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2