k4black's picture
update model card README.md
37835c2
|
raw
history blame
3.46 kB
metadata
license: mit
tags:
  - generated_from_trainer
metrics:
  - f1
model-index:
  - name: edos-2023-baseline-roberta-base-label_sexist
    results: []

edos-2023-baseline-roberta-base-label_sexist

This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0182
  • F1: 0.9951

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: 1e-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
  • lr_scheduler_warmup_steps: 5
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss F1
0.5623 0.29 100 0.4459 0.6857
0.4055 0.57 200 0.3119 0.8135
0.3455 0.86 300 0.2704 0.8430
0.3198 1.14 400 0.2431 0.8640
0.2817 1.43 500 0.2579 0.8650
0.2997 1.71 600 0.2089 0.8911
0.2784 2.0 700 0.2069 0.8818
0.2231 2.29 800 0.2233 0.8872
0.2261 2.57 900 0.1598 0.9215
0.238 2.86 1000 0.1524 0.9137
0.2014 3.14 1100 0.1155 0.9441
0.1669 3.43 1200 0.1203 0.9436
0.1691 3.71 1300 0.0957 0.9566
0.1787 4.0 1400 0.0763 0.9709
0.1277 4.29 1500 0.0696 0.9717
0.1359 4.57 1600 0.0654 0.9734
0.1138 4.86 1700 0.0542 0.9788
0.1057 5.14 1800 0.0587 0.9747
0.1055 5.43 1900 0.0420 0.9843
0.0908 5.71 2000 0.0386 0.9866
0.1094 6.0 2100 0.0328 0.9890
0.0845 6.29 2200 0.0320 0.9885
0.0697 6.57 2300 0.0322 0.9893
0.083 6.86 2400 0.0260 0.9912
0.0659 7.14 2500 0.0259 0.9923
0.0745 7.43 2600 0.0304 0.9900
0.0623 7.71 2700 0.0284 0.9912
0.0825 8.0 2800 0.0215 0.9933
0.0414 8.29 2900 0.0222 0.9939
0.0477 8.57 3000 0.0231 0.9940
0.0606 8.86 3100 0.0211 0.9937
0.0616 9.14 3200 0.0190 0.9947
0.0413 9.43 3300 0.0182 0.9950
0.0462 9.71 3400 0.0181 0.9949
0.0473 10.0 3500 0.0182 0.9951

Framework versions

  • Transformers 4.24.0
  • Pytorch 1.12.1+cu113
  • Datasets 2.7.1
  • Tokenizers 0.13.2