Edit model card

aces-roberta-13

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

  • Loss: 0.4600
  • Precision: 0.8364
  • Recall: 0.8452
  • F1: 0.8383
  • Accuracy: 0.8452
  • F1 Who: 0.9189
  • F1 What: 0.8621
  • F1 Where: 0.9231
  • F1 How: 0.9141

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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy F1 Who F1 What F1 Where F1 How
1.9849 0.35 20 1.4123 0.5426 0.6351 0.5494 0.6351 0.1026 0.6222 0.3232 0.7857
1.2159 0.7 40 0.9450 0.6559 0.7188 0.6592 0.7188 0.6780 0.7539 0.7071 0.7882
0.8634 1.05 60 0.6885 0.7652 0.7994 0.7725 0.7994 0.9067 0.8152 0.8070 0.8940
0.6777 1.4 80 0.6144 0.7650 0.7946 0.7711 0.7946 0.9189 0.7876 0.8039 0.9085
0.6051 1.75 100 0.5485 0.8126 0.8278 0.8150 0.8278 0.9315 0.8362 0.8148 0.9241
0.5511 2.11 120 0.5264 0.8113 0.8167 0.8036 0.8167 0.9315 0.8444 0.8257 0.9199
0.486 2.46 140 0.4867 0.8230 0.8357 0.8248 0.8357 0.9315 0.8539 0.9091 0.9048
0.4813 2.81 160 0.4767 0.8285 0.8278 0.8213 0.8278 0.9189 0.8701 0.9076 0.9135
0.4494 3.16 180 0.5042 0.8152 0.8199 0.8126 0.8199 0.9315 0.8427 0.8333 0.8956
0.4018 3.51 200 0.4802 0.8248 0.8357 0.8249 0.8357 0.9189 0.8736 0.8780 0.9357
0.4205 3.86 220 0.4723 0.8340 0.8389 0.8346 0.8389 0.9189 0.8636 0.9138 0.8986
0.3535 4.21 240 0.4669 0.8324 0.8452 0.8364 0.8452 0.9189 0.8571 0.9138 0.9167
0.3808 4.56 260 0.4585 0.8349 0.8452 0.8383 0.8452 0.9189 0.8621 0.9231 0.9141
0.3491 4.91 280 0.4600 0.8364 0.8452 0.8383 0.8452 0.9189 0.8621 0.9231 0.9141

Framework versions

  • Transformers 4.26.0
  • Pytorch 1.13.1+cu117
  • Datasets 2.8.0
  • Tokenizers 0.13.2
Downloads last month
1