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bert2bert-model2

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metadata
tags:
  - generated_from_trainer
datasets:
  - id_liputan6
model-index:
  - name: >-
      bert2bert-extabs-canonicalcleandata-lr-5e-05-batchsize-4-encmaxlen-512-decmaxlen-256
    results: []

bert2bert-extabs-canonicalcleandata-lr-5e-05-batchsize-4-encmaxlen-512-decmaxlen-256

10 Epoch Extractive Training + 10 Epoch Abtractive Training

  • Dev Set: Canonical Clean Data & Extreme Clean Data
  • Encoder max length (input): 512
  • Decoder max length (output): 256

This model was trained from scratch on the id_liputan6 dataset. It achieves the following results on the evaluation set:

  • Loss: 3.0021
  • R1 Precision: 0.3553
  • R1 Recall: 0.2599
  • R1 Fmeasure: 0.2974
  • R2 Precision: 0.1458
  • R2 Recall: 0.1039
  • R2 Fmeasure: 0.12
  • Rl Precision: 0.2925
  • Rl Recall: 0.2139
  • Rl Fmeasure: 0.2448

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: 5e-05
  • train_batch_size: 18
  • eval_batch_size: 18
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss R1 Precision R1 Recall R1 Fmeasure R2 Precision R2 Recall R2 Fmeasure Rl Precision Rl Recall Rl Fmeasure
1.7393 1.0 10772 2.6782 0.3432 0.2497 0.2864 0.1375 0.0975 0.113 0.2828 0.206 0.2361
1.4091 2.0 21544 2.6063 0.3486 0.2534 0.2907 0.142 0.1004 0.1164 0.2878 0.2094 0.2401
1.246 3.0 32316 2.6079 0.3535 0.2578 0.2955 0.1457 0.1036 0.1199 0.2917 0.2131 0.244
1.1175 4.0 43088 2.6382 0.3579 0.2618 0.2996 0.1488 0.106 0.1225 0.2956 0.2163 0.2475
1.0102 5.0 53860 2.6818 0.3574 0.2609 0.2987 0.1478 0.1052 0.1217 0.2949 0.2154 0.2466
0.9141 6.0 64632 2.7428 0.3571 0.2616 0.2992 0.148 0.1056 0.122 0.2938 0.2152 0.2461
0.8261 7.0 75404 2.8255 0.3534 0.2582 0.2956 0.1457 0.1039 0.12 0.2906 0.2126 0.2432
0.7509 8.0 86176 2.8975 0.3517 0.2572 0.2943 0.1428 0.1016 0.1175 0.289 0.2113 0.2418
0.6822 9.0 96948 2.9586 0.3557 0.2599 0.2975 0.1466 0.1043 0.1206 0.2936 0.2145 0.2455
0.6289 10.0 107720 3.0021 0.3553 0.2599 0.2974 0.1458 0.1039 0.12 0.2925 0.2139 0.2448

Framework versions

  • Transformers 4.39.3
  • Pytorch 2.2.1
  • Datasets 2.18.0
  • Tokenizers 0.15.2