aehrm / gepabert

huggingface.co
Total runs: 149
24-hour runs: 2
7-day runs: 13
30-day runs: 139
Model's Last Updated: December 14 2024
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Introduction of gepabert

Model Details of gepabert

GePaBERT

This model is a fine-tuned version of deepset/gbert-large on a corpus of parliamentary speeches held in the German Bundestag. It was specifically designed for the KONVENS 2023 shared task on speaker attribution. It achieves the following results on the evaluation set:

  • Loss: 0.7997
  • Accuracy: 0.8020
Training and evaluation data

The corpus of parliamentary speeches covers speeches held in the German Bundestag during the 9th-20th legislative period, from 1980 to April 2023. (757 MB) The speeches were automatically prepared from the publicly available plenary protocols , using the extraction pipeline Open Discourse ( GitHub code ). Evaluation was done on a randomly-sampled 5% held-out dataset.

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate : 2e-05
  • train_batch_size : 8
  • 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 Accuracy Validation Loss
1.0697 0.1 3489 0.7697 0.9802
1.0339 0.2 6978 0.7727 0.9562
1.0203 0.3 10467 0.7739 0.9463
1.0215 0.4 13956 0.7743 0.9477
1.0046 0.5 17445 0.7779 0.9299
1.0036 0.6 20934 0.7764 0.9372
1.2439 0.7 24423 0.7352 1.2473
1.4382 0.8 27912 0.6947 1.5782
1.1744 0.9 31401 0.7764 0.9360
0.9718 1.0 34890 0.7799 0.9179
0.9557 1.1 38379 0.7824 0.9038
0.947 1.2 41868 0.7830 0.9000
0.9487 1.3 45357 0.7833 0.8982
0.9457 1.4 48846 0.7851 0.8862
0.9442 1.5 52335 0.7863 0.8839
0.9473 1.6 55824 0.7850 0.8855
0.9388 1.7 59313 0.7865 0.8771
0.9293 1.8 62802 0.7868 0.8805
0.9242 1.9 66291 0.7873 0.8738
0.9241 2.0 69780 0.7872 0.8757
0.9127 2.1 73269 0.7896 0.8641
0.9114 2.2 76758 0.7900 0.8627
0.9095 2.3 80247 0.7913 0.8540
0.9042 2.4 83736 0.7920 0.8518
0.8999 2.5 87225 0.7919 0.8514
0.899 2.6 90714 0.7918 0.8543
0.8945 2.7 94203 0.7935 0.8418
0.8867 2.8 97692 0.7934 0.8437
0.893 2.9 101181 0.7938 0.8414
0.8798 3.0 104670 0.7951 0.8359
0.868 3.1 108159 0.7943 0.8375
0.8736 3.2 111648 0.7956 0.8323
0.8756 3.3 115137 0.7959 0.8315
0.8681 3.4 118626 0.7964 0.8258
0.8726 3.5 122115 0.7966 0.8266
0.8594 3.6 125604 0.7967 0.8246
0.8515 3.7 129093 0.7973 0.8227
0.8568 3.8 132582 0.7979 0.8195
0.8626 3.9 136071 0.7983 0.8173
0.8585 4.0 139560 0.7978 0.8190
0.8497 4.1 143049 0.7991 0.8127
0.8383 4.2 146538 0.7992 0.8154
0.8457 4.3 150027 0.8002 0.8080
0.8353 4.4 153516 0.8005 0.8077
0.8393 4.5 157005 0.8009 0.8027
0.8417 4.6 160494 0.8050 0.8007
0.836 4.7 163983 0.8004 0.8017
0.8317 4.8 167472 0.7993 0.8021
0.832 4.9 170961 0.8011 0.8013
Framework versions
  • Transformers 4.30.2
  • Pytorch 2.0.1+cu117
  • Datasets 2.13.1
  • Tokenizers 0.13.3

Runs of aehrm gepabert on huggingface.co

149
Total runs
2
24-hour runs
4
3-day runs
13
7-day runs
139
30-day runs

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