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aehrm/
gepabert

Name: gepabert
Availability: InStock

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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

aehrm/gepabert