--- Part of BA Thesis by Enis Settouf @ HTW Berlin Business computing ---

This model was trained additionally on a MLM task for the legal domain on German laws and legal Cases

Data Source: OpenLegalData.io

The rest of this model card has been generated automatically:

cross-en-de-roberta-sentence-transformer-openlegal

This model is a fine-tuned version of T-Systems-onsite/cross-en-de-roberta-sentence-transformer on the None dataset. It achieves the following results on the evaluation set:

• Loss: 2.3120

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: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• distributed_type: tpu
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
6.4835	0.06	500	5.2259
4.9569	0.13	1000	4.5163
4.4158	0.19	1500	4.1257
4.1221	0.25	2000	3.8695
3.8853	0.32	2500	3.6586
3.7092	0.38	3000	3.5126
3.5779	0.45	3500	3.3889
3.4424	0.51	4000	3.2731
3.3556	0.57	4500	3.1984
3.2627	0.64	5000	3.1103
3.1855	0.7	5500	3.0306
3.1381	0.76	6000	2.9796
3.0763	0.83	6500	2.9299
2.9985	0.89	7000	2.8740
2.9359	0.95	7500	2.8300
2.8954	1.02	8000	2.7861
2.8322	1.08	8500	2.7450
2.816	1.14	9000	2.7255
2.8013	1.21	9500	2.6872
2.7414	1.27	10000	2.6538
2.707	1.34	10500	2.6284
2.6866	1.4	11000	2.6021
2.6429	1.46	11500	2.5721
2.6269	1.53	12000	2.5646
2.6173	1.59	12500	2.5323
2.5959	1.65	13000	2.5052
2.5692	1.72	13500	2.4993
2.5563	1.78	14000	2.4840
2.5448	1.84	14500	2.4635
2.4932	1.91	15000	2.4581
2.5106	1.97	15500	2.4342
2.5009	2.03	16000	2.4260
2.46	2.1	16500	2.4152
2.4417	2.16	17000	2.4079
2.4568	2.23	17500	2.4010
2.442	2.29	18000	2.3875
2.4328	2.35	18500	2.3724
2.4126	2.42	19000	2.3645
2.4063	2.48	19500	2.3612
2.362	2.54	20000	2.3565
2.3877	2.61	20500	2.3507
2.3839	2.67	21000	2.3353
2.3657	2.73	21500	2.3326
2.3464	2.8	22000	2.3262
2.3915	2.86	22500	2.3259
2.3613	2.93	23000	2.3195
2.358	2.99	23500	2.3165

Framework versions

• Transformers 4.21.2
• Pytorch 1.8.2+cpu
• Datasets 2.4.0
• Tokenizers 0.12.1