README.md

metadata

language:
  - de
tags:
  - question-generation
  - german
  - text2text-generation
  - generated_from_trainer
datasets:
  - lmqg/qg_dequad
metrics:
  - bleu4
  - f1
  - rouge
  - exact_match
model-index:
  - name: german-jeopardy-longt5-large-128
    results:
      - task:
          name: Sequence-to-sequence Language Modeling
          type: text2text-generation
        dataset:
          name: lmqg/qg_dequad
          type: default
          args: default
        metrics:
          - name: BLEU-4
            type: bleu4
            value: 6.99
          - name: F1
            type: f1
            value: 28.39
          - name: ROUGE-1
            type: rouge1
            value: 28.96
          - name: ROUGE-2
            type: rouge2
            value: 11.91
          - name: ROUGE-L
            type: rougel
            value: 27.92
          - name: ROUGE-Lsum
            type: rougelsum
            value: 27.91
          - name: Exact Match
            type: exact_match
            value: 0.95

german-jeopardy-longt5-large-128

This model is a fine-tuned version of google/long-t5-tglobal-large on the lmqg/qg_dequad dataset. It achieves the following results on the evaluation set:

• Loss: 2.6149
• Brevity Penalty: 0.9386
• System Length: 19554
• Reference Length: 20793
• ROUGE-1: 28.96
• ROUGE-2: 11.91
• ROUGE-L: 27.92
• ROUGE-Lsum: 27.91
• Exact Match: 0.95
• BLEU: 6.99
• F1: 28.39

Model description

See google/long-t5-tglobal-large for more information about the model architecture.
The model was trained on a single NVIDIA RTX 3090 GPU with 24GB of VRAM.

Intended uses & limitations

This model can be used for question generation on German text.

Training and evaluation data

See lmqg/qg_dequad.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 2
• eval_batch_size: 2
• seed: 7
• gradient_accumulation_steps: 64
• total_train_batch_size: 128
• optimizer: Adafactor
• lr_scheduler_type: constant
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Counts 1	Counts 2	Counts 3	Counts 4	Totals 1	Totals 2	Totals 3	Totals 4	Precisions 1	Precisions 2	Precisions 3	Precisions 4	Brevity Penalty	System Length	Reference Length	ROUGE-1	ROUGE-2	ROUGE-L	ROUGE-Lsum	Exact Match	BLEU	Mean Generated Length	F1
7.5882	0.99	72	5.6823	3993	105	0	0	14790	12586	10382	8178	26.998	0.8343	0.0048	0.0031	0.6461	14790	21250	0.1101	0.0077	0.1078	0.1076	0.0	0.0872	9.7105	0.1155
5.2903	1.99	145	4.8721	3827	229	32	0	18894	16690	14486	12282	20.2551	1.3721	0.2209	0.0041	0.8828	18894	21250	0.0924	0.015	0.091	0.0909	0.0	0.351	16.7005	0.0964
4.6636	3.0	218	4.2806	3638	174	21	0	15268	13064	10860	8656	23.8276	1.3319	0.1934	0.0058	0.6758	15268	21250	0.0884	0.012	0.0876	0.0874	0.0	0.2933	8.9197	0.0925
4.2229	4.0	291	3.9210	4274	240	24	0	29308	27104	24900	22696	14.583	0.8855	0.0964	0.0022	1.0	29308	21250	0.0894	0.0109	0.0849	0.0849	0.0	0.2288	24.7015	0.1023
3.9434	4.99	363	3.6907	3652	218	35	1	16442	14238	12034	9830	22.2114	1.5311	0.2908	0.0102	0.7465	16442	21250	0.0856	0.0141	0.0843	0.0842	0.0	0.4204	12.3049	0.0898
3.6152	5.99	436	3.4603	4103	341	77	11	20581	18377	16173	13969	19.9359	1.8556	0.4761	0.0787	0.968	20581	21250	0.107	0.019	0.1023	0.1024	0.0	1.0505	14.3607	0.112
3.3814	7.0	509	3.2883	4342	675	218	43	17763	15559	13355	11151	24.4441	4.3383	1.6323	0.3856	0.8218	17763	21250	0.1264	0.0353	0.1234	0.1234	0.0005	2.3489	10.2418	0.1308
3.1711	8.0	582	3.0988	4820	856	246	44	19759	17555	15351	13147	24.3939	4.8761	1.6025	0.3347	0.9273	19759	21250	0.1503	0.0465	0.1455	0.1457	0.0005	2.6207	14.3249	0.1547
3.0147	8.99	654	2.9540	5167	1066	321	76	18725	16521	14317	12113	27.5941	6.4524	2.2421	0.6274	0.8739	18725	21250	0.1773	0.0588	0.1721	0.1721	0.0018	3.4764	14.3067	0.1816
2.7829	9.99	727	2.8288	5625	1267	420	124	17327	15123	12919	10715	32.4638	8.378	3.251	1.1573	0.7974	17327	21250	0.2127	0.0741	0.2067	0.2065	0.0045	4.5099	12.9741	0.2159
2.6093	10.99	800	2.7177	6005	1469	528	181	18625	16421	14217	12013	32.2416	8.9459	3.7139	1.5067	0.8685	18625	21250	0.229	0.0827	0.2215	0.2213	0.0064	5.5051	14.4791	0.231
2.453	12.0	873	2.5914	6396	1744	664	246	18307	16103	13899	11695	34.9375	10.8303	4.7773	2.1035	0.8515	18307	21250	0.2553	0.0998	0.2479	0.2478	0.0059	6.6865	13.7142	0.2565
2.3329	12.99	945	2.4993	6673	1888	741	291	18451	16247	14043	11839	36.1661	11.6206	5.2767	2.458	0.8592	18451	21250	0.2747	0.1114	0.2652	0.2652	0.0091	7.383	14.1751	0.2749
2.1663	13.99	1018	2.4196	6953	2052	834	337	18531	16327	14123	11919	37.5209	12.5681	5.9053	2.8274	0.8635	18531	21250	0.2886	0.1215	0.2773	0.277	0.0082	8.1343	14.6783	0.2889
2.0422	14.99	1091	2.3703	6968	2089	862	365	17984	15780	13576	11372	38.7456	13.2383	6.3494	3.2096	0.8339	17984	21250	0.2961	0.1268	0.2858	0.2857	0.0113	8.4322	13.6987	0.2951
1.9245	16.0	1164	2.3217	7500	2353	999	446	19017	16813	14609	12405	39.4384	13.9951	6.8383	3.5953	0.8892	19017	21250	0.3149	0.1407	0.3017	0.3017	0.0132	9.5973	14.77	0.314
1.8216	17.0	1237	2.2705	7444	2357	1044	488	18219	16015	13811	11607	40.8584	14.7175	7.5592	4.2044	0.8467	18219	21250	0.3201	0.1437	0.3081	0.3077	0.0132	9.9557	13.8031	0.3181
1.7503	17.99	1309	2.2386	7571	2487	1114	515	18275	16071	13867	11663	41.4282	15.4751	8.0335	4.4157	0.8498	18275	21250	0.3289	0.1512	0.3153	0.3151	0.0145	10.4354	13.9106	0.3265
1.6342	18.99	1382	2.2183	7697	2536	1155	537	18129	15925	13721	11517	42.4568	15.9246	8.4178	4.6627	0.8418	18129	21250	0.3342	0.1559	0.3224	0.3222	0.0177	10.7447	13.8494	0.3313
1.5474	19.79	1440	2.1956	7879	2632	1187	570	18815	16611	14407	12203	41.8762	15.8449	8.2391	4.671	0.8786	18815	21250	0.3398	0.1607	0.326	0.326	0.0177	11.1066	14.5136	0.3375

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.0
• Datasets 2.12.0
• Tokenizers 0.13.3