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---
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-256
    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: 4.87
          - name: F1
            type: f1
            value: 23.82
          - name: ROUGE-1
            type: rouge1
            value: 23.88
          - name: ROUGE-2
            type: rouge2
            value: 8.54
          - name: ROUGE-L
            type: rougel
            value: 23.14
          - name: ROUGE-Lsum
            type: rougelsum
            value: 23.13
          - name: Exact Match
            type: exact_match
            value: 0.32
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# german-jeopardy-longt5-large-256

This model is a fine-tuned version of [google/long-t5-tglobal-large](https://huggingface.co/google/long-t5-tglobal-large) on the [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8541
- Brevity Penalty: 0.8795
- System Length: 18427
- Reference Length: 20793
- ROUGE-1: 23.88
- ROUGE-2: 8.54
- ROUGE-L: 23.14
- ROUGE-Lsum: 23.13
- Exact Match: 0.32
- BLEU: 4.87
- F1: 23.82

## Model description

See [google/long-t5-tglobal-large](https://huggingface.co/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](https://huggingface.co/datasets/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: 128
- total_train_batch_size: 256
- 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   |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:----------------:|:-------:|:-------:|:-------:|:----------:|:-----------:|:------:|:---------------------:|:------:|
|    8.8727     | 0.99  |  36  |     6.3810      |   2198   |    0     |    0     |    0     |   2204   |    0     |    0     |    0     |   99.7278    |     0.0      |     0.0      |     0.0      |     0.0002      |     2204      |      21250       |   0.0   |   0.0   |   0.0   |    0.0     |     0.0     |  0.0   |          2.0          |  0.0   |
|    6.0165     | 1.98  |  72  |     5.3864      |   3587   |   137    |    0     |    0     |  21960   |  19756   |  17552   |  15348   |   16.3342    |    0.6935    |    0.0028    |    0.0016    |       1.0       |     21960     |      21250       | 0.0702  | 0.0079  |  0.07   |    0.07    |     0.0     | 0.0851 |        15.0091        | 0.073  |
|    5.1537     |  3.0  | 109  |     4.9617      |   3601   |   145    |    1     |    0     |  14449   |  12245   |  10041   |   7837   |   24.9221    |    1.1842    |     0.01     |    0.0064    |     0.6246      |     14449     |      21250       | 0.0882  | 0.0107  | 0.0877  |   0.0876   |     0.0     |  0.13  |        9.5309         | 0.0926 |
|     4.863     | 3.99  | 145  |     4.5531      |   4590   |   229    |    19    |    0     |  41674   |  39470   |  37266   |  35062   |   11.0141    |    0.5802    |    0.051     |    0.0014    |       1.0       |     41674     |      21250       | 0.0811  | 0.0081  | 0.0768  |   0.0767   |     0.0     | 0.1468 |        29.4528        | 0.0836 |
|    4.5201     | 4.97  | 181  |     4.2020      |   3643   |   169    |    19    |    0     |  16104   |  13900   |  11696   |   9492   |   22.6217    |    1.2158    |    0.1624    |    0.0053    |     0.7265      |     16104     |      21250       | 0.0865  | 0.0115  | 0.0856  |   0.0855   |     0.0     | 0.2845 |        12.5077        | 0.0907 |
|    4.1347     | 5.99  | 218  |     3.9353      |   3670   |   167    |    20    |    0     |  16796   |  14592   |  12388   |  10184   |   21.8504    |    1.1445    |    0.1614    |    0.0049    |     0.7671      |     16796     |      21250       |  0.087  | 0.0114  | 0.0859  |   0.0858   |     0.0     | 0.2878 |        13.1656        | 0.0917 |
|     4.012     | 6.98  | 254  |     3.7593      |   3780   |   198    |    35    |    1     |  16582   |  14378   |  12174   |   9970   |   22.7958    |    1.3771    |    0.2875    |     0.01     |     0.7546      |     16582     |      21250       | 0.0916  | 0.0128  | 0.0903  |   0.0902   |     0.0     | 0.4139 |        12.2931        | 0.0968 |
|    3.7048     |  8.0  | 291  |     3.6034      |   3668   |   205    |    36    |    3     |  16158   |  13954   |  11750   |   9546   |   22.7008    |    1.4691    |    0.3064    |    0.0314    |     0.7297      |     16158     |      21250       | 0.0882  | 0.0134  | 0.0873  |   0.0872   |     0.0     | 0.5493 |        11.7568        | 0.0923 |
|    3.6284     | 8.99  | 327  |     3.4567      |   4070   |   527    |   160    |    28    |  17459   |  15255   |  13051   |  10847   |   23.3118    |    3.4546    |    1.226     |    0.2581    |     0.8048      |     17459     |      21250       | 0.1109  | 0.0281  | 0.1083  |   0.1082   |     0.0     | 1.8083 |        9.7777         | 0.1152 |
|    3.4605     | 9.98  | 363  |     3.3390      |   4325   |   512    |   128    |    27    |  18829   |  16625   |  14421   |  12217   |   22.9699    |    3.0797    |    0.8876    |    0.221     |     0.8793      |     18829     |      21250       | 0.1206  | 0.0288  | 0.1168  |   0.1167   |     0.0     | 1.6972 |        12.6729        | 0.1254 |
|    3.2267     | 10.99 | 400  |     3.1995      |   4498   |   774    |   237    |    49    |  18802   |  16598   |  14394   |  12190   |    23.923    |    4.6632    |    1.6465    |    0.402     |     0.8779      |     18802     |      21250       | 0.1348  | 0.0405  |  0.132  |   0.1319   |   0.0005    | 2.5735 |        11.5009        | 0.1381 |
|    3.1761     | 11.98 | 436  |     3.1165      |   4578   |   866    |   260    |    50    |  16963   |  14759   |  12555   |  10351   |   26.9882    |    5.8676    |    2.0709    |    0.483     |     0.7767      |     16963     |      21250       | 0.1454  | 0.0464  | 0.1426  |   0.1427   |   0.0005    | 2.7554 |        10.5172        | 0.1492 |
|    3.0323     | 12.97 | 472  |     3.0074      |   5019   |   1048   |   319    |    59    |  18077   |  15873   |  13669   |  11465   |   27.7646    |    6.6024    |    2.3337    |    0.5146    |      0.839      |     18077     |      21250       | 0.1691  | 0.0557  | 0.1648  |   0.1647   |   0.0009    | 3.2318 |        12.8294        | 0.1729 |
|    2.8223     | 13.99 | 509  |     2.8911      |   5257   |   1120   |   341    |    85    |  17074   |  14870   |  12666   |  10462   |   30.7895    |    7.5319    |    2.6922    |    0.8125    |      0.783      |     17074     |      21250       |  0.189  | 0.0635  | 0.1841  |   0.184    |   0.0018    | 3.7161 |        12.6824        | 0.1929 |
|    2.7732     | 14.98 | 545  |     2.8103      |   5616   |   1271   |   407    |   113    |  17784   |  15580   |  13376   |  11172   |   31.5789    |    8.1579    |    3.0428    |    1.0115    |     0.8229      |     17784     |      21250       | 0.2122  | 0.0731  | 0.2063  |   0.2061   |   0.0045    | 4.3667 |        13.0944        | 0.217  |
|     2.58      | 16.0  | 582  |     2.7183      |   5959   |   1461   |   510    |   171    |  18808   |  16604   |  14400   |  12196   |   31.6833    |    8.7991    |    3.5417    |    1.4021    |     0.8782      |     18808     |      21250       | 0.2286  | 0.0822  | 0.2214  |   0.2212   |   0.0064    | 5.357  |        13.9174        | 0.2316 |
|    2.5368     | 16.99 | 618  |     2.6630      |   5935   |   1543   |   576    |   201    |  16923   |  14719   |  12515   |  10311   |   35.0706    |    10.483    |    4.6025    |    1.9494    |     0.7744      |     16923     |      21250       | 0.2365  |  0.089  | 0.2309  |   0.2307   |   0.0059    | 5.8686 |        12.3185        | 0.2377 |
|    2.4325     | 17.98 | 654  |     2.5798      |   6305   |   1756   |   685    |   265    |  17870   |  15666   |  13462   |  11258   |   35.2826    |    11.209    |    5.0884    |    2.3539    |     0.8277      |     17870     |      21250       | 0.2518  | 0.0982  | 0.2452  |   0.2452   |   0.0059    | 6.8664 |        13.1688        | 0.2537 |
|    2.2632     | 18.99 | 691  |     2.5155      |   6577   |   1888   |   762    |   304    |  17785   |  15581   |  13377   |  11173   |   36.9806    |   12.1173    |    5.6963    |    2.7208    |      0.823      |     17785     |      21250       | 0.2689  | 0.1102  |  0.261  |   0.2611   |   0.0086    | 7.5129 |        13.2373        | 0.2702 |
|    2.2026     | 19.79 | 720  |     2.4997      |   6644   |   1853   |   720    |   273    |  17658   |  15454   |  13250   |  11046   |    37.626    |   11.9904    |    5.434     |    2.4715    |     0.8159      |     17658     |      21250       | 0.2717  | 0.1097  | 0.2628  |   0.2625   |   0.0073    | 7.1987 |        13.6343        | 0.2742 |


### Framework versions

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