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# german-qg-t5-e2e-quad (Work in progress)

This model is a end-to-end question generation model in German. Given a text, it generates several questions about it. This model is a fine-tuned version of [valhalla/t5-base-e2e-qg](https://huggingface.co/valhalla/t5-base-e2e-qg) on the [GermanQuAD dataset from deepset](https://huggingface.co/datasets/deepset/germanquad).

## Model description 

More information needed

## Training and evaluation data

Bleu_1: 0.196051  
Bleu_2: 0.122380  
Bleu_3: 0.079980  
Bleu_4: 0.053672  

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0

### Training results



### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3

{"language": ["de"], "license": "mit", "tags": ["question generation"], "datasets": ["deepset/germanquad"], "widget": [{"text": "Naturschutzwarte haben auf der ostfriesischen Insel Wangerooge zwei seltene Kurzschn\u00e4uzige Seepferdchen entdeckt. Die Tiere seien vergangene Woche bei einer sogenannten Sp\u00fclsaumkontrolle entdeckt worden, bei der die Str\u00e4nde eigentlich nach M\u00fcll und toten V\u00f6geln abgesucht w\u00fcrden, sagte der Gesch\u00e4ftsf\u00fchrer der zust\u00e4ndigen Naturschutz- und Forschungsgemeinschaft Mellumrat, Mathias Heckroth. Dabei seien den Natursch\u00fctzern am Nordstrand kurz hintereinander die beiden leblosen, nur wenige Zentimeter gro\u00dfen Tiere aufgefallen. Experten der Nationalparkverwaltung bestimmten beide Tiere als Kurzschn\u00e4uzige Seepferdchen (Hippocampus hippocampus)."}], "inference": {"parameters": {"max_length": 128}}, "model-index": [{"name": "german-qg-t5-e2e-quad", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation question generation de dataset:deepset/germanquad license:mit autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #question generation #de #dataset-deepset/germanquad #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 




# german-qg-t5-e2e-quad (Work in progress)

This model is a end-to-end question generation model in German. Given a text, it generates several questions about it. This model is a fine-tuned version of valhalla/t5-base-e2e-qg on the GermanQuAD dataset from deepset.

## Model description 

More information needed

## Training and evaluation data

Bleu_1: 0.196051  
Bleu_2: 0.122380  
Bleu_3: 0.079980  
Bleu_4: 0.053672  

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0

### Training results



### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3

# german-qg-t5-e2e-quad (Work in progress)

This model is a end-to-end question generation model in German. Given a text, it generates several questions about it. This model is a fine-tuned version of valhalla/t5-base-e2e-qg on the GermanQuAD dataset from deepset. ## Model description 

More information needed ## Training and evaluation data

Bleu_1: 0.196051  
Bleu_2: 0.122380  
Bleu_3: 0.079980  
Bleu_4: 0.053672 ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0 ### Training results ### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #question generation #de #dataset-deepset/germanquad #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 # german-qg-t5-e2e-quad (Work in progress)

This model is a end-to-end question generation model in German. Given a text, it generates several questions about it. This model is a fine-tuned version of valhalla/t5-base-e2e-qg on the GermanQuAD dataset from deepset. ## Model description 

More information needed ## Training and evaluation data

Bleu_1: 0.196051  
Bleu_2: 0.122380  
Bleu_3: 0.079980  
Bleu_4: 0.053672 ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0 ### Training results ### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #question generation #de #dataset-deepset/germanquad #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
# german-qg-t5-e2e-quad (Work in progress)

This model is a end-to-end question generation model in German. Given a text, it generates several questions about it. This model is a fine-tuned version of valhalla/t5-base-e2e-qg on the GermanQuAD dataset from deepset.## Model description 

More information needed## Training and evaluation data

Bleu_1: 0.196051  
Bleu_2: 0.122380  
Bleu_3: 0.079980  
Bleu_4: 0.053672## Training procedure### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0### Training results### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3