


# t5-end2end-questions-generation-dutch

This model is a fine-tuned version of [yhavinga/t5-base-dutch](https://huggingface.co/yhavinga/t5-base-dutch) on a 
Google translated version of SQUAD 1.1 found here: https://www.kaggle.com/datasets/michelvanheijningen/squad1-dutch. 

The code used to finetune the model is largely based on the work by Thomas Simonini. You can find his English model 
[here](https://huggingface.co/ThomasSimonini/t5-end2end-question-generation) and his Google colab tutorial [here](https://colab.research.google.com/drive/1z-Zl2hftMrFXabYfmz8o9YZpgYx6sGeW?usp=sharing)

It achieves the following results on the evaluation set:
- Loss: 1.6546

## Model description

This is my first model ever and still a work in progress ;) 

## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6528        | 0.34  | 100  | 1.9249          |
| 1.964         | 0.68  | 200  | 1.7897          |
| 1.8695        | 1.02  | 300  | 1.7554          |
| 1.7922        | 1.35  | 400  | 1.7270          |
| 1.7747        | 1.69  | 500  | 1.7054          |
| 1.7473        | 2.03  | 600  | 1.7019          |
| 1.697         | 2.37  | 700  | 1.6868          |
| 1.6848        | 2.71  | 800  | 1.6810          |
| 1.6756        | 3.05  | 900  | 1.6779          |
| 1.6282        | 3.39  | 1000 | 1.6712          |
| 1.6285        | 3.73  | 1100 | 1.6626          |
| 1.6161        | 4.06  | 1200 | 1.6616          |
| 1.5887        | 4.4   | 1300 | 1.6588          |
| 1.5877        | 4.74  | 1400 | 1.6583          |
| 1.5723        | 5.08  | 1500 | 1.6560          |
| 1.5545        | 5.42  | 1600 | 1.6550          |
| 1.5415        | 5.76  | 1700 | 1.6540          |
| 1.5509        | 6.1   | 1800 | 1.6541          |
| 1.5326        | 6.44  | 1900 | 1.6539          |
| 1.5268        | 6.77  | 2000 | 1.6546          |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "pipeline_tag": "text2text-generation", "inference": {"parameters": {"max_length": 256, "num_beams": 4, "length_penalty": 1.5, "no_repeat_ngram_size": 3, "early_stopping": true}}, "model-index": [{"name": "t5-end2end-questions-generation-dutch", "results": []}]}
transformers pytorch t5 text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

t5-end2end-questions-generation-dutch
=====================================


This model is a fine-tuned version of yhavinga/t5-base-dutch on a
Google translated version of SQUAD 1.1 found here: URL


The code used to finetune the model is largely based on the work by Thomas Simonini. You can find his English model
here and his Google colab tutorial here


It achieves the following results on the evaluation set:


* Loss: 1.6546


Model description
-----------------


This is my first model ever and still a work in progress ;)


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: 0.0001
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 7


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2



### 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: 16
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 7 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 ### 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: 16
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 7 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
### 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: 16
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 7### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2