

# ul2-small-dutch-finetuned-qgsquad-qgen

This model is a fine-tuned version of [ul2-small-dutch](https://huggingface.co/yhavinga/ul2-small-dutch) on the [Squad V2 Dutch](https://huggingface.co/datasets/yhavinga/squad_v2_dutch) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.49
- Rouge4 Precision: 0.03
- Rouge4 Recall: 0.027
- Rouge4 Fmeasure: 0.027

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

Epoch | Training Loss | Validation Loss | Rouge4 Precision | Rouge4 Recall | Rouge4 Fmeasure
------|---------------|-----------------|------------------|---------------|-----------------
 1 | 0.585900 | 0.512915 | 0.026600 | 0.023800 | 0.024100
 2 | 0.550200 | 0.498777 | 0.030300 | 0.027800 | 0.027900
 3 | 0.534800 | 0.493952 | 0.031700 | 0.028900 | 0.029100

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["yhavinga/squad_v2_dutch"], "widget": [{"text": "answer: Skal context: In 2022 zijn meer boeren begonnen met de overstap naar duurzaam ondernemen dan in eerdere jaren. Eind vorig jaar was ruim 6.500 hectare landbouwgrond in transitie naar een biologische aanpak, ruim 28 procent meer dan het jaar ervoor. Dat blijkt uit cijfers die Skal, toezichthouder op de biologische landbouw in Nederland, woensdag bekend heeft gemaakt. Het totale aandeel van biologische boeren in de landbouwsector steeg van 4,3 naar 4,4 procent. Boeren die nog bezig zijn met de overstap naar biologisch zijn daarin niet meegeteld. De cijfers roepen de vraag op of de transitie naar biologische landbouw snel genoeg gaat. Het kabinet maakte in december bekend dat 15 procent van de Nederlandse landbouwgrond in 2030 biologisch moet worden gebruikt. Om dat te bereiken moet elk jaar ruim 24.000 hectare grond overgaan van \u2018gangbare\u2019 naar biologische landbouw \u2013 bijna vier keer zo veel dus als in 2022 gebeurde."}], "model-index": [{"name": "ul2-small-dutch-finetuned-qgsquad-qgen", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer dataset:yhavinga/squad_v2_dutch license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-yhavinga/squad_v2_dutch #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

ul2-small-dutch-finetuned-qgsquad-qgen
======================================


This model is a fine-tuned version of ul2-small-dutch on the Squad V2 Dutch dataset.
It achieves the following results on the evaluation set:


* Loss: 0.49
* Rouge4 Precision: 0.03
* Rouge4 Recall: 0.027
* Rouge4 Fmeasure: 0.027


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: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5


### Training results




### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-yhavinga/squad_v2_dutch #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-yhavinga/squad_v2_dutch #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5### Training results