README.md

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# Model Card for Model ID

q/a model, structurally same as RobBERT-v2-nl-qa, but trained with an augmented dataset.
sentences from the context not containing the answer-span have been moved from before the 
answer to after the answer, and v.v.
The start of the answer has been adapted accordingly. these modified records have an "m" 
appended to their ID field.

## Model Details

Results seem better than RobBERT-v2-nl-qa:

    {'exact': 65.97542490405392,
     'f1': 73.36792208890036,
     'total': 31007,
     'HasAns_exact': 62.55334441399757,
     'HasAns_f1': 72.85023854321435,
     'HasAns_total': 22261,
     'NoAns_exact': 74.68557054653556,
     'NoAns_f1': 74.68557054653556,
     'NoAns_total': 8746,
     'best_exact': 65.97542490405392,
     'best_exact_thresh': 0.0,
     'best_f1': 73.3679220889002,
     'best_f1_thresh': 0.0}

### Model Description

example dutch question and context for the hosted inference api:

Q: Op welke wijze heeft de termiet zich kunnen verspreiden ?

CX: De koloniën zijn verspreid over twee woningen, bijgebouwen en tuinen in Zuid-Holland. 
Een van de panden is een groot kassencomplex. Daaruit zijn meerdere planten verkocht,
waardoor het mogelijk is dat de termiet zich al verder heeft verspreid. 
Eerdere pogingen om de koloniën uit te roeien zijn mislukt. 
De plantenverkoop vanuit het koloniegebied is inmiddels tijdelijk stopgezet.



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### Model Sources [optional]

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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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#### Training Hyperparameters

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#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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