nl base_model:google-bert/bert-base-uncased base_model:finetune:google-bert/bert-base-uncased license:wtfpl region:us
---
base_model: google-bert/bert-base-uncased
language:
- nl
license: wtfpl
---

# Model Ariving Soon, Still Training

# Model Card: `bert-dutch-finetuned`

## Model Description

**Model Name**: `bert-dutch-finetuned`  
**Model Type**: `BERT (Bidirectional Encoder Representations from Transformers)`  
**Base Model**: `bert-base-cased`  
**Language**: Dutch (Nederlands)  
**Task**: Masked Language Modeling (MLM), Text Classification, and other NLP tasks.

This model is a fine-tuned version of the `bert-base-cased` model, adapted specifically for the Dutch language. It is pre-trained on a large Dutch corpus from [OSCAR](https://oscar-project.org/) and other Dutch datasets. The model is capable of understanding and generating text in Dutch and can be further fine-tuned for specific downstream NLP tasks like Named Entity Recognition (NER), Sentiment Analysis, etc.

## Intended Use

The `bert-dutch-finetuned` model can be used for various NLP tasks in Dutch, including:

- Masked Language Modeling (MLM)
- Text Classification
- Named Entity Recognition (NER)
- Question Answering (QA)
- Text Summarization

This model is ideal for researchers and practitioners working on Dutch NLP applications.

## How to Use

To use this model with the Hugging Face `transformers` library:

```python
from transformers import BertTokenizer, BertForMaskedLM

# Load model and tokenizer
tokenizer = BertTokenizer.from_pretrained("your-username/bert-dutch-finetuned")
model = BertForMaskedLM.from_pretrained("your-username/bert-dutch-finetuned")

# Example usage
inputs = tokenizer("Dit is een voorbeeldzin in het Nederlands.", return_tensors="pt")
outputs = model(**inputs)
```
## Training Data

The model was trained on a large Dutch corpus consisting of various publicly available datasets, such as:

- **OSCAR (Open Super-large Crawled ALMAnaCH Corpus):** A multilingual corpus obtained by language classification and filtering of the Common Crawl dataset.
- **Dutch Wikipedia Dumps:** A collection of Dutch Wikipedia pages.

The training data includes diverse text types, covering a wide range of topics to ensure robust language understanding.

## Training Procedure

The model was fine-tuned using the following setup:

- **Base Model:** `bert-base-cased`
- **Training Objective:** Masked Language Modeling (MLM)
- **Optimizer:** AdamW
- **Learning Rate:** 5e-5
- **Batch Size:** 8
- **Epochs:** 3
- **Hardware Used:** A GPU-enabled environment (NVIDIA V100)

## Evaluation

The model was evaluated on a validation set split from the same training corpus. The evaluation metrics included:

- **Perplexity** for Masked Language Modeling
- **Accuracy** for Text Classification tasks (if applicable)

The model performs well on standard Dutch text understanding tasks but might require further fine-tuning for specific downstream applications.

## Limitations and Biases

- The model may exhibit biases present in the training data. This includes potential social biases or stereotypes embedded in large web-scraped datasets like OSCAR.
- The model's performance is optimized for Dutch and may not generalize well to other languages.
- It may not perform well on domain-specific tasks without additional fine-tuning.

## Ethical Considerations

Users should be aware of the biases that might be present in the model outputs. It is recommended to conduct a bias assessment before deploying the model in sensitive applications, especially those related to decision-making.

## Acknowledgments

This model was built using the Hugging Face `transformers` library and fine-tuned on the OSCAR and Dutch Wikipedia datasets. Special thanks to the creators and maintainers of these resources.

## Citation

If you use this model in your research or applications, please consider citing:

```bibtex
@misc{bert-dutch-finetuned,
  author = DJ Ober,
  title = BERT Fine-Tuned for Dutch Language,
  year = 2024,
  howpublished = https://huggingface.co/dober123/bert-dutch-finetuned,
}
```
---
license: wtfpl
---