---
language:
- nl
tags:
- text-classification
- pytorch
metrics:
- accuracy
- f1-score
extra_gated_prompt: 'Our models are intended for academic use only. If you are not
  affiliated with an academic institution, please provide a rationale for using our
  models.

  If you use our models for your work or research, please cite this paper: Sebők,
  M., Máté, Á., Ring, O., Kovács, V., & Lehoczki, R. (2024). Leveraging Open Large
  Language Models for Multilingual Policy Topic Classification: The Babel Machine
  Approach. Social Science Computer Review, 0(0). https://doi.org/10.1177/08944393241259434'
extra_gated_fields:
  Name: text
  Country: country
  Institution: text
  E-mail: text
  Use case: text
---
# xlm-roberta-large-dutch-cap-v3
## Model description
An `xlm-roberta-large` model fine-tuned on dutch training data labeled with [major topic codes](https://www.comparativeagendas.net/pages/master-codebook) from the [Comparative Agendas Project](https://www.comparativeagendas.net/).

## How to use the model

```python
from transformers import AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")
pipe = pipeline(
    model="poltextlab/xlm-roberta-large-dutch-cap-v3",
    task="text-classification",
    tokenizer=tokenizer,
    use_fast=False,
    token="<your_hf_read_only_token>"
)

text = "We will place an immediate 6-month halt on the finance driven closure of beds and wards, and set up an independent audit of needs and facilities."
pipe(text)
```

### Gated access
Due to the gated access, you must pass the `token` parameter when loading the model. In earlier versions of the Transformers package, you may need to use the `use_auth_token` parameter instead.

## Model performance
The model was evaluated on a test set of 6398 examples.<br>
Model accuracy is **0.83**.
| label        |   precision |   recall |   f1-score |   support |
|:-------------|------------:|---------:|-----------:|----------:|
| 0            |        0.81 |     0.77 |       0.79 |       471 |
| 1            |        0.7  |     0.72 |       0.71 |       148 |
| 2            |        0.88 |     0.8  |       0.84 |       242 |
| 3            |        0.76 |     0.87 |       0.81 |        78 |
| 4            |        0.76 |     0.78 |       0.77 |       374 |
| 5            |        0.9  |     0.92 |       0.91 |       248 |
| 6            |        0.86 |     0.75 |       0.8  |       155 |
| 7            |        0.79 |     0.86 |       0.82 |        95 |
| 8            |        0.86 |     0.82 |       0.84 |       217 |
| 9            |        0.88 |     0.9  |       0.89 |       244 |
| 10           |        0.85 |     0.87 |       0.86 |       763 |
| 11           |        0.73 |     0.75 |       0.74 |       319 |
| 12           |        0.79 |     0.83 |       0.81 |       121 |
| 13           |        0.75 |     0.77 |       0.76 |       378 |
| 14           |        0.82 |     0.83 |       0.83 |       123 |
| 15           |        0.7  |     0.75 |       0.72 |       106 |
| 16           |        0.39 |     0.58 |       0.47 |        19 |
| 17           |        0.93 |     0.92 |       0.93 |      1136 |
| 18           |        0.86 |     0.84 |       0.85 |       903 |
| 19           |        0.64 |     0.75 |       0.69 |        72 |
| 20           |        0.86 |     0.82 |       0.84 |       186 |
| macro avg    |        0.79 |     0.8  |       0.79 |      6398 |
| weighted avg |        0.84 |     0.83 |       0.83 |      6398 |

### Fine-tuning procedure
This model was fine-tuned with the following key hyperparameters:

- **Number of Training Epochs**: 10
- **Batch Size**: 8
- **Learning Rate**: 5e-06
- **Early Stopping**: enabled with a patience of 2 epochs

## Inference platform
This model is used by the [CAP Babel Machine](https://babel.poltextlab.com), an open-source and free natural language processing tool, designed to simplify and speed up projects for comparative research.  

## Cooperation
Model performance can be significantly improved by extending our training sets. We appreciate every submission of CAP-coded corpora (of any domain and language) at poltextlab{at}poltextlab{dot}com or by using the [CAP Babel Machine](https://babel.poltextlab.com).

## Reference
Sebők, M., Máté, Á., Ring, O., Kovács, V., & Lehoczki, R. (2024). Leveraging Open Large Language Models for Multilingual Policy Topic Classification: The Babel Machine Approach. Social Science Computer Review, 0(0). https://doi.org/10.1177/08944393241259434

## Debugging and issues
This architecture uses the `sentencepiece` tokenizer. In order to use the model before `transformers==4.27` you need to install it manually.

If you encounter a `RuntimeError` when loading the model using the `from_pretrained()` method, adding `ignore_mismatched_sizes=True` should solve the issue.