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---
---
language:
- en
tags:
- text-classification
- pytorch
metrics:
- accuracy
- f1-score
---
# xlm-roberta-large-english-cap-v3
## Model description
An `xlm-roberta-large` model fine-tuned on english 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
This snippet prints the three most probable labels and their corresponding softmax scores:
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("poltextlab/xlm-roberta-large-english-cap-v3")
tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")
sentence = "This is an example."
inputs = tokenizer(sentence,
return_tensors="pt",
max_length=512,
padding="do_not_pad",
truncation=True
)
logits = model(**inputs).logits
probs = torch.softmax(logits, dim=1).tolist()[0]
probs = {model.config.id2label[index]: round(probability, 2) for index, probability in enumerate(probs)}
top3_probs = dict(sorted(probs.items(), key=lambda item: item[1], reverse=True)[:3])
print(top3_probs)
```
## Model performance
The model was evaluated on a test set of 91823 examples.<br>
Model accuracy is **0.84**.
| label | precision | recall | f1-score | support |
|:-------------|------------:|---------:|-----------:|----------:|
| 0 | 0.75 | 0.8 | 0.77 | 4671 |
| 1 | 0.79 | 0.74 | 0.76 | 2288 |
| 2 | 0.88 | 0.89 | 0.89 | 5732 |
| 3 | 0.87 | 0.87 | 0.87 | 2611 |
| 4 | 0.81 | 0.78 | 0.79 | 2819 |
| 5 | 0.86 | 0.89 | 0.88 | 3999 |
| 6 | 0.83 | 0.84 | 0.84 | 3044 |
| 7 | 0.9 | 0.84 | 0.87 | 2547 |
| 8 | 0.82 | 0.83 | 0.83 | 912 |
| 9 | 0.87 | 0.89 | 0.88 | 4322 |
| 10 | 0.84 | 0.85 | 0.85 | 6558 |
| 11 | 0.83 | 0.8 | 0.81 | 2965 |
| 12 | 0.74 | 0.82 | 0.78 | 1955 |
| 13 | 0.81 | 0.82 | 0.82 | 5422 |
| 14 | 0.83 | 0.82 | 0.83 | 6636 |
| 15 | 0.82 | 0.78 | 0.8 | 1580 |
| 16 | 0.87 | 0.85 | 0.86 | 2425 |
| 17 | 0.76 | 0.82 | 0.79 | 5700 |
| 18 | 0.86 | 0.82 | 0.84 | 10700 |
| 19 | 0.85 | 0.88 | 0.87 | 4761 |
| 20 | 0.76 | 0.76 | 0.76 | 2622 |
| 21 | 0.93 | 0.91 | 0.92 | 7554 |
| macro avg | 0.83 | 0.83 | 0.83 | 91823 |
| weighted avg | 0.84 | 0.84 | 0.84 | 91823 |
### 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.
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