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---
---
license: mit
language:
- multilingual
tags:
- zero-shot-classification
- text-classification
- pytorch
metrics:
- accuracy
- f1-score
---
# xlm-roberta-large-social-cap-v3
## Model description
An `xlm-roberta-large` model finetuned on multilingual training data containing texts of the `social` domain labelled 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
#### Loading and tokenizing input data
```python
import pandas as pd
import numpy as np
from datasets import Dataset
from transformers import (AutoModelForSequenceClassification, AutoTokenizer,
Trainer, TrainingArguments)
CAP_NUM_DICT = {0: '1', 1: '2', 2: '3', 3: '4', 4: '5', 5: '6',
6: '7', 7: '8', 8: '9', 9: '10', 10: '12', 11: '13', 12: '14',
13: '15', 14: '16', 15: '17', 16: '18', 17: '19', 18: '20', 19:
'21', 20: '23', 21: '999'}
tokenizer = AutoTokenizer.from_pretrained('xlm-roberta-large')
num_labels = len(CAP_NUM_DICT)
def tokenize_dataset(data : pd.DataFrame):
tokenized = tokenizer(data["text"],
max_length=MAXLEN,
truncation=True,
padding="max_length")
return tokenized
hg_data = Dataset.from_pandas(data)
dataset = hg_data.map(tokenize_dataset, batched=True, remove_columns=hg_data.column_names)
```
#### Inference using the Trainer class
```python
model = AutoModelForSequenceClassification.from_pretrained('poltextlab/xlm-roberta-large-social-cap-v3',
num_labels=22,
problem_type="multi_label_classification",
ignore_mismatched_sizes=True
)
training_args = TrainingArguments(
output_dir='.',
per_device_train_batch_size=8,
per_device_eval_batch_size=8
)
trainer = Trainer(
model=model,
args=training_args
)
probs = trainer.predict(test_dataset=dataset).predictions
predicted = pd.DataFrame(np.argmax(probs, axis=1)).replace({0: CAP_NUM_DICT}).rename(
columns={0: 'predicted'}).reset_index(drop=True)
```
### Fine-tuning procedure
`xlm-roberta-large-social-cap-v3` was fine-tuned using the Hugging Face Trainer class with the following hyperparameters:
```python
training_args = TrainingArguments(
output_dir=f"../model/{model_dir}/tmp/",
logging_dir=f"../logs/{model_dir}/",
logging_strategy='epoch',
num_train_epochs=10,
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
learning_rate=5e-06,
seed=42,
save_strategy='epoch',
evaluation_strategy='epoch',
save_total_limit=1,
load_best_model_at_end=True
)
```
We also incorporated an EarlyStoppingCallback in the process with a patience of 2 epochs.
## Model performance
The model was evaluated on a test set of 3488 examples (10% of the available data).<br>
Model accuracy is **0.74**.
| label | precision | recall | f1-score | support |
|:-------------|------------:|---------:|-----------:|----------:|
| 0 | 0.69 | 0.71 | 0.7 | 184 |
| 1 | 0.65 | 0.61 | 0.63 | 167 |
| 2 | 0.7 | 0.72 | 0.71 | 105 |
| 3 | 0.88 | 0.82 | 0.85 | 88 |
| 4 | 0.79 | 0.75 | 0.77 | 145 |
| 5 | 0.84 | 0.89 | 0.86 | 186 |
| 6 | 0.79 | 0.83 | 0.81 | 169 |
| 7 | 0.95 | 0.78 | 0.86 | 73 |
| 8 | 0.86 | 0.79 | 0.82 | 357 |
| 9 | 0.86 | 0.88 | 0.87 | 221 |
| 10 | 0.79 | 0.81 | 0.8 | 296 |
| 11 | 0.66 | 0.8 | 0.72 | 239 |
| 12 | 0.69 | 0.57 | 0.62 | 77 |
| 13 | 0.74 | 0.48 | 0.58 | 58 |
| 14 | 0.57 | 0.65 | 0.61 | 31 |
| 15 | 0.7 | 0.42 | 0.53 | 45 |
| 16 | 0 | 0 | 0 | 8 |
| 17 | 0.48 | 0.46 | 0.47 | 89 |
| 18 | 0.69 | 0.7 | 0.69 | 527 |
| 19 | 0.71 | 0.7 | 0.71 | 103 |
| 20 | 0.74 | 0.69 | 0.72 | 108 |
| 21 | 0.63 | 0.72 | 0.68 | 212 |
| macro avg | 0.7 | 0.67 | 0.68 | 3488 |
| weighted avg | 0.74 | 0.74 | 0.74 | 3488 |
## 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).
## Debugging and issues
This architecture uses the `sentencepiece` tokenizer. In order to run 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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