benjaminBeuster
/

xlm-roberta-base-ess-classification

@@ -1,199 +1,191 @@
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 ---
+language: en
+license: mit
+tags:
+- text-classification
+- xlm-roberta
+- survey-classification
+- European Social Survey
+datasets:
+- benjaminBeuster/ess_classification
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+base_model: FacebookAI/xlm-roberta-base
 ---
+# XLM-RoBERTa-Base for ESS Variable Classification
+Fine-tuned XLM-RoBERTa-Base model for classifying European Social Survey variables into 19 subject categories.
+## Model Description
+This model is a fine-tuned version of [`FacebookAI/xlm-roberta-base`](https://huggingface.co/FacebookAI/xlm-roberta-base) on the ESS variable classification dataset. It classifies survey questions and variables into predefined subject categories.
+- **Base Model**: XLM-RoBERTa-Base (125M parameters)
+- **Task**: Multi-class text classification (19 categories)
+- **Language**: English
+- **Dataset**: European Social Survey variables
+## Performance
+Evaluated on test set:
+- **Accuracy**: 0.8381
+- **Precision** (weighted): 0.7858
+- **Recall** (weighted): 0.8381
+- **F1-Score** (weighted): 0.7959
+- **Test samples**: 105
+## Intended Use
+This model is designed to automatically classify survey variables and questions from social science research into subject categories. It can be used for:
+- Organizing large survey datasets
+- Automating metadata generation
+- Subject classification of research questions
+- Data cataloging and discovery
+## Training Data
+The model was trained on the [`benjaminBeuster/ess_classification`](https://huggingface.co/datasets/benjaminBeuster/ess_classification) dataset, which contains survey variables extracted from European Social Survey DDI XML files.
+## Label Mapping
+The model predicts one of 19 subject categories:
+| Code | Category |
+|------|----------|
+| 0 | DEMOGRAPHY (POPULATION, VITAL STATISTICS, AND CENSUSES) |
+| 1 | ECONOMICS |
+| 2 | EDUCATION |
+| 3 | HEALTH |
+| 4 | HISTORY |
+| 5 | HOUSING AND LAND USE |
+| 6 | LABOUR AND EMPLOYMENT |
+| 7 | LAW, CRIME AND LEGAL SYSTEMS |
+| 8 | MEDIA, COMMUNICATION AND LANGUAGE |
+| 9 | NATURAL ENVIRONMENT |
+| 10 | OTHER |
+| 11 | POLITICS |
+| 12 | PSYCHOLOGY |
+| 13 | SCIENCE AND TECHNOLOGY |
+| 14 | SOCIAL STRATIFICATION AND GROUPINGS |
+| 15 | SOCIAL WELFARE POLICY AND SYSTEMS |
+| 16 | SOCIETY AND CULTURE |
+| 17 | TRADE, INDUSTRY AND MARKETS |
+| 18 | TRANSPORT AND TRAVEL |
+## Usage
+### Basic Classification
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+# Load model and tokenizer
+model_name = "benjaminBeuster/xlm-roberta-base-ess-classification"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+# Create classification pipeline
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+# Classify a survey question
+text = "Trust in country's parliament. Using this card, please tell me on a score of 0-10 how much you personally trust each of the institutions I read out."
+result = classifier(text)
+print(f"Category: {result[0]['label']}")
+print(f"Confidence: {result[0]['score']:.4f}")
+```
+### Batch Classification
+```python
+# Classify multiple questions
+questions = [
+    "How often pray apart from at religious services",
+    "Highest level of education completed",
+    "Trust in politicians"
+]
+results = classifier(questions)
+for question, result in zip(questions, results):
+    print(f"{question[:50]}: {result['label']} ({result['score']:.2f})")
+```
+### Manual Prediction
+```python
+import torch
+# Tokenize input
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
+# Get predictions
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_class = torch.argmax(predictions, dim=-1).item()
+# Get label name
+label = model.config.id2label[predicted_class]
+confidence = predictions[0][predicted_class].item()
+print(f"Predicted: {label} (confidence: {confidence:.4f})")
+```
+## Training Procedure
+### Training Hyperparameters
+- **Learning rate**: 2e-05
+- **Batch size**: 8
+- **Epochs**: 5
+- **Weight decay**: 0.01
+- **Warmup ratio**: 0.1
+- **Max sequence length**: 256
+- **Optimizer**: AdamW
+- **LR scheduler**: Linear with warmup
+### Training Details
+The model was fine-tuned using the Hugging Face Transformers library with the following setup:
+- Early stopping with patience of 2 epochs
+- Evaluation on validation set after each epoch
+- Best model selection based on validation loss
+- Mixed precision training (fp16/bf16 where supported)
+## Limitations and Bias
+- The model is trained on a relatively small dataset (50 samples), which may limit generalization
+- Performance may vary on survey questions outside the European Social Survey domain
+- The model may inherit biases present in the training data
+- English-language surveys are the primary focus, though the base model supports 100 languages
+## Citation
+If you use this model, please cite:
+```bibtex
+@misc{xlm-roberta-ess-classifier,
+  author = {Benjamin Beuster},
+  title = {XLM-RoBERTa-Large for ESS Variable Classification},
+  year = {2025},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/benjaminBeuster/xlm-roberta-base-ess-classification}
+}
+```
+## Model Card Authors
+Benjamin Beuster
 ## Model Card Contact
+For questions or feedback, please open an issue on the [model repository](https://huggingface.co/benjaminBeuster/xlm-roberta-base-ess-classification).