Text Classification
Transformers
Safetensors
Dutch
bert
dutch
regression
text-quality
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Felixbrk/bert-base-dutch-cased-simple-score-text-only")
model = AutoModelForSequenceClassification.from_pretrained("Felixbrk/bert-base-dutch-cased-simple-score-text-only", device_map="auto")Quick Links
🚀 transformer_y_quality — Dutch BERT for Text Quality Regression
Model: GroNLP/bert-base-dutch-cased finetuned for regression
Task: Predict y_quality_simple (text quality score)
Language: Dutch 🇳🇱
Problem type: Single output regression
📈 Performance
| Epoch | Train Loss | Val Loss | RMSE | R² |
|---|---|---|---|---|
| 1 | 0.007200 | 0.007010 | 0.0837 | 0.8117 |
| 2 | 0.005500 | 0.006300 | 0.0794 | 0.8307 |
| 3 | 0.004600 | 0.006079 | 0.0780 | 0.8367 |
| 4 | 0.003300 | 0.006122 | 0.0782 | 0.8355 |
| 5 | 0.002600 | 0.006891 | 0.0830 | 0.8149 |
✅ Final Test Metrics:
- RMSE: 0.0780
- R²: 0.8367
⚙️ How to use
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("YourUsername/transformer_y_quality")
model = AutoModelForSequenceClassification.from_pretrained("YourUsername/transformer_y_quality")
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Evaluation results
- RMSE on Custom Datasetself-reported0.078
- R² on Custom Datasetself-reported0.837
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Felixbrk/bert-base-dutch-cased-simple-score-text-only")