Text Classification
Transformers
Safetensors
Dutch
roberta
generated from trainer
text-embeddings-inference
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("WideMan/robbert_dutch-cola")
model = AutoModelForSequenceClassification.from_pretrained("WideMan/robbert_dutch-cola", device_map="auto")Quick Links
Model Card for Model ID
This model is a fine-tuned version of pdelobelle/robbert-v2-dutch-base on GroNLP/dutch-cola. It achieves the following results on the evaluation set:
- Accuracy: 0.7879
Training Hyperparameters
The following hyperparameters were used during training:
- learning rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- num_epochs=2
- FP16 = True
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="WideMan/robbert_dutch-cola")