Text Classification
Transformers
Safetensors
Dutch
bert
dutch
multi-head regression
text quality
sequence classification
Eval Results (legacy)
text-embeddings-inference
Instructions to use Felixbrk/bert-base-dutch-cased-multi-score-text-only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Felixbrk/bert-base-dutch-cased-multi-score-text-only with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Felixbrk/bert-base-dutch-cased-multi-score-text-only")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Felixbrk/bert-base-dutch-cased-multi-score-text-only") model = AutoModelForSequenceClassification.from_pretrained("Felixbrk/bert-base-dutch-cased-multi-score-text-only", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57375426eacc285dc4b1573353e8f9d584a69167892d96d2758a872144df9b11
- Size of remote file:
- 437 MB
- SHA256:
- 6a0fdbee12bd2ff313c6e5051ead80c7dfc2bc56fffdbb76704a1c163c9d0c9d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.