Token Classification
Transformers
PyTorch
Safetensors
German
xlm-roberta
part-of-speech
Eval Results (legacy)
Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wietsedv/xlm-roberta-base-ft-udpos28-de with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="wietsedv/xlm-roberta-base-ft-udpos28-de")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-de") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-de") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 94c2db751cc62f303d5ba605c19b334da7f50beed2329b9e5c217ff7a4423bf2
- Size of remote file:
- 1.11 GB
- SHA256:
- 2f6171b1114abe08e63737cfed4aad0257230c474a524ee942904dac00ed1b1c
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