Token Classification
Transformers
PyTorch
Safetensors
Greek
xlm-roberta
part-of-speech
Eval Results (legacy)
Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-el 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-el 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-el")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-el") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-el", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9d3e1289f6dba94566feb8d4fc3256129fbae038f80609dd03f377cf17d58f8a
- Size of remote file:
- 1.11 GB
- SHA256:
- 297e3e3c0ac74f64854bf353f138dad5ad668fe95b14d912ef87ff147ee74be3
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