Token Classification
Transformers
PyTorch
Safetensors
Ancient Greek (to 1453)
xlm-roberta
part-of-speech
Eval Results (legacy)
Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-grc 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-grc 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-grc")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-grc") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-grc") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5c36bde98cc6d03487b6d4149e12cd6c91ac867d65182d80ab26de7da5728dd1
- Size of remote file:
- 1.11 GB
- SHA256:
- 1df329064af4a5cd08d1856d1a62a15d4c971707878a90d93dfdcfedd75aa498
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