Token Classification
Transformers
PyTorch
Northern Sami
xlm-roberta
part-of-speech
Eval Results (legacy)
Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-sme 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-sme 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-sme")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sme") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sme", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 13d18c78389ec07a998e25c682fe59272281249ce9d57223f26ae3ac5ad95315
- Size of remote file:
- 1.11 GB
- SHA256:
- 224f6bb90418636580334082762943d9aa0ce1ef64576fe5cd449b6856217185
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.