Token Classification
Transformers
PyTorch
Safetensors
English
xlm-roberta
part-of-speech
Eval Results (legacy)
Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-en 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-en 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-en")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-en") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-en") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5ecdd902b719548b923b92806a1fc63e7aef3bce7e25d67dae9a07b00214cd9d
- Size of remote file:
- 1.11 GB
- SHA256:
- 948dd0376764321d2cec719cd7c5eef21f9c7716fa5da6e17314e793f52b5e46
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.