Token Classification
Transformers
PyTorch
Nigerian Pidgin
xlm-roberta
part-of-speech
Eval Results (legacy)
Instructions to use wietsedv/xlm-roberta-base-ft-udpos28-pcm 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-pcm 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-pcm")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-pcm") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-pcm") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5ab3bf90456d40b79aa5bc1e37e731ab18458aa7b6b4091a949ffdad491aa7ba
- Size of remote file:
- 1.11 GB
- SHA256:
- 78356a9efea047a67a3d6c9b60e705119f28d521b50350295c148ab09217f680
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