Token Classification
Transformers
PyTorch
German
Spanish
English
xlm-roberta
politics
communication
public sphere
Instructions to use Sami92/XLM-PER-L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sami92/XLM-PER-L with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Sami92/XLM-PER-L")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Sami92/XLM-PER-L") model = AutoModelForTokenClassification.from_pretrained("Sami92/XLM-PER-L") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 94d5e433994c37a819c75583f658c2f0d3191089122ebedbcfbc297171de6bea
- Size of remote file:
- 2.24 GB
- SHA256:
- f4daafb48be351fd77582bbb1102f6cc5eafbaee22bb4ab6beca59fb219081b2
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