Instructions to use Borsa356/paws_multiling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Borsa356/paws_multiling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Borsa356/paws_multiling")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Borsa356/paws_multiling") model = AutoModelForSequenceClassification.from_pretrained("Borsa356/paws_multiling") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3add3ed3bcea2276521112f4c19891a32864a895c765a177d2e1beebee1db8cc
- Size of remote file:
- 471 MB
- SHA256:
- e66b475d088d072b796e8fa1e6fb3baeb128470c82f4b4ec31a6d908193958f2
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