Instructions to use stanford-nlpxed/solution with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stanford-nlpxed/solution with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="stanford-nlpxed/solution")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("stanford-nlpxed/solution") model = AutoModelForSequenceClassification.from_pretrained("stanford-nlpxed/solution") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a34590015a446480712b026516a9c1b60cfc55a625c181ee6576e6ed0ba14d7b
- Size of remote file:
- 1.42 GB
- SHA256:
- b90a9e5b84dc423708330a64f9fb6fa91f7aebdac9897a1d0556d482c3faede0
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