Instructions to use alvp/alberti-stanzas with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alvp/alberti-stanzas with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="alvp/alberti-stanzas")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("alvp/alberti-stanzas") model = AutoModelForSequenceClassification.from_pretrained("alvp/alberti-stanzas") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0258aca935cd7896a12310a44ef0bc5a789bf02f687b2002f68a3d906de06ef3
- Size of remote file:
- 712 MB
- SHA256:
- f8e462c9683ef5745a68dc97ee36e098008e80a02cd4d48d92b9858ec480a974
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