Instructions to use xshubhamx/bart-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xshubhamx/bart-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xshubhamx/bart-large")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xshubhamx/bart-large") model = AutoModelForSequenceClassification.from_pretrained("xshubhamx/bart-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7027e2ff781887c12246797515d8c084a2866324e7a604832a386e1e52604c90
- Size of remote file:
- 3.26 GB
- SHA256:
- d85929c582205d122f87dc90992f193d207d88e038774f5039470e27c06006cc
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