Instructions to use xshubhamx/bart-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xshubhamx/bart-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xshubhamx/bart-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xshubhamx/bart-base") model = AutoModelForSequenceClassification.from_pretrained("xshubhamx/bart-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d97881913571c8572a351d88cf591db0c33238a8d65daf14771eaa759511cc6a
- Size of remote file:
- 1.12 GB
- SHA256:
- 9c18095039d5448c9d1d433680c58aef592f70d9fa5a1a7747f5775fc6dbeb35
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