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:
- 7360b9f76b4d2d8cf30b2833a0f9a261a6498ef77be08dc6aae5b84c3348b0ad
- Size of remote file:
- 1.12 GB
- SHA256:
- 29009b7c2c5b85589ce163a9b31074e1e897ea3f2ea471e568df95f12a126608
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