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:
- 25d7b96e20dbb8a4df150f1c7de2c857177ee51d22b9bb03a4738dd7f9660cd8
- Size of remote file:
- 1.12 GB
- SHA256:
- 15658f7e1de09abaa810c2529e11952fb61e5da8b42a75bbfc038cd6cfc4b5a0
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