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:
- ca4cbcc33ab0a68f4b6587b118a348786f2063798348a3a68a28e8176f21ade4
- Size of remote file:
- 3.26 GB
- SHA256:
- 789afae73a101871c5976627bdf36bae9c0194c9ab20c50be968a0a248659d9c
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