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:
- 70c5d995223b021fb4bd20c6c05c55ad1d9b67bcf0d689c87eed4023001fc739
- Size of remote file:
- 3.26 GB
- SHA256:
- d67bb3009425efd8cfd6547bd722cb28cb44a6ca49ab3d2d53d710c2c70a8bc5
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