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:
- 874ec54238429451dc09ef98e3b672ba12ff30f6fc09bc3853cc90a235d639d6
- Size of remote file:
- 3.26 GB
- SHA256:
- e8c6ac70c0c43ad38b476d43ef3bd175bea749dccba59382f916c58e7619436e
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