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:
- fe79c66c5001615aceaa88f29279f159da7fef87ecc253eb30f040e6edf66668
- Size of remote file:
- 3.26 GB
- SHA256:
- a930f7a7ae0d88ba2caecd5cd5a32997c70813364dd7539dfa767363fdb9dd4f
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