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:
- 42fdc7e63100c2020a7e3254b2b529d80ed2cf52780cbb915ca6f5e2906dcdfe
- Size of remote file:
- 1.12 GB
- SHA256:
- ab5a7f2428cc6a8c65f8c19c2fc5c0f4135ce175cd3b5897a4bc863155752010
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.