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:
- 436a65a97d52d19da349fc2fb84677e99c1d36f3303ac7346d5b58c17d1582b0
- Size of remote file:
- 1.12 GB
- SHA256:
- bcb730a0cf2d95b3801c8e725f38db18872d85ecfcc3ef79590b444f306c8024
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