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:
- daed613174fd2d1fb98c9917a67b480a3d6eb1c6f53573fd69017e1beed6c25a
- Size of remote file:
- 3.26 GB
- SHA256:
- 123dacb138656f6ba164ce5fcdcfa92f5927afd02a0cf77c3b0797a09bc3ab4a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.