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")# pip install -U transformers accelerate # 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
Download training_checkpoints/checkpoint-9645/optimizer.pt from xshubhamx/bart-base: direct link, hf CLI and curl.
- Browser
- Download file 1.12 GB
-
https://huggingface.co/xshubhamx/bart-base/resolve/main/training_checkpoints/checkpoint-9645/optimizer.pt
- Command line
-
hf download hf://xshubhamx/bart-base/training_checkpoints/checkpoint-9645/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/xshubhamx/bart-base/resolve/main/training_checkpoints/checkpoint-9645/optimizer.pt
1.12 GB
- Xet hash:
- 23b13cfcbb4b9ea57b76430e30f60c0092fd418b96479045351add14b5ebc6b6
- Size of remote file:
- 1.12 GB
- SHA256:
- adec737225bb91fa4f4ec1644e43db4b4bf1cb90347565f203af61963a92ef28
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