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-9002/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-9002/optimizer.pt
- Command line
-
hf download hf://xshubhamx/bart-base/training_checkpoints/checkpoint-9002/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/xshubhamx/bart-base/resolve/main/training_checkpoints/checkpoint-9002/optimizer.pt
1.12 GB
- Xet hash:
- 4cdeac2d61ab23595456bb296e7a85e071d535578b83261e42cf5a87f3dfce51
- Size of remote file:
- 1.12 GB
- SHA256:
- f64e2b3e51337ee30907a9dfbc5fa64632f1bb74dcb6a21a57e4f9d2fe562df6
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