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-643/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-643/optimizer.pt
- Command line
-
hf download hf://xshubhamx/bart-base/training_checkpoints/checkpoint-643/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/xshubhamx/bart-base/resolve/main/training_checkpoints/checkpoint-643/optimizer.pt
1.12 GB
- Xet hash:
- b371d48363a6c01cf28c9ee0be3882ebb21cc950b61996ffeffce456832e1f99
- Size of remote file:
- 1.12 GB
- SHA256:
- 38db39bc543348f050edf50e526b52da3c47327207e242407dee607b4c6a56f3
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