Instructions to use soschuetze/blm-bart-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use soschuetze/blm-bart-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="soschuetze/blm-bart-binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("soschuetze/blm-bart-binary") model = AutoModelForSequenceClassification.from_pretrained("soschuetze/blm-bart-binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 04f45394750f44865847e7d40a9f2bac6485b496add9dfc4178067d34b3f2342
- Size of remote file:
- 433 MB
- SHA256:
- ff096b4dff1ce423c8728d9da13fc4fbb09458b9329595ae72b62ccffd6b5ee5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.