Instructions to use ModelTC/bart-base-qnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModelTC/bart-base-qnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ModelTC/bart-base-qnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ModelTC/bart-base-qnli") model = AutoModelForSequenceClassification.from_pretrained("ModelTC/bart-base-qnli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 79d8395251f0417ee06b2bb4cf24bdac9d372804202100bc9f3937feee18133e
- Size of remote file:
- 1.12 GB
- SHA256:
- 4d8711fb50d167d06006e992e2f08394b31b9787c73fc05c1b953e984baad415
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.