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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dae3d555cd75b28ed097105419739d069b9686c44bcb7b7504978e00e5b7647b
- Size of remote file:
- 560 MB
- SHA256:
- 8d32b5abb6d59ef0ac4a4726a20e361baa18ba3257f94194fbf6ddb625bd6ea5
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