Instructions to use ModelTC/bart-base-qqp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModelTC/bart-base-qqp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ModelTC/bart-base-qqp")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ModelTC/bart-base-qqp") model = AutoModelForSequenceClassification.from_pretrained("ModelTC/bart-base-qqp") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 60e42883695d39488b90e3bc38ee374b78df0459b052dfe5fb7c2c554340e61d
- Size of remote file:
- 14.7 kB
- SHA256:
- a36bf66c9d9314a6035a9c8f3a9c6d17f6af6077e09f7fc342e8c5ddcba4126b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.