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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b9fcdcbea426ff8072a7b122cb1178ae6d8d0248ac37a9ce1b6dabd70bc839fe
- Size of remote file:
- 1.12 GB
- SHA256:
- 2707878876f58372c902d3bb32adefb48b0e8f46c9565ffe67edcb36c343fd89
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