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:
- d43238565e1a0c1fc1819a4864dbd8c93a2f11a398d37490aecf8fb12b2d724c
- Size of remote file:
- 560 MB
- SHA256:
- 58c3fdbafdc9e00d90d9037f23346b6edcc5ac6bb56addbe9649a3e43adef004
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