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:
- b859865e3abd262ad74dadca1aa490c46da6cff7a5175d5418fd0f1ba26429b6
- Size of remote file:
- 2.99 kB
- SHA256:
- deb6d415dd7293fb9fc7ecae0a9a429f94eed07075dd532bf210eeb896bec7a9
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