Instructions to use AyoubChLin/BART-mnli_cnn_256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AyoubChLin/BART-mnli_cnn_256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="AyoubChLin/BART-mnli_cnn_256")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AyoubChLin/BART-mnli_cnn_256") model = AutoModelForSequenceClassification.from_pretrained("AyoubChLin/BART-mnli_cnn_256", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9973ce7d854278b6b7b43e014d0013501902a5c3cab6b7df68b3c0a368d467f8
- Size of remote file:
- 3.32 kB
- SHA256:
- 6562bfde0c91dc21882b1f84a2bbf5bf9518fcc597bcc0dcd032dbe7a1bdaa2f
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