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:
- 1f8d30e550e5141600588df7a30c9e1d2f6ef065f3a6e5b671115c63ab33454b
- Size of remote file:
- 1.63 GB
- SHA256:
- 7facb3174c12073540e82c2393c07ae10e69dca42132ba21ca14285a5755a7fa
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