Instructions to use JW17/L32-3B-UC-BatchSum-seed78 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JW17/L32-3B-UC-BatchSum-seed78 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JW17/L32-3B-UC-BatchSum-seed78")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JW17/L32-3B-UC-BatchSum-seed78") model = AutoModelForSequenceClassification.from_pretrained("JW17/L32-3B-UC-BatchSum-seed78") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 49e8475964f252eba17b9a2c9298b356da0a95ba46c0fd39fed2c2e303d97242
- Size of remote file:
- 2.92 GB
- SHA256:
- 6d4bf200850887f9d33a757920a1d71961b3fc88b299cc90df07319b08e2ee2b
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