Instructions to use JW17/L32-1B-UC-BatchSum-seed90 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JW17/L32-1B-UC-BatchSum-seed90 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JW17/L32-1B-UC-BatchSum-seed90")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JW17/L32-1B-UC-BatchSum-seed90") model = AutoModelForSequenceClassification.from_pretrained("JW17/L32-1B-UC-BatchSum-seed90") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1d2e8fd4a43ceef057761d616160b1b24da8bdf7b39dd40a00dec665ebe84d11
- Size of remote file:
- 5.69 kB
- SHA256:
- 41fba0b8415acc0305ef16b279e1d55dc0ec8fe10f209723785b6828d705c35d
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