Instructions to use nbroad/sciwiki-e5-sm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nbroad/sciwiki-e5-sm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nbroad/sciwiki-e5-sm")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nbroad/sciwiki-e5-sm") model = AutoModelForSequenceClassification.from_pretrained("nbroad/sciwiki-e5-sm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 857a8ffdda896c012d2b48f88ac10faad5b4ab0ab6687c9c97b0cbc5f9eb054f
- Size of remote file:
- 134 MB
- SHA256:
- 0a8b9cfe6879e508eff89a88100500d095f8f4626a0f5456ce262f21963a5b2a
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