Instructions to use JasperLS/gelectra-base-injection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JasperLS/gelectra-base-injection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JasperLS/gelectra-base-injection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JasperLS/gelectra-base-injection") model = AutoModelForSequenceClassification.from_pretrained("JasperLS/gelectra-base-injection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 067193900f2044a93b51dd805d5593e359468b35ccd0353e2bfe98c2d921d48b
- Size of remote file:
- 3.58 kB
- SHA256:
- 3870989f3f09f1eed3fd066e44f9bbd8536a526460a468fdbb0a98332e2b1a15
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