Instructions to use JasperLS/gelectra-base-injection-pt_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JasperLS/gelectra-base-injection-pt_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JasperLS/gelectra-base-injection-pt_v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JasperLS/gelectra-base-injection-pt_v1") model = AutoModelForSequenceClassification.from_pretrained("JasperLS/gelectra-base-injection-pt_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bcf1e346177cad597cb1dee190b766ef41298664bdb609dbb7dcadc6ba5a3b26
- Size of remote file:
- 440 MB
- SHA256:
- a6df6a2560bf8bf8db8518605cbf27feb5a5c61f5ea4bd6efd7494ec6f1ee6e6
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