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") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from JasperLS/gelectra-base-injection: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/JasperLS/gelectra-base-injection/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://JasperLS/gelectra-base-injection@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/JasperLS/gelectra-base-injection/resolve/refs%2Fpr%2F1/pytorch_model.bin
440 MB
- Xet hash:
- cdfceb32b29862affda23ec28fdb45993ce80abb1e344d4d714c490cd142d883
- Size of remote file:
- 440 MB
- SHA256:
- de7dd41c5bc15774aa935a2b27f738484959795001be073a5541d16ac15df05c
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