Instructions to use deepset/gelectra-base-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/gelectra-base-generator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="deepset/gelectra-base-generator")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("deepset/gelectra-base-generator") model = AutoModelForMaskedLM.from_pretrained("deepset/gelectra-base-generator", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 401852d7a39a457d94787b34a89aca57a25502b8d9c4315f0df334b48b7f2369
- Size of remote file:
- 234 MB
- SHA256:
- 00bada437ce1da08b345ff7270fd9f3901d9559e7ca43bde61bc895623e401b7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.