Instructions to use fspanda/electra-medical-small-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fspanda/electra-medical-small-generator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="fspanda/electra-medical-small-generator")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("fspanda/electra-medical-small-generator") model = AutoModelForMaskedLM.from_pretrained("fspanda/electra-medical-small-generator") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f407ab14f5e41dbfd41cab93704398f50abf52898f35e7b19f8424432ad882ed
- Size of remote file:
- 54.3 MB
- SHA256:
- 857c19de12cdde676493eabee87de79137561b2fb5a8df21851cea80a9312a16
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