Instructions to use jcblaise/electra-tagalog-small-cased-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jcblaise/electra-tagalog-small-cased-generator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jcblaise/electra-tagalog-small-cased-generator")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jcblaise/electra-tagalog-small-cased-generator") model = AutoModelForMaskedLM.from_pretrained("jcblaise/electra-tagalog-small-cased-generator", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bda23dfaf55e8da4da2d502f5e2cea5747e6307cc4a435f3ca89ef8fdff8d131
- Size of remote file:
- 19.3 MB
- SHA256:
- 4cdd18af5f383899f93658324c7b625b2e533be26a80dfe1e9104b845096f4c3
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