Instructions to use hf-tiny-model-private/tiny-random-ElectraForMaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-ElectraForMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-tiny-model-private/tiny-random-ElectraForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-ElectraForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-ElectraForMaskedLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0af56f83bc11b5e212fc089d3ae4d1bfbb09ca9af4acbba3d664131c1bd7df4d
- Size of remote file:
- 2.01 MB
- SHA256:
- 714e65759ff47eec146410fbfb89ce8bd048a2e6e85c530203b061bfab5c0da3
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