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:
- e56a5ab5c957a7514ebae4217a01905b5ff1461ccb3d0a94d8a93a8e3ee9cd3c
- Size of remote file:
- 1.06 MB
- SHA256:
- 5a2e346ba80b147492055c677a0f03be037defea9aa2c8434e193463dad0a5e2
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