Instructions to use hf-internal-testing/tiny-random-ElectraForMaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ElectraForMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-internal-testing/tiny-random-ElectraForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-ElectraForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-internal-testing/tiny-random-ElectraForMaskedLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 569e31a7cf49b6b0b1ada25299e6fff532110b0ac23803679f9ba28652cb7e1d
- Size of remote file:
- 2.01 MB
- SHA256:
- 0522e78e64ce449cce5017c8f87f7dada5dafa136b9cc5f29c79912537950b19
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