Instructions to use hf-tiny-model-private/tiny-random-ElectraForQuestionAnswering 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-ElectraForQuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hf-tiny-model-private/tiny-random-ElectraForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-ElectraForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-tiny-model-private/tiny-random-ElectraForQuestionAnswering", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 61329c00ff6393e70b677c75b30dcdff5a3037ce67d349c4c7ea2159940ea8c8
- Size of remote file:
- 1.03 MB
- SHA256:
- e87562f3129a8b3b07986d9c2f4f5c4c7590e0f68350c6cd4af67ae21e28bf2b
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