Instructions to use hf-tiny-model-private/tiny-random-DPRQuestionEncoder 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-DPRQuestionEncoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-DPRQuestionEncoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-DPRQuestionEncoder") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-DPRQuestionEncoder") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 650ec32e7d828135d8290642e63b22daa4eddc3ecc75c61ec46dd43d929589ae
- Size of remote file:
- 363 kB
- SHA256:
- 2db7f2fe9970bc900000f9ba49958cd60785f925e4918c6a83b878d6632c94c9
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