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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7522ad27ba7e4f53f63c2cec252ab09b6211400a463650484d9e47ed1f0ccddf
- Size of remote file:
- 471 kB
- SHA256:
- 4f6b625fe0c8d01d48a38f364c6053dbcc2170dbbb0cc127c80a31c422d354c0
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