Instructions to use hf-internal-testing/tiny-random-DPRQuestionEncoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-DPRQuestionEncoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-DPRQuestionEncoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-DPRQuestionEncoder") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-DPRQuestionEncoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4d86a4bbede41a64bd947b33dfc591b3bb6d41b67d2bfa86eb22dafce89a7953
- Size of remote file:
- 381 kB
- SHA256:
- 03d97622fb809034375e280f385c15f3d0a07478d8143b5d6e736c8c9270f83e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.