Instructions to use McGill-NLP/dpr-statcan-metadata_encoder-basic_and_member with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use McGill-NLP/dpr-statcan-metadata_encoder-basic_and_member with Transformers:
# Load model directly from transformers import AutoTokenizer, DPRContextEncoder tokenizer = AutoTokenizer.from_pretrained("McGill-NLP/dpr-statcan-metadata_encoder-basic_and_member") model = DPRContextEncoder.from_pretrained("McGill-NLP/dpr-statcan-metadata_encoder-basic_and_member", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7d9b3d04bf8010a08e87c857b6d9f9d7e9bb18ba6d104a2451316e026469f365
- Size of remote file:
- 438 MB
- SHA256:
- c5eacb63061dc0d2484506c30e98094811b9c04a3e573e1fd5f5080fb51e1b7b
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