Instructions to use McGill-NLP/dpr-statcan-conversation_encoder-title with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use McGill-NLP/dpr-statcan-conversation_encoder-title with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="McGill-NLP/dpr-statcan-conversation_encoder-title")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("McGill-NLP/dpr-statcan-conversation_encoder-title") model = AutoModel.from_pretrained("McGill-NLP/dpr-statcan-conversation_encoder-title") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0ef13410290f0ef1bdb6ad5081e58386d4dfb628f51f2b97f3d08762b2ed5134
- Size of remote file:
- 438 MB
- SHA256:
- 8101f0c3f6408a1fd3dce33077569d6c99342065d6a4b94594dfc8529ccc98b8
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