Instructions to use McGill-NLP/dpr-statcan-conversation_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-conversation_encoder-basic_and_member 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-basic_and_member")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("McGill-NLP/dpr-statcan-conversation_encoder-basic_and_member") model = AutoModel.from_pretrained("McGill-NLP/dpr-statcan-conversation_encoder-basic_and_member") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8332227e76330d92d572b9d1e4b0085667e7b2605a11d9809264adec0d46dec0
- Size of remote file:
- 438 MB
- SHA256:
- a4226b2f366366bb5eb001865de44134b6a70c1de3aeb0f26a0ea7c7d030d476
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