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:
- 3f6d2fc03a12fc611ff63abbdd1911f8340d13913d45d302c4d6d4bb23ecf5b2
- Size of remote file:
- 438 MB
- SHA256:
- 15a29a1baa2183d05c5f5f2e742ac25be47996931c991b54f58699eee61bc2b4
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