Instructions to use McGill-NLP/tapas-statcan-large-conversation_encoder-title with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use McGill-NLP/tapas-statcan-large-conversation_encoder-title with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="McGill-NLP/tapas-statcan-large-conversation_encoder-title")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("McGill-NLP/tapas-statcan-large-conversation_encoder-title") model = AutoModel.from_pretrained("McGill-NLP/tapas-statcan-large-conversation_encoder-title") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 877045b689251a3dcf8681c46d70809489e76f058679c410a0e6818d2b96ec19
- Size of remote file:
- 1.35 GB
- SHA256:
- b1a8d3fa8a5377121794edaab078d4ea72def9dc53dc23ad49842bc332e955cf
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