Instructions to use McGill-NLP/tapas-statcan-large-metadata_encoder-cell_tokens with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use McGill-NLP/tapas-statcan-large-metadata_encoder-cell_tokens with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="McGill-NLP/tapas-statcan-large-metadata_encoder-cell_tokens")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("McGill-NLP/tapas-statcan-large-metadata_encoder-cell_tokens") model = AutoModel.from_pretrained("McGill-NLP/tapas-statcan-large-metadata_encoder-cell_tokens") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3b7f786365e9f4815713fbfff5cf73799275ff120f8ea406e3486f4f47b00274
- Size of remote file:
- 1.35 GB
- SHA256:
- b853ed48d343480bcb5361ac0a957d5774d87e9625c0ad10ece4772de6812977
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