Instructions to use McGill-NLP/tapas-statcan-large-metadata_encoder-title_and_member 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-title_and_member 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-title_and_member")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("McGill-NLP/tapas-statcan-large-metadata_encoder-title_and_member") model = AutoModel.from_pretrained("McGill-NLP/tapas-statcan-large-metadata_encoder-title_and_member") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a6187ff56904d50be777f4bf6e27aac1773d8757ce921e17edd1872832d0769b
- Size of remote file:
- 1.35 GB
- SHA256:
- fcda253e751a189a33d2bb760dce3e33df9fc6d7c17d00f6115c12f072e66258
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