Instructions to use McGill-NLP/dpr-statcan-metadata_encoder-basic_info_fr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use McGill-NLP/dpr-statcan-metadata_encoder-basic_info_fr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="McGill-NLP/dpr-statcan-metadata_encoder-basic_info_fr")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("McGill-NLP/dpr-statcan-metadata_encoder-basic_info_fr") model = AutoModel.from_pretrained("McGill-NLP/dpr-statcan-metadata_encoder-basic_info_fr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from McGill-NLP/dpr-statcan-metadata_encoder-basic_info_fr: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/McGill-NLP/dpr-statcan-metadata_encoder-basic_info_fr/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://McGill-NLP/dpr-statcan-metadata_encoder-basic_info_fr/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/McGill-NLP/dpr-statcan-metadata_encoder-basic_info_fr/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- f88b68976612f54a9e8113acc7f6b207c27009074c48c60ebeeeaec3b25f9ea6
- Size of remote file:
- 443 MB
- SHA256:
- 78bc62b8b2955ef4f285f57057bac7c523b8b6264260188a16d4b6b5e5ef0af5
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