Instructions to use McGill-NLP/tapas-statcan-large-conversation_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-conversation_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-conversation_encoder-cell_tokens")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("McGill-NLP/tapas-statcan-large-conversation_encoder-cell_tokens") model = AutoModel.from_pretrained("McGill-NLP/tapas-statcan-large-conversation_encoder-cell_tokens", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from McGill-NLP/tapas-statcan-large-conversation_encoder-cell_tokens: direct link, hf CLI and curl.
- Browser
- Download file 1.35 GB
-
https://huggingface.co/McGill-NLP/tapas-statcan-large-conversation_encoder-cell_tokens/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://McGill-NLP/tapas-statcan-large-conversation_encoder-cell_tokens/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/McGill-NLP/tapas-statcan-large-conversation_encoder-cell_tokens/resolve/main/pytorch_model.bin
1.35 GB
- Xet hash:
- 2cad1244d764a7df64dfcd80797fd41c901d06bc84813b9b5e47e379f21a12f2
- Size of remote file:
- 1.35 GB
- SHA256:
- 52e1c3c695da420ec3a290d94462b3bc4c5ba1947ce990b4ca36f61cf7a4d577
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