Instructions to use fzbuzz/COMET2019_ConceptNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fzbuzz/COMET2019_ConceptNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="fzbuzz/COMET2019_ConceptNet", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("fzbuzz/COMET2019_ConceptNet", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from fzbuzz/COMET2019_ConceptNet: direct link, hf CLI and curl.
- Browser
- Download file 465 MB
-
https://huggingface.co/fzbuzz/COMET2019_ConceptNet/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://fzbuzz/COMET2019_ConceptNet@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/fzbuzz/COMET2019_ConceptNet/resolve/refs%2Fpr%2F1/pytorch_model.bin
465 MB
- Xet hash:
- e1085a9a093e1a397175406891bd045eeb91b24bc284705ff5bcfc1d9016e2ba
- Size of remote file:
- 465 MB
- SHA256:
- a295a5827a7c1e8f09031d9c45036c3a7047b59505f1966a829933fc59819961
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