Instructions to use ChatterjeeLab/FusOn-pLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChatterjeeLab/FusOn-pLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ChatterjeeLab/FusOn-pLM")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ChatterjeeLab/FusOn-pLM") model = AutoModelForMaskedLM.from_pretrained("ChatterjeeLab/FusOn-pLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download fuson_plm/benchmarking/caid/splits/train_df.csv from ChatterjeeLab/FusOn-pLM: direct link, hf CLI and curl.
- Browser
- Download file 3.05 MB
-
https://huggingface.co/ChatterjeeLab/FusOn-pLM/resolve/main/fuson_plm/benchmarking/caid/splits/train_df.csv
- Command line
-
hf download hf://ChatterjeeLab/FusOn-pLM/fuson_plm/benchmarking/caid/splits/train_df.csv
-
curl -L -o train_df.csv https://huggingface.co/ChatterjeeLab/FusOn-pLM/resolve/main/fuson_plm/benchmarking/caid/splits/train_df.csv
3.05 MB
- Xet hash:
- 404afce6de808d9418769544d9ce920acba61c559b6fd851f5de6d0c932731bd
- Size of remote file:
- 3.05 MB
- SHA256:
- e4a5da0f40fa75fd40f5b08aec7dde1680fa57bc4b9f90d6d1624918bb380377
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