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/test_df.csv from ChatterjeeLab/FusOn-pLM: direct link, hf CLI and curl.
- Browser
- Download file 329 kB
-
https://huggingface.co/ChatterjeeLab/FusOn-pLM/resolve/main/fuson_plm/benchmarking/caid/splits/test_df.csv
- Command line
-
hf download hf://ChatterjeeLab/FusOn-pLM/fuson_plm/benchmarking/caid/splits/test_df.csv
-
curl -L -o test_df.csv https://huggingface.co/ChatterjeeLab/FusOn-pLM/resolve/main/fuson_plm/benchmarking/caid/splits/test_df.csv
329 kB
- Xet hash:
- 65ae9fed8f05df0d47fe397fffc7940118e4322bc23e3429233c5bd5a59b8328
- Size of remote file:
- 329 kB
- SHA256:
- 4e6c75f13136d0daa34c944dbd6bb2980082034d87f1e2a98586e25584c2db63
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