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/splits.csv from ChatterjeeLab/FusOn-pLM: direct link, hf CLI and curl.
- Browser
- Download file 4.13 MB
-
https://huggingface.co/ChatterjeeLab/FusOn-pLM/resolve/main/fuson_plm/benchmarking/caid/splits/splits.csv
- Command line
-
hf download hf://ChatterjeeLab/FusOn-pLM/fuson_plm/benchmarking/caid/splits/splits.csv
-
curl -L -o splits.csv https://huggingface.co/ChatterjeeLab/FusOn-pLM/resolve/main/fuson_plm/benchmarking/caid/splits/splits.csv
4.13 MB
- Xet hash:
- cd88338ddbf42078075400c7b7f8a5c9129e3b23c100ea20b016b03da4c96d75
- Size of remote file:
- 4.13 MB
- SHA256:
- 14fb44c158d7c1fb6bf393e90489693df8122e75ca03039ec3e17dc0fc60b7af
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