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/data/raw_data/FOdb_all.csv from ChatterjeeLab/FusOn-pLM: direct link, hf CLI and curl.
- Browser
- Download file 3.88 MB
-
https://huggingface.co/ChatterjeeLab/FusOn-pLM/resolve/refs%2Fpr%2F1/fuson_plm/data/raw_data/FOdb_all.csv
- Command line
-
hf download hf://ChatterjeeLab/FusOn-pLM@refs/pr/1/fuson_plm/data/raw_data/FOdb_all.csv
-
curl -L -o FOdb_all.csv https://huggingface.co/ChatterjeeLab/FusOn-pLM/resolve/refs%2Fpr%2F1/fuson_plm/data/raw_data/FOdb_all.csv
3.88 MB
- Xet hash:
- 0cdef6d13e0b6b086b664d876d2bacb9ae94be9fac28fc1edac5152823cb054f
- Size of remote file:
- 3.88 MB
- SHA256:
- acd531078289d83d42cdd8c0031b682612d396d92eea2e1e8b1871044424fdb0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.