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/splits/train_df.csv from ChatterjeeLab/FusOn-pLM: direct link, hf CLI and curl.
- Browser
- Download file 257 MB
-
https://huggingface.co/ChatterjeeLab/FusOn-pLM/resolve/refs%2Fpr%2F1/fuson_plm/data/splits/train_df.csv
- Command line
-
hf download hf://ChatterjeeLab/FusOn-pLM@refs/pr/1/fuson_plm/data/splits/train_df.csv
-
curl -L -o train_df.csv https://huggingface.co/ChatterjeeLab/FusOn-pLM/resolve/refs%2Fpr%2F1/fuson_plm/data/splits/train_df.csv
257 MB
- Xet hash:
- d0ce8e0f3c9c61596fd8f5161520bd4d6748e699893081d19918ecf29fea84e0
- Size of remote file:
- 257 MB
- SHA256:
- ddf563734014c7d4fec944c639431d3423d2ab79e1e6e9e800c955c24438c8eb
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