Instructions to use pulp/CHILDES-ParentBERTo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pulp/CHILDES-ParentBERTo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="pulp/CHILDES-ParentBERTo")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("pulp/CHILDES-ParentBERTo") model = AutoModelForMaskedLM.from_pretrained("pulp/CHILDES-ParentBERTo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from pulp/CHILDES-ParentBERTo: direct link, hf CLI and curl.
- Browser
- Download file 2.29 kB
-
https://huggingface.co/pulp/CHILDES-ParentBERTo/resolve/main/training_args.bin
- Command line
-
hf download hf://pulp/CHILDES-ParentBERTo/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/pulp/CHILDES-ParentBERTo/resolve/main/training_args.bin
2.29 kB
- Xet hash:
- a447e5938f4f0f60326fa986bfc95d7d165592513e1298fcdc6673e0bb3563f6
- Size of remote file:
- 2.29 kB
- SHA256:
- ab6be4659483088d6a2cb6fdc195af12092bbbfce51dfac4b700a9f9a324e400
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