Instructions to use patricklai14/tapt_citation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use patricklai14/tapt_citation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="patricklai14/tapt_citation")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("patricklai14/tapt_citation") model = AutoModelForMaskedLM.from_pretrained("patricklai14/tapt_citation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from patricklai14/tapt_citation: direct link, hf CLI and curl.
- Browser
- Download file 2.29 kB
-
https://huggingface.co/patricklai14/tapt_citation/resolve/main/training_args.bin
- Command line
-
hf download hf://patricklai14/tapt_citation/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/patricklai14/tapt_citation/resolve/main/training_args.bin
2.29 kB
- Xet hash:
- f5179e4c95227f6e0f76bb88e5a0c770daa71b94e9f8469b167354bf52bdfd70
- Size of remote file:
- 2.29 kB
- SHA256:
- e64c6b589468d0e627ac2bca4c90ac9e22fb14d216166cd06429901e43cb6d02
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.