Instructions to use HillZhang/pseudo_native_bart_CGEC_thesis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HillZhang/pseudo_native_bart_CGEC_thesis with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("HillZhang/pseudo_native_bart_CGEC_thesis") model = AutoModelForSeq2SeqLM.from_pretrained("HillZhang/pseudo_native_bart_CGEC_thesis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from HillZhang/pseudo_native_bart_CGEC_thesis: direct link, hf CLI and curl.
- Browser
- Download file 1.5 GB
-
https://huggingface.co/HillZhang/pseudo_native_bart_CGEC_thesis/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://HillZhang/pseudo_native_bart_CGEC_thesis/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/HillZhang/pseudo_native_bart_CGEC_thesis/resolve/main/pytorch_model.bin
1.5 GB
- Xet hash:
- 4eb405dee1b414adc1a1bff1109a063087fc0b2b1d17070212fd07d4c02e8e67
- Size of remote file:
- 1.5 GB
- SHA256:
- 22f59d3d4f3dce6c5c3fd946e2dd996463df516445b65bb7d74e15950be6c68c
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