Instructions to use TerenceAmil/bert_cn_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TerenceAmil/bert_cn_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="TerenceAmil/bert_cn_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("TerenceAmil/bert_cn_finetuning") model = AutoModelForMaskedLM.from_pretrained("TerenceAmil/bert_cn_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from TerenceAmil/bert_cn_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 409 MB
-
https://huggingface.co/TerenceAmil/bert_cn_finetuning/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://TerenceAmil/bert_cn_finetuning/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/TerenceAmil/bert_cn_finetuning/resolve/main/pytorch_model.bin
409 MB
- Xet hash:
- 671f3160a1a40586eae7c5e9cba897770edbb2beb3225f805435a708025298b1
- Size of remote file:
- 409 MB
- SHA256:
- 57b55a176df6c44211029e41b93f9e9c24aacd534a938b40d47fe5bb5a72f2b6
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