Instructions to use junzai/bert_finetuning_test1227_hug with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use junzai/bert_finetuning_test1227_hug with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="junzai/bert_finetuning_test1227_hug")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("junzai/bert_finetuning_test1227_hug") model = AutoModelForMaskedLM.from_pretrained("junzai/bert_finetuning_test1227_hug", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from junzai/bert_finetuning_test1227_hug: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/junzai/bert_finetuning_test1227_hug/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://junzai/bert_finetuning_test1227_hug/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/junzai/bert_finetuning_test1227_hug/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 6884d25ebc50a8bbed5af3dcd5a28ee2258a2353418bbd89f9f8e46ec230287d
- Size of remote file:
- 438 MB
- SHA256:
- 19cbb5482b77781aeb04cd5d98f974cd574d51dd6d97ce41bc5f24afa2d4558d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.