Instructions to use lian01110/bert_finetuning_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lian01110/bert_finetuning_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lian01110/bert_finetuning_test")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lian01110/bert_finetuning_test") model = AutoModelForMaskedLM.from_pretrained("lian01110/bert_finetuning_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from lian01110/bert_finetuning_test: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/lian01110/bert_finetuning_test/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://lian01110/bert_finetuning_test/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/lian01110/bert_finetuning_test/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- 527a2f61c28c16aedf8724f44a9375a4493c8eaec6b266ee9a96ae92a178d351
- Size of remote file:
- 438 MB
- SHA256:
- 17d276a02fa763d4cec28f8ee7c7ea6913a21e8273654fc8125a866daf5c654a
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