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 pytorch_model.bin 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/pytorch_model.bin
- Command line
-
hf download hf://lian01110/bert_finetuning_test/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/lian01110/bert_finetuning_test/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- e4d2ae54168ed48de5717604bfdfd9bfc33348811a10a0e2530d20a71774bd1d
- Size of remote file:
- 438 MB
- SHA256:
- 5c0e8421ab4eceb57845db7106b71c3a11ac25e7fa0f3dbbc25a2bd5c5320da1
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