Instructions to use transformersbook/bert-base-uncased-issues-128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use transformersbook/bert-base-uncased-issues-128 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="transformersbook/bert-base-uncased-issues-128")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("transformersbook/bert-base-uncased-issues-128") model = AutoModelForMaskedLM.from_pretrained("transformersbook/bert-base-uncased-issues-128", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from transformersbook/bert-base-uncased-issues-128: direct link, hf CLI and curl.
- Browser
- Download file 2.86 kB
-
https://huggingface.co/transformersbook/bert-base-uncased-issues-128/resolve/main/training_args.bin
- Command line
-
hf download hf://transformersbook/bert-base-uncased-issues-128/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/transformersbook/bert-base-uncased-issues-128/resolve/main/training_args.bin
2.86 kB
- Xet hash:
- 837c01c91c3aeb0eda8a194098fff72175252921c9fb6bac9985a9326c193579
- Size of remote file:
- 2.86 kB
- SHA256:
- aaaff617d63b2552137f18cc990e2587b6a21aeb4f5da20a37071ec29bbf2caf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.