Instructions to use gokulsrinivasagan/bert_base_code_uml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gokulsrinivasagan/bert_base_code_uml with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="gokulsrinivasagan/bert_base_code_uml")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/bert_base_code_uml") model = AutoModelForMaskedLM.from_pretrained("gokulsrinivasagan/bert_base_code_uml", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from gokulsrinivasagan/bert_base_code_uml: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/gokulsrinivasagan/bert_base_code_uml/resolve/main/training_args.bin
- Command line
-
hf download hf://gokulsrinivasagan/bert_base_code_uml/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/gokulsrinivasagan/bert_base_code_uml/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- ccacbfc339756d0f85fc3121d90710659ae14420edb5fd9a9fd1ee406ebae266
- Size of remote file:
- 5.3 kB
- SHA256:
- 4b92421fc5764f372447de469edd2087779cfd797ea065eb0dd3f847acf8d340
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