Instructions to use CAUKiel/JavaBERT-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CAUKiel/JavaBERT-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="CAUKiel/JavaBERT-uncased", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("CAUKiel/JavaBERT-uncased") model = AutoModelForMaskedLM.from_pretrained("CAUKiel/JavaBERT-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- java
- code
license: apache-2.0
widget:
- text: >-
public [MASK] isOdd(Integer num){if (num % 2 == 0) {return "even";} else
{return "odd";}}
JavaBERT
A BERT-like model pretrained on Java software code.
Training Data
The model was trained on 2,998,345 Java files retrieved from open source projects on GitHub. A bert-base-uncased tokenizer is used by this model.
Training Objective
A MLM (Masked Language Model) objective was used to train this model.
Usage
from transformers import pipeline
pipe = pipeline('fill-mask', model='CAUKiel/JavaBERT')
output = pipe(CODE) # Replace with Java code; Use '[MASK]' to mask tokens/words in the code.