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")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("CAUKiel/JavaBERT-uncased") model = AutoModelForMaskedLM.from_pretrained("CAUKiel/JavaBERT-uncased") - Notebooks
- Google Colab
- Kaggle
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A BERT-like model pretrained on Java software code.
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### Training Data
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The model was trained on 2,998,345 Java files retrieved from open source projects on GitHub. A ```bert-base-
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### Training Objective
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A MLM (Masked Language Model) objective was used to train this model.
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A BERT-like model pretrained on Java software code.
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### Training Data
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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.
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### Training Objective
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A MLM (Masked Language Model) objective was used to train this model.
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