Instructions to use up201806461/bert-java-bfp_combined with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use up201806461/bert-java-bfp_combined with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="up201806461/bert-java-bfp_combined")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("up201806461/bert-java-bfp_combined") model = AutoModelForMaskedLM.from_pretrained("up201806461/bert-java-bfp_combined") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): b5fd2b1
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README.md
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```python
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from transformers import pipeline
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unmasker = pipeline('fill-mask', model='bert-
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unmasker(java_code) # Replace with Java code; Use '[MASK]' to mask tokens/words in the code.
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```
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#### Preprocessing [optional]
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Remove comments and replace consecutive whitespace characters by a single space
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#### Training Hyperparameters
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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### Results
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#### Summary
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```python
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from transformers import pipeline
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unmasker = pipeline('fill-mask', model='bert-java-bfp_combined')
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unmasker(java_code) # Replace with Java code; Use '[MASK]' to mask tokens/words in the code.
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```
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#### Preprocessing [optional]
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Remove comments and replace consecutive whitespace characters by a single space.
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#### Training Hyperparameters
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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Perplexity
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### Results
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1.48
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#### Summary
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