codebert base mlm

Introduction

CodeBERT-Base-MLM is a pre-trained model designed for both programming and natural languages, leveraging a Masked Language Model (MLM) objective. It is built upon the Roberta-base architecture and trained using the CodeSearchNet corpus.

Architecture

The model is initialized with the Roberta-base architecture, a variant of the transformer model widely used for its efficiency and effectiveness in language modeling tasks.

Training

CodeBERT is trained on the CodeSearchNet dataset, focusing on a simple MLM objective. This approach allows the model to predict masked tokens within input sequences, aiding in understanding and generating code and natural language simultaneously.

Guide: Running Locally

To run CodeBERT-Base-MLM locally, follow these steps:

1. Install Transformers Library:

   pip install transformers
   

2. Import Model and Tokenizer:

   from transformers import RobertaTokenizer, RobertaForMaskedLM, pipeline
   

3. Load Pre-trained Model:

   model = RobertaForMaskedLM.from_pretrained('microsoft/codebert-base-mlm')
   tokenizer = RobertaTokenizer.from_pretrained('microsoft/codebert-base-mlm')
   

4. Prepare Example Code:

   code_example = "if (x is not None) <mask> (x>1)"
   

5. Create Fill-Mask Pipeline:

   fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)
   

6. Run the Model:

   outputs = fill_mask(code_example)
   print(outputs)
   

Cloud GPUs: For optimal performance, consider using cloud-based GPU services such as AWS EC2, Google Cloud Platform, or Azure to handle the computational requirements of CodeBERT.

License

Please refer to the official Hugging Face model card for licensing information.