Instructions to use claudios/cbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use claudios/cbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="claudios/cbert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("claudios/cbert") model = AutoModelForMaskedLM.from_pretrained("claudios/cbert") - Notebooks
- Google Colab
- Kaggle
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## Exploring Software Naturalness through Neural Language Models
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## Overview
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This model is the unofficial HuggingFace version of "[C-BERT](http://arxiv.org/abs/2302.04026)" with just the masked language modeling head for pretraining.
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## Exploring Software Naturalness through Neural Language Models
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## Overview
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This model is the unofficial HuggingFace version of "[C-BERT](http://arxiv.org/abs/2302.04026)" with just the masked language modeling head for pretraining. The weights come from "[An Empirical Comparison of Pre-Trained Models of Source Code](http://arxiv.org/abs/2302.04026)". Please cite the authors if you use this in an academic setting.
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