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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
arxiv: 2302.04026
pipeline_tag: fill-mask
tags:
- code
C-BERT MLM
Exploring Software Naturalness through Neural Language Models
Overview
This model is the unofficial HuggingFace version of "C-BERT" with just the masked language modeling head for pretraining. The weights come from "An Empirical Comparison of Pre-Trained Models of Source Code". Please cite the authors if you use this in an academic setting.