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
File size: 509 Bytes
3293e34 bf2b9e4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 | ---
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](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. |