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--- |
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language: code |
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thumbnail: |
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tags: |
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- javascript |
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- code |
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widget: |
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- text: "async function createUser(req, <mask>) { if (!validUser(req.body.user)) { return res.status(400); } user = userService.createUser(req.body.user); return res.json(user); }" |
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--- |
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# CodeBERTaJS |
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CodeBERTaJS is a RoBERTa-like model trained on the [CodeSearchNet](https://github.blog/2019-09-26-introducing-the-codesearchnet-challenge/) dataset from GitHub for `javaScript` by [Manuel Romero](https://twitter.com/mrm8488) |
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The **tokenizer** is a Byte-level BPE tokenizer trained on the corpus using Hugging Face `tokenizers`. |
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Because it is trained on a corpus of code (vs. natural language), it encodes the corpus efficiently (the sequences are between 33% to 50% shorter, compared to the same corpus tokenized by gpt2/roberta). |
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The (small) **model** is a 6-layer, 84M parameters, RoBERTa-like Transformer model – that’s the same number of layers & heads as DistilBERT – initialized from the default initialization settings and trained from scratch on the full `javascript` corpus (120M after preproccessing) for 2 epochs. |
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## Quick start: masked language modeling prediction |
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```python |
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JS_CODE = """ |
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async function createUser(req, <mask>) { |
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if (!validUser(req.body.user)) { |
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\t return res.status(400); |
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} |
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user = userService.createUser(req.body.user); |
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return res.json(user); |
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} |
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""".lstrip() |
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``` |
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### Does the model know how to complete simple JS/express like code? |
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```python |
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from transformers import pipeline |
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fill_mask = pipeline( |
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"fill-mask", |
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model="mrm8488/codeBERTaJS", |
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tokenizer="mrm8488/codeBERTaJS" |
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) |
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fill_mask(JS_CODE) |
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## Top 5 predictions: |
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# |
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'res' # prob 0.069489665329 |
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'next' |
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'req' |
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'user' |
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',req' |
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``` |
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### Yes! That was easy 🎉 Let's try with another example |
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```python |
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JS_CODE_= """ |
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function getKeys(obj) { |
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keys = []; |
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for (var [key, value] of Object.entries(obj)) { |
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keys.push(<mask>); |
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} |
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return keys |
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} |
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""".lstrip() |
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``` |
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Results: |
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```python |
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'obj', 'key', ' value', 'keys', 'i' |
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``` |
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> Not so bad! Right token was predicted as second option! 🎉 |
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## This work is heavely inspired on [codeBERTa](https://github.com/huggingface/transformers/blob/master/model_cards/huggingface/CodeBERTa-small-v1/README.md) by huggingface team |
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<br> |
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## CodeSearchNet citation |
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<details> |
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```bibtex |
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@article{husain_codesearchnet_2019, |
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\ttitle = {{CodeSearchNet} {Challenge}: {Evaluating} the {State} of {Semantic} {Code} {Search}}, |
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\tshorttitle = {{CodeSearchNet} {Challenge}}, |
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\turl = {http://arxiv.org/abs/1909.09436}, |
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\turldate = {2020-03-12}, |
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\tjournal = {arXiv:1909.09436 [cs, stat]}, |
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\tauthor = {Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc}, |
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\tmonth = sep, |
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\tyear = {2019}, |
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\tnote = {arXiv: 1909.09436}, |
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} |
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``` |
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</details> |
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> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) |
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> Made with <span style="color: #e25555;">♥</span> in Spain |
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