
[Paper on arXiv](https://arxiv.org/abs/2306.15604)

##　pre-training data
You need to manually combine each dataset if you want to use a multilingual dataset.
```python
from datasets import load_dataset
xcsn_pt_python_en = load_dataset("ynklab/XCodeSearchNet", data_dir='pretraining/python/en')
"""
DatasetDict({
    train: Dataset({
        features: ['function_tokens', 'docstring'],
        num_rows: 453623
    })
    validation: Dataset({
        features: ['function_tokens', 'docstring'],
        num_rows: 4596
    })
    test: Dataset({
        features: ['function_tokens', 'docstring'],
        num_rows: 45283
    })
})
"""
print(xcsn_pt_python_en['train'][0])
"""
{
  'function_tokens': ['def', 'get_feature_ide_paths', '(', 'container_dir', ',', 'product_name', ')', ':', 'repo_name', '=', 'get_repo_name', '(', 'container_dir', ')', 'class', 'Paths', '(', 'object', ')', ':', 'feature_order_json', '=', 'os', '.', 'path', '.', 'join', '(', 'container_dir', ',', "'_lib/featuremodel/productline/feature_order.json'", ')', 'model_xml_path', '=', 'os', '.', 'path', '.', 'join', '(', 'container_dir', ',', "'_lib/featuremodel/productline/model.xml'", ')', 'config_file_path', '=', 'os', '.', 'path', '.', 'join', '(', 'container_dir', ',', "'_lib/featuremodel/productline/products/'", ',', 'repo_name', ',', 'product_name', ',', "'product.equation.config'", ')', 'equation_file_path', '=', 'os', '.', 'path', '.', 'join', '(', 'container_dir', ',', "'products'", ',', 'product_name', ',', "'product.equation'", ')', 'product_spec_path', '=', 'os', '.', 'path', '.', 'join', '(', 'container_dir', ',', "'_lib/featuremodel/productline/products/'", ',', 'repo_name', ',', "'product_spec.json'", ')', 'return', 'Paths'],
  'docstring': 'Takes the container_dir and the product name and returns all relevant paths from the\n    feature_order_json to the config_file_path.\n    :param container_dir: the full path of the container dir\n    :param product_name: the name of the product\n    :return: object with divert path attributes'
}
"""
```
##　fine-tuning data
```python
from datasets import load_dataset
xcsn_ft_python_en = load_dataset("ynklab/XCodeSearchNet", data_dir='finetuning/python/en')
"""
DatasetDict({
    train: Dataset({
        features: ['text'],
        num_rows: 1648684
    })
    validation: Dataset({
        features: ['text'],
        num_rows: 92426
    })
})
"""
print(xcsn_ft_python_en['train'][0])
"""
{
  'text': '1<CODESPLIT><CODESPLIT><CODESPLIT>Logs the definition of the object that was just auto - decorated inside the ipython notebook .<CODESPLIT>def _logdef ( self , n , o , otype ) : import re try : #The latest input cell will be the one that this got executed #from. TODO: actually, if acorn got imported after the fact, then #the import would have caused all the undecorated functions to be #decorated as soon as acorn imported. I suppose we just won\'t have #any code for that case. if otype == "classes" : cellno = max ( [ int ( k [ 2 : ] ) for k in self . shell . user_ns . keys ( ) if re . match ( "_i\\d+" , k ) ] ) elif otype == "functions" : cellno = int ( o . __code__ . co_filename . strip ( "<>" ) . split ( \'-\' ) [ 2 ] ) except : #This must not have been an ipython notebook declaration, so we #don\'t store the code. cellno = None pass code = "" if cellno is not None : cellstr = "_i{0:d}" . format ( cellno ) if cellstr in self . shell . user_ns : cellcode = self . shell . user_ns [ cellstr ] import ast astm = ast . parse ( cellcode ) ab = astm . body parts = { ab [ i ] . name : ( ab [ i ] . lineno , None if i + 1 >= len ( ab ) else ab [ i + 1 ] . lineno ) for i , d in enumerate ( ab ) } if n in parts : celllines = cellcode . split ( \'\\n\' ) start , end = parts [ n ] if end is not None : code = celllines [ start - 1 : end - 1 ] else : code = celllines [ start - 1 : ] #Now, we actually create the entry. Since the execution for function #definitions is almost instantaneous, we just log the pre and post #events at the same time. from time import time from acorn . logging . database import record entry = { "m" : "def" , "a" : None , "s" : time ( ) , "r" : None , "c" : code , } from acorn import msg record ( "__main__.{}" . format ( n ) , entry , diff = True ) msg . info ( entry , 1 )'
}
"""
```

language:en language:fr language:ja language:zh license:mit codesearch arxiv:2306.15604 region:us
{"language": ["en", "fr", "ja", "zh"], "license": "mit", "pretty_name": "XCodeSearchNet", "tags": ["codesearch"]}
TAGS
#language-English #language-French #language-Japanese #language-Chinese #license-mit #codesearch #arxiv-2306.15604 #region-us 


Paper on arXiv

##　pre-training data
You need to manually combine each dataset if you want to use a multilingual dataset.

##　fine-tuning data


TAGS
#language-English #language-French #language-Japanese #language-Chinese #license-mit #codesearch #arxiv-2306.15604 #region-us 

passage: TAGS
#language-English #language-French #language-Japanese #language-Chinese #license-mit #codesearch #arxiv-2306.15604 #region-us 
