Update Readme for Text-to-code codesearchnet

README.md

@@ -1,3 +1,94 @@
 ---
 license: mit
+tags:
+- CodeSearchNet
+- CodeXGLUE
+size_categories:
+- n<1K
 ---
+
+### Dataset is imported from CodeXGLUE and pre-processed using their script.
+
+# Where to find in Semeru:
+The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/text-to-code/codesearchnet/python in Semeru
+
+# CodeXGLUE -- Code Search (AdvTest)
+
+## Task Definition
+
+Given a natural language, the task is to search source code that matches the natural language. To test the generalization ability of a model,  function names and variables in test sets are replaced by special tokens.
+
+## Dataset
+
+The dataset we use comes from [CodeSearchNet](https://arxiv.org/pdf/1909.09436.pdf) and we filter the dataset as the following:
+
+- Remove examples that codes cannot be parsed into an abstract syntax tree.
+- Remove examples that #tokens of documents is < 3 or >256
+- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
+- Remove examples that documents are not English.
+
+Besides, to test the generalization ability of a model,  function names and variables in test sets are replaced by special tokens.
+
+
+### Data Format
+
+After preprocessing dataset, you can obtain three .jsonl files, i.e. train.jsonl, valid.jsonl, test.jsonl
+
+For each file, each line in the uncompressed file represents one function.  One row is illustrated below.
+
+  - **repo:** the owner/repo
+  - **path:** the full path to the original file
+  - **func_name:** the function or method name
+  - **original_string:** the raw string before tokenization or parsing
+  - **language:** the programming language
+  - **code/function:** the part of the `original_string` that is code
+  - **code_tokens/function_tokens:** tokenized version of `code`
+  - **docstring:** the top-level comment or docstring, if it exists in the original string
+  - **docstring_tokens:** tokenized version of `docstring`
+  - **url:** the url for the example (identify natural language)
+  - **idx**: the index of code (identify code)
+
+### Data Statistics
+
+Data statistics of the dataset are shown in the below table:
+
+|       | #Examples |
+| ----- | :-------: |
+| Train |  251,820  |
+| Dev   |   9,604   |
+| Test  |  19,210   |
+
+
+### Example
+
+Given a text-code file evaluator/test.jsonl:
+
+```json
+{"url": "url0", "docstring": "doc0","function": "fun0", "idx": 10}
+{"url": "url1", "docstring": "doc1","function": "fun1", "idx": 11}
+{"url": "url2", "docstring": "doc2","function": "fun2", "idx": 12}
+{"url": "url3", "docstring": "doc3","function": "fun3", "idx": 13}
+{"url": "url4", "docstring": "doc4","function": "fun4", "idx": 14}
+```
+
+
+### Input Predictions
+
+For each url for natural language, descending sort candidate codes and return their idx in order. For example:
+
+```json
+{"url": "url0", "answers": [10,11,12,13,14]}
+{"url": "url1", "answers": [10,12,11,13,14]}
+{"url": "url2", "answers": [13,11,12,10,14]}
+{"url": "url3", "answers": [10,14,12,13,11]}
+{"url": "url4", "answers": [10,11,12,13,14]}
+```
+
+
+## Reference
+<pre><code>@article{husain2019codesearchnet,
+  title={Codesearchnet challenge: Evaluating the state of semantic code search},
+  author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
+  journal={arXiv preprint arXiv:1909.09436},
+  year={2019}
+}</code></pre>