Update README.md

README.md

@@ -18,42 +18,5 @@ dataset_info:
   download_size: 33528231
   dataset_size: 156327244
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-### 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/code-to-code/Defect-detection in Semeru
-
-
-# CodeXGLUE -- Defect Detection
-
-## [](https://huggingface.co/datasets/semeru/code-code-DefectDetection#task-definition)Task Definition
-
-Given a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code.
-
-### [](https://huggingface.co/datasets/semeru/code-code-DefectDetection#dataset)Dataset
-
-The dataset we use comes from the paper  [_Devign_: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks](http://papers.nips.cc/paper/9209-devign-effective-vulnerability-identification-by-learning-comprehensive-program-semantics-via-graph-neural-networks.pdf). We combine all projects and split 80%/10%/10% for training/dev/test.
-
-### [](https://huggingface.co/datasets/semeru/code-code-DefectDetection#data-format)Data Format
-
-Three pre-processed .jsonl files, i.e. train.jsonl, valid.jsonl, test.jsonl are present
-
-For each file, each line in the uncompressed file represents one function. One row is illustrated below.
-
--   **func:**  the source code
--   **target:**  0 or 1 (vulnerability or not)
--   **idx:**  the index of example
-
-### [](https://huggingface.co/datasets/semeru/code-code-DefectDetection#data-statistics)Data Statistics
-
-Data statistics of the dataset are shown in the below table:
-
-
-
-| | Description |
-| ----------- | ----------- |
-| Train| 126,477|
-| Dev| 15,809|
-|Test|15,810
-
+## Dataset Card for "AGabs_finetuning"
+More Information needed