Delete legacy JSON metadata (#2)

- Delete legacy JSON metadata (7be56e099d68e7b619e9a3c03ebb05c447a075a6)

dataset_infos.json

@@ -1 +0,0 @@
-{"default": {"description": "CodeXGLUE Defect-detection dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Defect-detection\n\nGiven 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.\nThe dataset we use comes from the paper Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. We combine all projects and split 80%/10%/10% for training/dev/test.", "citation": "@inproceedings{zhou2019devign,\ntitle={Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks},\nauthor={Zhou, Yaqin and Liu, Shangqing and Siow, Jingkai and Du, Xiaoning and Liu, Yang},\nbooktitle={Advances in Neural Information Processing Systems},\npages={10197--10207}, year={2019}", "homepage": "https://github.com/madlag/CodeXGLUE/tree/main/Code-Code/Defect-detection", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "func": {"dtype": "string", "id": null, "_type": "Value"}, "target": {"dtype": "bool", "id": null, "_type": "Value"}, "project": {"dtype": "string", "id": null, "_type": "Value"}, "commit_id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "target", "output": ""}, "task_templates": null, "builder_name": "code_x_glue_cc_defect_detection", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 45723487, "num_examples": 21854, "dataset_name": "code_x_glue_cc_defect_detection"}, "validation": {"name": "validation", "num_bytes": 5582545, "num_examples": 2732, "dataset_name": "code_x_glue_cc_defect_detection"}, "test": {"name": "test", "num_bytes": 5646752, "num_examples": 2732, "dataset_name": "code_x_glue_cc_defect_detection"}}, "download_checksums": {"https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/Defect-detection/dataset/train.txt": {"num_bytes": 122185, "checksum": "f0a25410594302a9f0e542a393ad82ad479308a8aa471f4d6cf61b91d6d572bf"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/Defect-detection/dataset/function.json": {"num_bytes": 61532917, "checksum": "0a3b2d561dc6280e53795886ede727d0045c016d083905ba3e9ce384a7eab246"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/Defect-detection/dataset/valid.txt": {"num_bytes": 15295, "checksum": "9f2fa1e108955f197d4a7fa2aa2c7f5e542457b51e0eb1f6e890172d6f700a6e"}, "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/Defect-detection/dataset/test.txt": {"num_bytes": 15318, "checksum": "b5336b337170ea1edf0570b69edb5a90e3c99bf41cd92909795f5fe32d376d52"}}, "download_size": 61685715, "post_processing_size": null, "dataset_size": 56952784, "size_in_bytes": 118638499}}