| {"adv_sst2": {"description": "Adversarial GLUE Benchmark (AdvGLUE) is a comprehensive robustness evaluation benchmark\nthat focuses on the adversarial robustness evaluation of language models. It covers five\nnatural language understanding tasks from the famous GLUE tasks and is an adversarial\nversion of GLUE benchmark.\n", "citation": "@article{Wang2021AdversarialGA,\n title={Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models},\n author={Boxin Wang and Chejian Xu and Shuohang Wang and Zhe Gan and Yu Cheng and Jianfeng Gao and Ahmed Hassan Awadallah and B. Li},\n journal={ArXiv},\n year={2021},\n volume={abs/2111.02840}\n}\n", "homepage": "https://adversarialglue.github.io/", "license": "", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["negative", "positive"], "id": null, "_type": "ClassLabel"}, "idx": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "adv_glue", "config_name": "adv_sst2", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 16595, "num_examples": 148, "dataset_name": "adv_glue"}}, "download_checksums": {"https://adversarialglue.github.io/dataset/dev.zip": {"num_bytes": 40662, "checksum": "efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9"}}, "download_size": 40662, "post_processing_size": null, "dataset_size": 16595, "size_in_bytes": 57257}, "adv_qqp": {"description": "Adversarial GLUE Benchmark (AdvGLUE) is a comprehensive robustness evaluation benchmark\nthat focuses on the adversarial robustness evaluation of language models. It covers five\nnatural language understanding tasks from the famous GLUE tasks and is an adversarial\nversion of GLUE benchmark.\n", "citation": "@article{Wang2021AdversarialGA,\n title={Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models},\n author={Boxin Wang and Chejian Xu and Shuohang Wang and Zhe Gan and Yu Cheng and Jianfeng Gao and Ahmed Hassan Awadallah and B. Li},\n journal={ArXiv},\n year={2021},\n volume={abs/2111.02840}\n}\n", "homepage": "https://adversarialglue.github.io/", "license": "", "features": {"question1": {"dtype": "string", "id": null, "_type": "Value"}, "question2": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["not_duplicate", "duplicate"], "id": null, "_type": "ClassLabel"}, "idx": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "adv_glue", "config_name": "adv_qqp", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 9926, "num_examples": 78, "dataset_name": "adv_glue"}}, "download_checksums": {"https://adversarialglue.github.io/dataset/dev.zip": {"num_bytes": 40662, "checksum": "efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9"}}, "download_size": 40662, "post_processing_size": null, "dataset_size": 9926, "size_in_bytes": 50588}, "adv_mnli": {"description": "Adversarial GLUE Benchmark (AdvGLUE) is a comprehensive robustness evaluation benchmark\nthat focuses on the adversarial robustness evaluation of language models. It covers five\nnatural language understanding tasks from the famous GLUE tasks and is an adversarial\nversion of GLUE benchmark.\n", "citation": "@article{Wang2021AdversarialGA,\n title={Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models},\n author={Boxin Wang and Chejian Xu and Shuohang Wang and Zhe Gan and Yu Cheng and Jianfeng Gao and Ahmed Hassan Awadallah and B. Li},\n journal={ArXiv},\n year={2021},\n volume={abs/2111.02840}\n}\n", "homepage": "https://adversarialglue.github.io/", "license": "", "features": {"premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["entailment", "neutral", "contradiction"], "id": null, "_type": "ClassLabel"}, "idx": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "adv_glue", "config_name": "adv_mnli", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 23736, "num_examples": 121, "dataset_name": "adv_glue"}}, "download_checksums": {"https://adversarialglue.github.io/dataset/dev.zip": {"num_bytes": 40662, "checksum": "efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9"}}, "download_size": 40662, "post_processing_size": null, "dataset_size": 23736, "size_in_bytes": 64398}, "adv_mnli_mismatched": {"description": "Adversarial GLUE Benchmark (AdvGLUE) is a comprehensive robustness evaluation benchmark\nthat focuses on the adversarial robustness evaluation of language models. It covers five\nnatural language understanding tasks from the famous GLUE tasks and is an adversarial\nversion of GLUE benchmark.\n", "citation": "@article{Wang2021AdversarialGA,\n title={Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models},\n author={Boxin Wang and Chejian Xu and Shuohang Wang and Zhe Gan and Yu Cheng and Jianfeng Gao and Ahmed Hassan Awadallah and B. Li},\n journal={ArXiv},\n year={2021},\n volume={abs/2111.02840}\n}\n", "homepage": "https://adversarialglue.github.io/", "license": "", "features": {"premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["entailment", "neutral", "contradiction"], "id": null, "_type": "ClassLabel"}, "idx": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "adv_glue", "config_name": "adv_mnli_mismatched", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 40982, "num_examples": 162, "dataset_name": "adv_glue"}}, "download_checksums": {"https://adversarialglue.github.io/dataset/dev.zip": {"num_bytes": 40662, "checksum": "efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9"}}, "download_size": 40662, "post_processing_size": null, "dataset_size": 40982, "size_in_bytes": 81644}, "adv_qnli": {"description": "Adversarial GLUE Benchmark (AdvGLUE) is a comprehensive robustness evaluation benchmark\nthat focuses on the adversarial robustness evaluation of language models. It covers five\nnatural language understanding tasks from the famous GLUE tasks and is an adversarial\nversion of GLUE benchmark.\n", "citation": "@article{Wang2021AdversarialGA,\n title={Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models},\n author={Boxin Wang and Chejian Xu and Shuohang Wang and Zhe Gan and Yu Cheng and Jianfeng Gao and Ahmed Hassan Awadallah and B. Li},\n journal={ArXiv},\n year={2021},\n volume={abs/2111.02840}\n}\n", "homepage": "https://adversarialglue.github.io/", "license": "", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["entailment", "not_entailment"], "id": null, "_type": "ClassLabel"}, "idx": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "adv_glue", "config_name": "adv_qnli", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 34877, "num_examples": 148, "dataset_name": "adv_glue"}}, "download_checksums": {"https://adversarialglue.github.io/dataset/dev.zip": {"num_bytes": 40662, "checksum": "efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9"}}, "download_size": 40662, "post_processing_size": null, "dataset_size": 34877, "size_in_bytes": 75539}, "adv_rte": {"description": "Adversarial GLUE Benchmark (AdvGLUE) is a comprehensive robustness evaluation benchmark\nthat focuses on the adversarial robustness evaluation of language models. It covers five\nnatural language understanding tasks from the famous GLUE tasks and is an adversarial\nversion of GLUE benchmark.\n", "citation": "@article{Wang2021AdversarialGA,\n title={Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models},\n author={Boxin Wang and Chejian Xu and Shuohang Wang and Zhe Gan and Yu Cheng and Jianfeng Gao and Ahmed Hassan Awadallah and B. Li},\n journal={ArXiv},\n year={2021},\n volume={abs/2111.02840}\n}\n", "homepage": "https://adversarialglue.github.io/", "license": "", "features": {"sentence1": {"dtype": "string", "id": null, "_type": "Value"}, "sentence2": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["entailment", "not_entailment"], "id": null, "_type": "ClassLabel"}, "idx": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "adv_glue", "config_name": "adv_rte", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 25998, "num_examples": 81, "dataset_name": "adv_glue"}}, "download_checksums": {"https://adversarialglue.github.io/dataset/dev.zip": {"num_bytes": 40662, "checksum": "efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9"}}, "download_size": 40662, "post_processing_size": null, "dataset_size": 25998, "size_in_bytes": 66660}} |