Datasets:

Efstathios
/

guardian_authorship

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+ {"cross_topic_1": {"description": "A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. \n1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).\n2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).\n\n3- The same-topic/genre scenario is created by grouping all the datasts as follows. \nFor ex., to use same_topic and split the data 60-40 use:\ntrain_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[:60%]+validation[:60%]+test[:60%]')\ntests_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[-40%:]+validation[-40%:]+test[-40%:]') \n\nIMPORTANT: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced\n\n* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples \n", "citation": "@article{article,\n author = {Stamatatos, Efstathios},\n year = {2013},\n month = {01},\n pages = {421-439},\n title = {On the robustness of authorship attribution based on character n-gram features},\n volume = {21},\n journal = {Journal of Law and Policy}\n}\n\n@inproceedings{stamatatos2017authorship,\n title={Authorship attribution using text distortion},\n author={Stamatatos, Efstathios},\n booktitle={Proc. of the 15th Conf. of the European Chapter of the Association for Computational Linguistics},\n volume={1}\n pages={1138--1149},\n year={2017}\n}\n", "homepage": "http://www.icsd.aegean.gr/lecturers/stamatatos/papers/JLP2013.pdf", "license": "", "features": {"author": {"num_classes": 13, "names": ["catherinebennett", "georgemonbiot", "hugoyoung", "jonathanfreedland", "martinkettle", "maryriddell", "nickcohen", "peterpreston", "pollytoynbee", "royhattersley", "simonhoggart", "willhutton", "zoewilliams"], "names_file": null, "id": null, "_type": "ClassLabel"}, "topic": {"num_classes": 5, "names": ["Politics", "Society", "UK", "World", "Books"], "names_file": null, "id": null, "_type": "ClassLabel"}, "article": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": {"features": null, "resources_checksums": {"train": {}, "test": {}, "validation": {}}}, "supervised_keys": {"input": ["article", "author"], "output": ""}, "builder_name": "guardian_authorship", "config_name": "cross_topic_1", "version": {"version_str": "1.0.0", "description": "The Original DS with the cross-topic scenario no.1", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 677054, "num_examples": 112, "dataset_name": "guardian_authorship"}, "test": {"name": "test", "num_bytes": 1283126, "num_examples": 207, "dataset_name": "guardian_authorship"}, "validation": {"name": "validation", "num_bytes": 374390, "num_examples": 62, "dataset_name": "guardian_authorship"}}, "download_checksums": {"https://www.dropbox.com/s/lc5mje0owl9shms/Guardian.zip?dl=1": {"num_bytes": 3100749, "checksum": "8bb1130a697ee5943bcb6e24989aa477341a8ce1b9e057955cf010b27e8a9d06"}}, "download_size": 3100749, "post_processing_size": 0, "dataset_size": 2334570, "size_in_bytes": 5435319}, "cross_genre_1": {"description": "A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. \n1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).\n2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).\n\n3- The same-topic/genre scenario is created by grouping all the datasts as follows. \nFor ex., to use same_topic and split the data 60-40 use:\ntrain_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[:60%]+validation[:60%]+test[:60%]')\ntests_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[-40%:]+validation[-40%:]+test[-40%:]') \n\nIMPORTANT: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced\n\n* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples \n", "citation": "@article{article,\n author = {Stamatatos, Efstathios},\n year = {2013},\n month = {01},\n pages = {421-439},\n title = {On the robustness of authorship attribution based on character n-gram features},\n volume = {21},\n journal = {Journal of Law and Policy}\n}\n\n@inproceedings{stamatatos2017authorship,\n title={Authorship attribution using text distortion},\n author={Stamatatos, Efstathios},\n booktitle={Proc. of the 15th Conf. of the European Chapter of the Association for Computational Linguistics},\n volume={1}\n pages={1138--1149},\n year={2017}\n}\n", "homepage": "http://www.icsd.aegean.gr/lecturers/stamatatos/papers/JLP2013.pdf", "license": "", "features": {"author": {"num_classes": 13, "names": ["catherinebennett", "georgemonbiot", "hugoyoung", "jonathanfreedland", "martinkettle", "maryriddell", "nickcohen", "peterpreston", "pollytoynbee", "royhattersley", "simonhoggart", "willhutton", "zoewilliams"], "names_file": null, "id": null, "_type": "ClassLabel"}, "topic": {"num_classes": 5, "names": ["Politics", "Society", "UK", "World", "Books"], "names_file": null, "id": null, "_type": "ClassLabel"}, "article": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": {"features": null, "resources_checksums": {"train": {}, "test": {}, "validation": {}}}, "supervised_keys": {"input": ["article", "author"], "output": ""}, "builder_name": "guardian_authorship", "config_name": "cross_genre_1", "version": {"version_str": "13.0.0", "description": "The Original DS with the cross-genre scenario no.1", "datasets_version_to_prepare": null, "major": 13, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 406144, "num_examples": 63, "dataset_name": "guardian_authorship"}, "test": {"name": "test", "num_bytes": 1657512, "num_examples": 269, "dataset_name": "guardian_authorship"}, "validation": {"name": "validation", "num_bytes": 677054, "num_examples": 112, "dataset_name": "guardian_authorship"}}, "download_checksums": {"https://www.dropbox.com/s/lc5mje0owl9shms/Guardian.zip?dl=1": {"num_bytes": 3100749, "checksum": "8bb1130a697ee5943bcb6e24989aa477341a8ce1b9e057955cf010b27e8a9d06"}}, "download_size": 3100749, "post_processing_size": 0, "dataset_size": 2740710, "size_in_bytes": 5841459}, "cross_topic_2": {"description": "A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. \n1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).\n2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).\n\n3- The same-topic/genre scenario is created by grouping all the datasts as follows. \nFor ex., to use same_topic and split the data 60-40 use:\ntrain_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[:60%]+validation[:60%]+test[:60%]')\ntests_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[-40%:]+validation[-40%:]+test[-40%:]') \n\nIMPORTANT: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced\n\n* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples \n", "citation": "@article{article,\n author = {Stamatatos, Efstathios},\n year = {2013},\n month = {01},\n pages = {421-439},\n title = {On the robustness of authorship attribution based on character n-gram features},\n volume = {21},\n journal = {Journal of Law and Policy}\n}\n\n@inproceedings{stamatatos2017authorship,\n title={Authorship attribution using text distortion},\n author={Stamatatos, Efstathios},\n booktitle={Proc. of the 15th Conf. of the European Chapter of the Association for Computational Linguistics},\n volume={1}\n pages={1138--1149},\n year={2017}\n}\n", "homepage": "http://www.icsd.aegean.gr/lecturers/stamatatos/papers/JLP2013.pdf", "license": "", "features": {"author": {"num_classes": 13, "names": ["catherinebennett", "georgemonbiot", "hugoyoung", "jonathanfreedland", "martinkettle", "maryriddell", "nickcohen", "peterpreston", "pollytoynbee", "royhattersley", "simonhoggart", "willhutton", "zoewilliams"], "names_file": null, "id": null, "_type": "ClassLabel"}, "topic": {"num_classes": 5, "names": ["Politics", "Society", "UK", "World", "Books"], "names_file": null, "id": null, "_type": "ClassLabel"}, "article": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": {"features": null, "resources_checksums": {"train": {}, "test": {}, "validation": {}}}, "supervised_keys": {"input": ["article", "author"], "output": ""}, "builder_name": "guardian_authorship", "config_name": "cross_topic_2", "version": {"version_str": "2.0.0", "description": "The Original DS with the cross-topic scenario no.2", "datasets_version_to_prepare": null, "major": 2, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 677054, "num_examples": 112, "dataset_name": "guardian_authorship"}, "test": {"name": "test", "num_bytes": 1104764, "num_examples": 179, "dataset_name": "guardian_authorship"}, "validation": {"name": "validation", "num_bytes": 552752, "num_examples": 90, "dataset_name": "guardian_authorship"}}, "download_checksums": {"https://www.dropbox.com/s/lc5mje0owl9shms/Guardian.zip?dl=1": {"num_bytes": 3100749, "checksum": "8bb1130a697ee5943bcb6e24989aa477341a8ce1b9e057955cf010b27e8a9d06"}}, "download_size": 3100749, "post_processing_size": 0, "dataset_size": 2334570, "size_in_bytes": 5435319}, "cross_topic_3": {"description": "A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. \n1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).\n2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).\n\n3- The same-topic/genre scenario is created by grouping all the datasts as follows. \nFor ex., to use same_topic and split the data 60-40 use:\ntrain_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[:60%]+validation[:60%]+test[:60%]')\ntests_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[-40%:]+validation[-40%:]+test[-40%:]') \n\nIMPORTANT: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced\n\n* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples \n", "citation": "@article{article,\n author = {Stamatatos, Efstathios},\n year = {2013},\n month = {01},\n pages = {421-439},\n title = {On the robustness of authorship attribution based on character n-gram features},\n volume = {21},\n journal = {Journal of Law and Policy}\n}\n\n@inproceedings{stamatatos2017authorship,\n title={Authorship attribution using text distortion},\n author={Stamatatos, Efstathios},\n booktitle={Proc. of the 15th Conf. of the European Chapter of the Association for Computational Linguistics},\n volume={1}\n pages={1138--1149},\n year={2017}\n}\n", "homepage": "http://www.icsd.aegean.gr/lecturers/stamatatos/papers/JLP2013.pdf", "license": "", "features": {"author": {"num_classes": 13, "names": ["catherinebennett", "georgemonbiot", "hugoyoung", "jonathanfreedland", "martinkettle", "maryriddell", "nickcohen", "peterpreston", "pollytoynbee", "royhattersley", "simonhoggart", "willhutton", "zoewilliams"], "names_file": null, "id": null, "_type": "ClassLabel"}, "topic": {"num_classes": 5, "names": ["Politics", "Society", "UK", "World", "Books"], "names_file": null, "id": null, "_type": "ClassLabel"}, "article": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": {"features": null, "resources_checksums": {"train": {}, "test": {}, "validation": {}}}, "supervised_keys": {"input": ["article", "author"], "output": ""}, "builder_name": "guardian_authorship", "config_name": "cross_topic_3", "version": {"version_str": "3.0.0", "description": "The Original DS with the cross-topic scenario no.3", "datasets_version_to_prepare": null, "major": 3, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 677054, "num_examples": 112, "dataset_name": "guardian_authorship"}, "test": {"name": "test", "num_bytes": 927138, "num_examples": 152, "dataset_name": "guardian_authorship"}, "validation": {"name": "validation", "num_bytes": 730378, "num_examples": 117, "dataset_name": "guardian_authorship"}}, "download_checksums": {"https://www.dropbox.com/s/lc5mje0owl9shms/Guardian.zip?dl=1": {"num_bytes": 3100749, "checksum": "8bb1130a697ee5943bcb6e24989aa477341a8ce1b9e057955cf010b27e8a9d06"}}, "download_size": 3100749, "post_processing_size": 0, "dataset_size": 2334570, "size_in_bytes": 5435319}, "cross_topic_4": {"description": "A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. \n1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).\n2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).\n\n3- The same-topic/genre scenario is created by grouping all the datasts as follows. \nFor ex., to use same_topic and split the data 60-40 use:\ntrain_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[:60%]+validation[:60%]+test[:60%]')\ntests_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[-40%:]+validation[-40%:]+test[-40%:]') \n\nIMPORTANT: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced\n\n* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples \n", "citation": "@article{article,\n author = {Stamatatos, Efstathios},\n year = {2013},\n month = {01},\n pages = {421-439},\n title = {On the robustness of authorship attribution based on character n-gram features},\n volume = {21},\n journal = {Journal of Law and Policy}\n}\n\n@inproceedings{stamatatos2017authorship,\n title={Authorship attribution using text distortion},\n author={Stamatatos, Efstathios},\n booktitle={Proc. of the 15th Conf. of the European Chapter of the Association for Computational Linguistics},\n volume={1}\n pages={1138--1149},\n year={2017}\n}\n", "homepage": "http://www.icsd.aegean.gr/lecturers/stamatatos/papers/JLP2013.pdf", "license": "", "features": {"author": {"num_classes": 13, "names": ["catherinebennett", "georgemonbiot", "hugoyoung", "jonathanfreedland", "martinkettle", "maryriddell", "nickcohen", "peterpreston", "pollytoynbee", "royhattersley", "simonhoggart", "willhutton", "zoewilliams"], "names_file": null, "id": null, "_type": "ClassLabel"}, "topic": {"num_classes": 5, "names": ["Politics", "Society", "UK", "World", "Books"], "names_file": null, "id": null, "_type": "ClassLabel"}, "article": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": {"features": null, "resources_checksums": {"train": {}, "test": {}, "validation": {}}}, "supervised_keys": {"input": ["article", "author"], "output": ""}, "builder_name": "guardian_authorship", "config_name": "cross_topic_4", "version": {"version_str": "4.0.0", "description": "The Original DS with the cross-topic scenario no.4", "datasets_version_to_prepare": null, "major": 4, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 374390, "num_examples": 62, "dataset_name": "guardian_authorship"}, "test": {"name": "test", "num_bytes": 1283126, "num_examples": 207, "dataset_name": "guardian_authorship"}, "validation": {"name": "validation", "num_bytes": 677054, "num_examples": 112, "dataset_name": "guardian_authorship"}}, "download_checksums": {"https://www.dropbox.com/s/lc5mje0owl9shms/Guardian.zip?dl=1": {"num_bytes": 3100749, "checksum": "8bb1130a697ee5943bcb6e24989aa477341a8ce1b9e057955cf010b27e8a9d06"}}, "download_size": 3100749, "post_processing_size": 0, "dataset_size": 2334570, "size_in_bytes": 5435319}, "cross_topic_5": {"description": "A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. \n1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).\n2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).\n\n3- The same-topic/genre scenario is created by grouping all the datasts as follows. \nFor ex., to use same_topic and split the data 60-40 use:\ntrain_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[:60%]+validation[:60%]+test[:60%]')\ntests_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[-40%:]+validation[-40%:]+test[-40%:]') \n\nIMPORTANT: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced\n\n* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples \n", "citation": "@article{article,\n author = {Stamatatos, Efstathios},\n year = {2013},\n month = {01},\n pages = {421-439},\n title = {On the robustness of authorship attribution based on character n-gram features},\n volume = {21},\n journal = {Journal of Law and Policy}\n}\n\n@inproceedings{stamatatos2017authorship,\n title={Authorship attribution using text distortion},\n author={Stamatatos, Efstathios},\n booktitle={Proc. of the 15th Conf. of the European Chapter of the Association for Computational Linguistics},\n volume={1}\n pages={1138--1149},\n year={2017}\n}\n", "homepage": "http://www.icsd.aegean.gr/lecturers/stamatatos/papers/JLP2013.pdf", "license": "", "features": {"author": {"num_classes": 13, "names": ["catherinebennett", "georgemonbiot", "hugoyoung", "jonathanfreedland", "martinkettle", "maryriddell", "nickcohen", "peterpreston", "pollytoynbee", "royhattersley", "simonhoggart", "willhutton", "zoewilliams"], "names_file": null, "id": null, "_type": "ClassLabel"}, "topic": {"num_classes": 5, "names": ["Politics", "Society", "UK", "World", "Books"], "names_file": null, "id": null, "_type": "ClassLabel"}, "article": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": {"features": null, "resources_checksums": {"train": {}, "test": {}, "validation": {}}}, "supervised_keys": {"input": ["article", "author"], "output": ""}, "builder_name": "guardian_authorship", "config_name": "cross_topic_5", "version": {"version_str": "5.0.0", "description": "The Original DS with the cross-topic scenario no.5", "datasets_version_to_prepare": null, "major": 5, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 374390, "num_examples": 62, "dataset_name": "guardian_authorship"}, "test": {"name": "test", "num_bytes": 1407428, "num_examples": 229, "dataset_name": "guardian_authorship"}, "validation": {"name": "validation", "num_bytes": 552752, "num_examples": 90, "dataset_name": "guardian_authorship"}}, "download_checksums": {"https://www.dropbox.com/s/lc5mje0owl9shms/Guardian.zip?dl=1": {"num_bytes": 3100749, "checksum": "8bb1130a697ee5943bcb6e24989aa477341a8ce1b9e057955cf010b27e8a9d06"}}, "download_size": 3100749, "post_processing_size": 0, "dataset_size": 2334570, "size_in_bytes": 5435319}, "cross_topic_6": {"description": "A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. \n1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).\n2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).\n\n3- The same-topic/genre scenario is created by grouping all the datasts as follows. \nFor ex., to use same_topic and split the data 60-40 use:\ntrain_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[:60%]+validation[:60%]+test[:60%]')\ntests_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[-40%:]+validation[-40%:]+test[-40%:]') \n\nIMPORTANT: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced\n\n* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples \n", "citation": "@article{article,\n author = {Stamatatos, Efstathios},\n year = {2013},\n month = {01},\n pages = {421-439},\n title = {On the robustness of authorship attribution based on character n-gram features},\n volume = {21},\n journal = {Journal of Law and Policy}\n}\n\n@inproceedings{stamatatos2017authorship,\n title={Authorship attribution using text distortion},\n author={Stamatatos, Efstathios},\n booktitle={Proc. of the 15th Conf. of the European Chapter of the Association for Computational Linguistics},\n volume={1}\n pages={1138--1149},\n year={2017}\n}\n", "homepage": "http://www.icsd.aegean.gr/lecturers/stamatatos/papers/JLP2013.pdf", "license": "", "features": {"author": {"num_classes": 13, "names": ["catherinebennett", "georgemonbiot", "hugoyoung", "jonathanfreedland", "martinkettle", "maryriddell", "nickcohen", "peterpreston", "pollytoynbee", "royhattersley", "simonhoggart", "willhutton", "zoewilliams"], "names_file": null, "id": null, "_type": "ClassLabel"}, "topic": {"num_classes": 5, "names": ["Politics", "Society", "UK", "World", "Books"], "names_file": null, "id": null, "_type": "ClassLabel"}, "article": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": {"features": null, "resources_checksums": {"train": {}, "test": {}, "validation": {}}}, "supervised_keys": {"input": ["article", "author"], "output": ""}, "builder_name": "guardian_authorship", "config_name": "cross_topic_6", "version": {"version_str": "6.0.0", "description": "The Original DS with the cross-topic scenario no.6", "datasets_version_to_prepare": null, "major": 6, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 374390, "num_examples": 62, "dataset_name": "guardian_authorship"}, "test": {"name": "test", "num_bytes": 1229802, "num_examples": 202, "dataset_name": "guardian_authorship"}, "validation": {"name": "validation", "num_bytes": 730378, "num_examples": 117, "dataset_name": "guardian_authorship"}}, "download_checksums": {"https://www.dropbox.com/s/lc5mje0owl9shms/Guardian.zip?dl=1": {"num_bytes": 3100749, "checksum": "8bb1130a697ee5943bcb6e24989aa477341a8ce1b9e057955cf010b27e8a9d06"}}, "download_size": 3100749, "post_processing_size": 0, "dataset_size": 2334570, "size_in_bytes": 5435319}, "cross_topic_7": {"description": "A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. \n1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).\n2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).\n\n3- The same-topic/genre scenario is created by grouping all the datasts as follows. \nFor ex., to use same_topic and split the data 60-40 use:\ntrain_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[:60%]+validation[:60%]+test[:60%]')\ntests_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[-40%:]+validation[-40%:]+test[-40%:]') \n\nIMPORTANT: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced\n\n* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples \n", "citation": "@article{article,\n author = {Stamatatos, Efstathios},\n year = {2013},\n month = {01},\n pages = {421-439},\n title = {On the robustness of authorship attribution based on character n-gram features},\n volume = {21},\n journal = {Journal of Law and Policy}\n}\n\n@inproceedings{stamatatos2017authorship,\n title={Authorship attribution using text distortion},\n author={Stamatatos, Efstathios},\n booktitle={Proc. of the 15th Conf. of the European Chapter of the Association for Computational Linguistics},\n volume={1}\n pages={1138--1149},\n year={2017}\n}\n", "homepage": "http://www.icsd.aegean.gr/lecturers/stamatatos/papers/JLP2013.pdf", "license": "", "features": {"author": {"num_classes": 13, "names": ["catherinebennett", "georgemonbiot", "hugoyoung", "jonathanfreedland", "martinkettle", "maryriddell", "nickcohen", "peterpreston", "pollytoynbee", "royhattersley", "simonhoggart", "willhutton", "zoewilliams"], "names_file": null, "id": null, "_type": "ClassLabel"}, "topic": {"num_classes": 5, "names": ["Politics", "Society", "UK", "World", "Books"], "names_file": null, "id": null, "_type": "ClassLabel"}, "article": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": {"features": null, "resources_checksums": {"train": {}, "test": {}, "validation": {}}}, "supervised_keys": {"input": ["article", "author"], "output": ""}, "builder_name": "guardian_authorship", "config_name": "cross_topic_7", "version": {"version_str": "7.0.0", "description": "The Original DS with the cross-topic scenario no.7", "datasets_version_to_prepare": null, "major": 7, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 552752, "num_examples": 90, "dataset_name": "guardian_authorship"}, "test": {"name": "test", "num_bytes": 1104764, "num_examples": 179, "dataset_name": "guardian_authorship"}, "validation": {"name": "validation", "num_bytes": 677054, "num_examples": 112, "dataset_name": "guardian_authorship"}}, "download_checksums": {"https://www.dropbox.com/s/lc5mje0owl9shms/Guardian.zip?dl=1": {"num_bytes": 3100749, "checksum": "8bb1130a697ee5943bcb6e24989aa477341a8ce1b9e057955cf010b27e8a9d06"}}, "download_size": 3100749, "post_processing_size": 0, "dataset_size": 2334570, "size_in_bytes": 5435319}, "cross_topic_8": {"description": "A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. \n1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).\n2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).\n\n3- The same-topic/genre scenario is created by grouping all the datasts as follows. \nFor ex., to use same_topic and split the data 60-40 use:\ntrain_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[:60%]+validation[:60%]+test[:60%]')\ntests_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[-40%:]+validation[-40%:]+test[-40%:]') \n\nIMPORTANT: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced\n\n* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples \n", "citation": "@article{article,\n author = {Stamatatos, Efstathios},\n year = {2013},\n month = {01},\n pages = {421-439},\n title = {On the robustness of authorship attribution based on character n-gram features},\n volume = {21},\n journal = {Journal of Law and Policy}\n}\n\n@inproceedings{stamatatos2017authorship,\n title={Authorship attribution using text distortion},\n author={Stamatatos, Efstathios},\n booktitle={Proc. of the 15th Conf. of the European Chapter of the Association for Computational Linguistics},\n volume={1}\n pages={1138--1149},\n year={2017}\n}\n", "homepage": "http://www.icsd.aegean.gr/lecturers/stamatatos/papers/JLP2013.pdf", "license": "", "features": {"author": {"num_classes": 13, "names": ["catherinebennett", "georgemonbiot", "hugoyoung", "jonathanfreedland", "martinkettle", "maryriddell", "nickcohen", "peterpreston", "pollytoynbee", "royhattersley", "simonhoggart", "willhutton", "zoewilliams"], "names_file": null, "id": null, "_type": "ClassLabel"}, "topic": {"num_classes": 5, "names": ["Politics", "Society", "UK", "World", "Books"], "names_file": null, "id": null, "_type": "ClassLabel"}, "article": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": {"features": null, "resources_checksums": {"train": {}, "test": {}, "validation": {}}}, "supervised_keys": {"input": ["article", "author"], "output": ""}, "builder_name": "guardian_authorship", "config_name": "cross_topic_8", "version": {"version_str": "8.0.0", "description": "The Original DS with the cross-topic scenario no.8", "datasets_version_to_prepare": null, "major": 8, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 552752, "num_examples": 90, "dataset_name": "guardian_authorship"}, "test": {"name": "test", "num_bytes": 1407428, "num_examples": 229, "dataset_name": "guardian_authorship"}, "validation": {"name": "validation", "num_bytes": 374390, "num_examples": 62, "dataset_name": "guardian_authorship"}}, "download_checksums": {"https://www.dropbox.com/s/lc5mje0owl9shms/Guardian.zip?dl=1": {"num_bytes": 3100749, "checksum": "8bb1130a697ee5943bcb6e24989aa477341a8ce1b9e057955cf010b27e8a9d06"}}, "download_size": 3100749, "post_processing_size": 0, "dataset_size": 2334570, "size_in_bytes": 5435319}, "cross_topic_9": {"description": "A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. \n1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).\n2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).\n\n3- The same-topic/genre scenario is created by grouping all the datasts as follows. \nFor ex., to use same_topic and split the data 60-40 use:\ntrain_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[:60%]+validation[:60%]+test[:60%]')\ntests_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[-40%:]+validation[-40%:]+test[-40%:]') \n\nIMPORTANT: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced\n\n* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples \n", "citation": "@article{article,\n author = {Stamatatos, Efstathios},\n year = {2013},\n month = {01},\n pages = {421-439},\n title = {On the robustness of authorship attribution based on character n-gram features},\n volume = {21},\n journal = {Journal of Law and Policy}\n}\n\n@inproceedings{stamatatos2017authorship,\n title={Authorship attribution using text distortion},\n author={Stamatatos, Efstathios},\n booktitle={Proc. of the 15th Conf. of the European Chapter of the Association for Computational Linguistics},\n volume={1}\n pages={1138--1149},\n year={2017}\n}\n", "homepage": "http://www.icsd.aegean.gr/lecturers/stamatatos/papers/JLP2013.pdf", "license": "", "features": {"author": {"num_classes": 13, "names": ["catherinebennett", "georgemonbiot", "hugoyoung", "jonathanfreedland", "martinkettle", "maryriddell", "nickcohen", "peterpreston", "pollytoynbee", "royhattersley", "simonhoggart", "willhutton", "zoewilliams"], "names_file": null, "id": null, "_type": "ClassLabel"}, "topic": {"num_classes": 5, "names": ["Politics", "Society", "UK", "World", "Books"], "names_file": null, "id": null, "_type": "ClassLabel"}, "article": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": {"features": null, "resources_checksums": {"train": {}, "test": {}, "validation": {}}}, "supervised_keys": {"input": ["article", "author"], "output": ""}, "builder_name": "guardian_authorship", "config_name": "cross_topic_9", "version": {"version_str": "9.0.0", "description": "The Original DS with the cross-topic scenario no.9", "datasets_version_to_prepare": null, "major": 9, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 552752, "num_examples": 90, "dataset_name": "guardian_authorship"}, "test": {"name": "test", "num_bytes": 1051440, "num_examples": 174, "dataset_name": "guardian_authorship"}, "validation": {"name": "validation", "num_bytes": 730378, "num_examples": 117, "dataset_name": "guardian_authorship"}}, "download_checksums": {"https://www.dropbox.com/s/lc5mje0owl9shms/Guardian.zip?dl=1": {"num_bytes": 3100749, "checksum": "8bb1130a697ee5943bcb6e24989aa477341a8ce1b9e057955cf010b27e8a9d06"}}, "download_size": 3100749, "post_processing_size": 0, "dataset_size": 2334570, "size_in_bytes": 5435319}, "cross_topic_10": {"description": "A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. \n1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).\n2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).\n\n3- The same-topic/genre scenario is created by grouping all the datasts as follows. \nFor ex., to use same_topic and split the data 60-40 use:\ntrain_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[:60%]+validation[:60%]+test[:60%]')\ntests_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[-40%:]+validation[-40%:]+test[-40%:]') \n\nIMPORTANT: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced\n\n* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples \n", "citation": "@article{article,\n author = {Stamatatos, Efstathios},\n year = {2013},\n month = {01},\n pages = {421-439},\n title = {On the robustness of authorship attribution based on character n-gram features},\n volume = {21},\n journal = {Journal of Law and Policy}\n}\n\n@inproceedings{stamatatos2017authorship,\n title={Authorship attribution using text distortion},\n author={Stamatatos, Efstathios},\n booktitle={Proc. of the 15th Conf. of the European Chapter of the Association for Computational Linguistics},\n volume={1}\n pages={1138--1149},\n year={2017}\n}\n", "homepage": "http://www.icsd.aegean.gr/lecturers/stamatatos/papers/JLP2013.pdf", "license": "", "features": {"author": {"num_classes": 13, "names": ["catherinebennett", "georgemonbiot", "hugoyoung", "jonathanfreedland", "martinkettle", "maryriddell", "nickcohen", "peterpreston", "pollytoynbee", "royhattersley", "simonhoggart", "willhutton", "zoewilliams"], "names_file": null, "id": null, "_type": "ClassLabel"}, "topic": {"num_classes": 5, "names": ["Politics", "Society", "UK", "World", "Books"], "names_file": null, "id": null, "_type": "ClassLabel"}, "article": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": {"features": null, "resources_checksums": {"train": {}, "test": {}, "validation": {}}}, "supervised_keys": {"input": ["article", "author"], "output": ""}, "builder_name": "guardian_authorship", "config_name": "cross_topic_10", "version": {"version_str": "10.0.0", "description": "The Original DS with the cross-topic scenario no.10", "datasets_version_to_prepare": null, "major": 10, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 730378, "num_examples": 117, "dataset_name": "guardian_authorship"}, "test": {"name": "test", "num_bytes": 927138, "num_examples": 152, "dataset_name": "guardian_authorship"}, "validation": {"name": "validation", "num_bytes": 677054, "num_examples": 112, "dataset_name": "guardian_authorship"}}, "download_checksums": {"https://www.dropbox.com/s/lc5mje0owl9shms/Guardian.zip?dl=1": {"num_bytes": 3100749, "checksum": "8bb1130a697ee5943bcb6e24989aa477341a8ce1b9e057955cf010b27e8a9d06"}}, "download_size": 3100749, "post_processing_size": 0, "dataset_size": 2334570, "size_in_bytes": 5435319}, "cross_topic_11": {"description": "A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. \n1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).\n2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).\n\n3- The same-topic/genre scenario is created by grouping all the datasts as follows. \nFor ex., to use same_topic and split the data 60-40 use:\ntrain_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[:60%]+validation[:60%]+test[:60%]')\ntests_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[-40%:]+validation[-40%:]+test[-40%:]') \n\nIMPORTANT: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced\n\n* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples \n", "citation": "@article{article,\n author = {Stamatatos, Efstathios},\n year = {2013},\n month = {01},\n pages = {421-439},\n title = {On the robustness of authorship attribution based on character n-gram features},\n volume = {21},\n journal = {Journal of Law and Policy}\n}\n\n@inproceedings{stamatatos2017authorship,\n title={Authorship attribution using text distortion},\n author={Stamatatos, Efstathios},\n booktitle={Proc. of the 15th Conf. of the European Chapter of the Association for Computational Linguistics},\n volume={1}\n pages={1138--1149},\n year={2017}\n}\n", "homepage": "http://www.icsd.aegean.gr/lecturers/stamatatos/papers/JLP2013.pdf", "license": "", "features": {"author": {"num_classes": 13, "names": ["catherinebennett", "georgemonbiot", "hugoyoung", "jonathanfreedland", "martinkettle", "maryriddell", "nickcohen", "peterpreston", "pollytoynbee", "royhattersley", "simonhoggart", "willhutton", "zoewilliams"], "names_file": null, "id": null, "_type": "ClassLabel"}, "topic": {"num_classes": 5, "names": ["Politics", "Society", "UK", "World", "Books"], "names_file": null, "id": null, "_type": "ClassLabel"}, "article": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": {"features": null, "resources_checksums": {"train": {}, "test": {}, "validation": {}}}, "supervised_keys": {"input": ["article", "author"], "output": ""}, "builder_name": "guardian_authorship", "config_name": "cross_topic_11", "version": {"version_str": "11.0.0", "description": "The Original DS with the cross-topic scenario no.11", "datasets_version_to_prepare": null, "major": 11, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 730378, "num_examples": 117, "dataset_name": "guardian_authorship"}, "test": {"name": "test", "num_bytes": 1229802, "num_examples": 202, "dataset_name": "guardian_authorship"}, "validation": {"name": "validation", "num_bytes": 374390, "num_examples": 62, "dataset_name": "guardian_authorship"}}, "download_checksums": {"https://www.dropbox.com/s/lc5mje0owl9shms/Guardian.zip?dl=1": {"num_bytes": 3100749, "checksum": "8bb1130a697ee5943bcb6e24989aa477341a8ce1b9e057955cf010b27e8a9d06"}}, "download_size": 3100749, "post_processing_size": 0, "dataset_size": 2334570, "size_in_bytes": 5435319}, "cross_topic_12": {"description": "A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. \n1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).\n2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).\n\n3- The same-topic/genre scenario is created by grouping all the datasts as follows. \nFor ex., to use same_topic and split the data 60-40 use:\ntrain_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[:60%]+validation[:60%]+test[:60%]')\ntests_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[-40%:]+validation[-40%:]+test[-40%:]') \n\nIMPORTANT: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced\n\n* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples \n", "citation": "@article{article,\n author = {Stamatatos, Efstathios},\n year = {2013},\n month = {01},\n pages = {421-439},\n title = {On the robustness of authorship attribution based on character n-gram features},\n volume = {21},\n journal = {Journal of Law and Policy}\n}\n\n@inproceedings{stamatatos2017authorship,\n title={Authorship attribution using text distortion},\n author={Stamatatos, Efstathios},\n booktitle={Proc. of the 15th Conf. of the European Chapter of the Association for Computational Linguistics},\n volume={1}\n pages={1138--1149},\n year={2017}\n}\n", "homepage": "http://www.icsd.aegean.gr/lecturers/stamatatos/papers/JLP2013.pdf", "license": "", "features": {"author": {"num_classes": 13, "names": ["catherinebennett", "georgemonbiot", "hugoyoung", "jonathanfreedland", "martinkettle", "maryriddell", "nickcohen", "peterpreston", "pollytoynbee", "royhattersley", "simonhoggart", "willhutton", "zoewilliams"], "names_file": null, "id": null, "_type": "ClassLabel"}, "topic": {"num_classes": 5, "names": ["Politics", "Society", "UK", "World", "Books"], "names_file": null, "id": null, "_type": "ClassLabel"}, "article": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": {"features": null, "resources_checksums": {"train": {}, "test": {}, "validation": {}}}, "supervised_keys": {"input": ["article", "author"], "output": ""}, "builder_name": "guardian_authorship", "config_name": "cross_topic_12", "version": {"version_str": "12.0.0", "description": "The Original DS with the cross-topic scenario no.12", "datasets_version_to_prepare": null, "major": 12, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 730378, "num_examples": 117, "dataset_name": "guardian_authorship"}, "test": {"name": "test", "num_bytes": 1051440, "num_examples": 174, "dataset_name": "guardian_authorship"}, "validation": {"name": "validation", "num_bytes": 552752, "num_examples": 90, "dataset_name": "guardian_authorship"}}, "download_checksums": {"https://www.dropbox.com/s/lc5mje0owl9shms/Guardian.zip?dl=1": {"num_bytes": 3100749, "checksum": "8bb1130a697ee5943bcb6e24989aa477341a8ce1b9e057955cf010b27e8a9d06"}}, "download_size": 3100749, "post_processing_size": 0, "dataset_size": 2334570, "size_in_bytes": 5435319}, "cross_genre_2": {"description": "A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. \n1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).\n2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).\n\n3- The same-topic/genre scenario is created by grouping all the datasts as follows. \nFor ex., to use same_topic and split the data 60-40 use:\ntrain_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[:60%]+validation[:60%]+test[:60%]')\ntests_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[-40%:]+validation[-40%:]+test[-40%:]') \n\nIMPORTANT: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced\n\n* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples \n", "citation": "@article{article,\n author = {Stamatatos, Efstathios},\n year = {2013},\n month = {01},\n pages = {421-439},\n title = {On the robustness of authorship attribution based on character n-gram features},\n volume = {21},\n journal = {Journal of Law and Policy}\n}\n\n@inproceedings{stamatatos2017authorship,\n title={Authorship attribution using text distortion},\n author={Stamatatos, Efstathios},\n booktitle={Proc. of the 15th Conf. of the European Chapter of the Association for Computational Linguistics},\n volume={1}\n pages={1138--1149},\n year={2017}\n}\n", "homepage": "http://www.icsd.aegean.gr/lecturers/stamatatos/papers/JLP2013.pdf", "license": "", "features": {"author": {"num_classes": 13, "names": ["catherinebennett", "georgemonbiot", "hugoyoung", "jonathanfreedland", "martinkettle", "maryriddell", "nickcohen", "peterpreston", "pollytoynbee", "royhattersley", "simonhoggart", "willhutton", "zoewilliams"], "names_file": null, "id": null, "_type": "ClassLabel"}, "topic": {"num_classes": 5, "names": ["Politics", "Society", "UK", "World", "Books"], "names_file": null, "id": null, "_type": "ClassLabel"}, "article": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": {"features": null, "resources_checksums": {"train": {}, "test": {}, "validation": {}}}, "supervised_keys": {"input": ["article", "author"], "output": ""}, "builder_name": "guardian_authorship", "config_name": "cross_genre_2", "version": {"version_str": "14.0.0", "description": "The Original DS with the cross-genre scenario no.2", "datasets_version_to_prepare": null, "major": 14, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 406144, "num_examples": 63, "dataset_name": "guardian_authorship"}, "test": {"name": "test", "num_bytes": 1960176, "num_examples": 319, "dataset_name": "guardian_authorship"}, "validation": {"name": "validation", "num_bytes": 374390, "num_examples": 62, "dataset_name": "guardian_authorship"}}, "download_checksums": {"https://www.dropbox.com/s/lc5mje0owl9shms/Guardian.zip?dl=1": {"num_bytes": 3100749, "checksum": "8bb1130a697ee5943bcb6e24989aa477341a8ce1b9e057955cf010b27e8a9d06"}}, "download_size": 3100749, "post_processing_size": 0, "dataset_size": 2740710, "size_in_bytes": 5841459}, "cross_genre_3": {"description": "A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. \n1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).\n2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).\n\n3- The same-topic/genre scenario is created by grouping all the datasts as follows. \nFor ex., to use same_topic and split the data 60-40 use:\ntrain_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[:60%]+validation[:60%]+test[:60%]')\ntests_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[-40%:]+validation[-40%:]+test[-40%:]') \n\nIMPORTANT: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced\n\n* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples \n", "citation": "@article{article,\n author = {Stamatatos, Efstathios},\n year = {2013},\n month = {01},\n pages = {421-439},\n title = {On the robustness of authorship attribution based on character n-gram features},\n volume = {21},\n journal = {Journal of Law and Policy}\n}\n\n@inproceedings{stamatatos2017authorship,\n title={Authorship attribution using text distortion},\n author={Stamatatos, Efstathios},\n booktitle={Proc. of the 15th Conf. of the European Chapter of the Association for Computational Linguistics},\n volume={1}\n pages={1138--1149},\n year={2017}\n}\n", "homepage": "http://www.icsd.aegean.gr/lecturers/stamatatos/papers/JLP2013.pdf", "license": "", "features": {"author": {"num_classes": 13, "names": ["catherinebennett", "georgemonbiot", "hugoyoung", "jonathanfreedland", "martinkettle", "maryriddell", "nickcohen", "peterpreston", "pollytoynbee", "royhattersley", "simonhoggart", "willhutton", "zoewilliams"], "names_file": null, "id": null, "_type": "ClassLabel"}, "topic": {"num_classes": 5, "names": ["Politics", "Society", "UK", "World", "Books"], "names_file": null, "id": null, "_type": "ClassLabel"}, "article": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": {"features": null, "resources_checksums": {"train": {}, "test": {}, "validation": {}}}, "supervised_keys": {"input": ["article", "author"], "output": ""}, "builder_name": "guardian_authorship", "config_name": "cross_genre_3", "version": {"version_str": "15.0.0", "description": "The Original DS with the cross-genre scenario no.3", "datasets_version_to_prepare": null, "major": 15, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 406144, "num_examples": 63, "dataset_name": "guardian_authorship"}, "test": {"name": "test", "num_bytes": 1781814, "num_examples": 291, "dataset_name": "guardian_authorship"}, "validation": {"name": "validation", "num_bytes": 552752, "num_examples": 90, "dataset_name": "guardian_authorship"}}, "download_checksums": {"https://www.dropbox.com/s/lc5mje0owl9shms/Guardian.zip?dl=1": {"num_bytes": 3100749, "checksum": "8bb1130a697ee5943bcb6e24989aa477341a8ce1b9e057955cf010b27e8a9d06"}}, "download_size": 3100749, "post_processing_size": 0, "dataset_size": 2740710, "size_in_bytes": 5841459}, "cross_genre_4": {"description": "A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. \n1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).\n2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).\n\n3- The same-topic/genre scenario is created by grouping all the datasts as follows. \nFor ex., to use same_topic and split the data 60-40 use:\ntrain_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[:60%]+validation[:60%]+test[:60%]')\ntests_ds = load_dataset('guardian_authorship', name=\"cross_topic_<<#>>\", \n split='train[-40%:]+validation[-40%:]+test[-40%:]') \n\nIMPORTANT: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced\n\n* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples \n", "citation": "@article{article,\n author = {Stamatatos, Efstathios},\n year = {2013},\n month = {01},\n pages = {421-439},\n title = {On the robustness of authorship attribution based on character n-gram features},\n volume = {21},\n journal = {Journal of Law and Policy}\n}\n\n@inproceedings{stamatatos2017authorship,\n title={Authorship attribution using text distortion},\n author={Stamatatos, Efstathios},\n booktitle={Proc. of the 15th Conf. of the European Chapter of the Association for Computational Linguistics},\n volume={1}\n pages={1138--1149},\n year={2017}\n}\n", "homepage": "http://www.icsd.aegean.gr/lecturers/stamatatos/papers/JLP2013.pdf", "license": "", "features": {"author": {"num_classes": 13, "names": ["catherinebennett", "georgemonbiot", "hugoyoung", "jonathanfreedland", "martinkettle", "maryriddell", "nickcohen", "peterpreston", "pollytoynbee", "royhattersley", "simonhoggart", "willhutton", "zoewilliams"], "names_file": null, "id": null, "_type": "ClassLabel"}, "topic": {"num_classes": 5, "names": ["Politics", "Society", "UK", "World", "Books"], "names_file": null, "id": null, "_type": "ClassLabel"}, "article": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": {"features": null, "resources_checksums": {"train": {}, "test": {}, "validation": {}}}, "supervised_keys": {"input": ["article", "author"], "output": ""}, "builder_name": "guardian_authorship", "config_name": "cross_genre_4", "version": {"version_str": "16.0.0", "description": "The Original DS with the cross-genre scenario no.4", "datasets_version_to_prepare": null, "major": 16, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 406144, "num_examples": 63, "dataset_name": "guardian_authorship"}, "test": {"name": "test", "num_bytes": 1604188, "num_examples": 264, "dataset_name": "guardian_authorship"}, "validation": {"name": "validation", "num_bytes": 730378, "num_examples": 117, "dataset_name": "guardian_authorship"}}, "download_checksums": {"https://www.dropbox.com/s/lc5mje0owl9shms/Guardian.zip?dl=1": {"num_bytes": 3100749, "checksum": "8bb1130a697ee5943bcb6e24989aa477341a8ce1b9e057955cf010b27e8a9d06"}}, "download_size": 3100749, "post_processing_size": 0, "dataset_size": 2740710, "size_in_bytes": 5841459}}

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""This is an authorship attribution dataset based on the work of Stamatatos 2013. """
+from __future__ import absolute_import, division, print_function
+import os
+import datasets
+_CITATION = """\
+@article{article,
+    author = {Stamatatos, Efstathios},
+    year = {2013},
+    month = {01},
+    pages = {421-439},
+    title = {On the robustness of authorship attribution based on character n-gram features},
+    volume = {21},
+    journal = {Journal of Law and Policy}
+}
+@inproceedings{stamatatos2017authorship,
+    title={Authorship attribution using text distortion},
+    author={Stamatatos, Efstathios},
+    booktitle={Proc. of the 15th Conf. of the European Chapter of the Association for Computational Linguistics},
+    volume={1}
+    pages={1138--1149},
+    year={2017}
+}
+"""
+_DESCRIPTION = """\
+A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013.
+1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).
+2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).
+3- The same-topic/genre scenario is created by grouping all the datasts as follows.
+For ex., to use same_topic and split the data 60-40 use:
+train_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>",
+                        split='train[:60%]+validation[:60%]+test[:60%]')
+tests_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>",
+                        split='train[-40%:]+validation[-40%:]+test[-40%:]')
+IMPORTANT: train+validation+test[:60%] will generate the wrong splits becasue the data is imbalanced
+* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples
+"""
+_URL = "https://www.dropbox.com/s/lc5mje0owl9shms/Guardian.zip?dl=1"
+# Using a specific configuration class is optional, you can also use the base class if you don't need
+# to add specific attributes.
+# here we give an example for three sub-set of the dataset with difference sizes.
+class GuardianAuthorshipConfig(datasets.BuilderConfig):
+    """ BuilderConfig for NewDataset"""
+    def __init__(self, train_folder, valid_folder, test_folder, **kwargs):
+        """
+        Args:
+            Train_folder: Topic/genre used for training
+            valid_folder:       ~      ~   for validation
+            test_folder:        ~      ~   for testing
+            **kwargs: keyword arguments forwarded to super.
+        """
+        super(GuardianAuthorshipConfig, self).__init__(**kwargs)
+        self.train_folder = train_folder
+        self.valid_folder = valid_folder
+        self.test_folder = test_folder
+class GuardianAuthorship(datasets.GeneratorBasedBuilder):
+    """dataset for same- and cross-topic authorship attribution"""
+    config_counter = 0
+    BUILDER_CONFIG_CLASS = GuardianAuthorshipConfig
+    BUILDER_CONFIGS = [
+        # cross-topic
+        GuardianAuthorshipConfig(
+            name="cross_topic_{}".format(1),
+            version=datasets.Version(
+                "{}.0.0".format(1), description="The Original DS with the cross-topic scenario no.{}".format(1)
+            ),
+            train_folder="Politics",
+            valid_folder="Society",
+            test_folder="UK,World",
+        ),
+        GuardianAuthorshipConfig(
+            name="cross_topic_{}".format(2),
+            version=datasets.Version(
+                "{}.0.0".format(2), description="The Original DS with the cross-topic scenario no.{}".format(2)
+            ),
+            train_folder="Politics",
+            valid_folder="UK",
+            test_folder="Society,World",
+        ),
+        GuardianAuthorshipConfig(
+            name="cross_topic_{}".format(3),
+            version=datasets.Version(
+                "{}.0.0".format(3), description="The Original DS with the cross-topic scenario no.{}".format(3)
+            ),
+            train_folder="Politics",
+            valid_folder="World",
+            test_folder="Society,UK",
+        ),
+        GuardianAuthorshipConfig(
+            name="cross_topic_{}".format(4),
+            version=datasets.Version(
+                "{}.0.0".format(4), description="The Original DS with the cross-topic scenario no.{}".format(4)
+            ),
+            train_folder="Society",
+            valid_folder="Politics",
+            test_folder="UK,World",
+        ),
+        GuardianAuthorshipConfig(
+            name="cross_topic_{}".format(5),
+            version=datasets.Version(
+                "{}.0.0".format(5), description="The Original DS with the cross-topic scenario no.{}".format(5)
+            ),
+            train_folder="Society",
+            valid_folder="UK",
+            test_folder="Politics,World",
+        ),
+        GuardianAuthorshipConfig(
+            name="cross_topic_{}".format(6),
+            version=datasets.Version(
+                "{}.0.0".format(6), description="The Original DS with the cross-topic scenario no.{}".format(6)
+            ),
+            train_folder="Society",
+            valid_folder="World",
+            test_folder="Politics,UK",
+        ),
+        GuardianAuthorshipConfig(
+            name="cross_topic_{}".format(7),
+            version=datasets.Version(
+                "{}.0.0".format(7), description="The Original DS with the cross-topic scenario no.{}".format(7)
+            ),
+            train_folder="UK",
+            valid_folder="Politics",
+            test_folder="Society,World",
+        ),
+        GuardianAuthorshipConfig(
+            name="cross_topic_{}".format(8),
+            version=datasets.Version(
+                "{}.0.0".format(8), description="The Original DS with the cross-topic scenario no.{}".format(8)
+            ),
+            train_folder="UK",
+            valid_folder="Society",
+            test_folder="Politics,World",
+        ),
+        GuardianAuthorshipConfig(
+            name="cross_topic_{}".format(9),
+            version=datasets.Version(
+                "{}.0.0".format(9), description="The Original DS with the cross-topic scenario no.{}".format(9)
+            ),
+            train_folder="UK",
+            valid_folder="World",
+            test_folder="Politics,Society",
+        ),
+        GuardianAuthorshipConfig(
+            name="cross_topic_{}".format(10),
+            version=datasets.Version(
+                "{}.0.0".format(10), description="The Original DS with the cross-topic scenario no.{}".format(10)
+            ),
+            train_folder="World",
+            valid_folder="Politics",
+            test_folder="Society,UK",
+        ),
+        GuardianAuthorshipConfig(
+            name="cross_topic_{}".format(11),
+            version=datasets.Version(
+                "{}.0.0".format(11), description="The Original DS with the cross-topic scenario no.{}".format(11)
+            ),
+            train_folder="World",
+            valid_folder="Society",
+            test_folder="Politics,UK",
+        ),
+        GuardianAuthorshipConfig(
+            name="cross_topic_{}".format(12),
+            version=datasets.Version(
+                "{}.0.0".format(12), description="The Original DS with the cross-topic scenario no.{}".format(12)
+            ),
+            train_folder="World",
+            valid_folder="UK",
+            test_folder="Politics,Society",
+        ),
+        # # cross-genre
+        GuardianAuthorshipConfig(
+            name="cross_genre_{}".format(1),
+            version=datasets.Version(
+                "{}.0.0".format(13), description="The Original DS with the cross-genre scenario no.{}".format(1)
+            ),
+            train_folder="Books",
+            valid_folder="Politics",
+            test_folder="Society,UK,World",
+        ),
+        GuardianAuthorshipConfig(
+            name="cross_genre_{}".format(2),
+            version=datasets.Version(
+                "{}.0.0".format(14), description="The Original DS with the cross-genre scenario no.{}".format(2)
+            ),
+            train_folder="Books",
+            valid_folder="Society",
+            test_folder="Politics,UK,World",
+        ),
+        GuardianAuthorshipConfig(
+            name="cross_genre_{}".format(3),
+            version=datasets.Version(
+                "{}.0.0".format(15), description="The Original DS with the cross-genre scenario no.{}".format(3)
+            ),
+            train_folder="Books",
+            valid_folder="UK",
+            test_folder="Politics,Society,World",
+        ),
+        GuardianAuthorshipConfig(
+            name="cross_genre_{}".format(4),
+            version=datasets.Version(
+                "{}.0.0".format(16), description="The Original DS with the cross-genre scenario no.{}".format(4)
+            ),
+            train_folder="Books",
+            valid_folder="World",
+            test_folder="Politics,Society,UK",
+        ),
+    ]
+    def _info(self):
+        # Specifies the datasets.DatasetInfo object
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    # These are the features of your dataset like images, labels ...
+                    # There are 13 authors in this dataset
+                    "author": datasets.features.ClassLabel(
+                        names=[
+                            "catherinebennett",
+                            "georgemonbiot",
+                            "hugoyoung",
+                            "jonathanfreedland",
+                            "martinkettle",
+                            "maryriddell",
+                            "nickcohen",
+                            "peterpreston",
+                            "pollytoynbee",
+                            "royhattersley",
+                            "simonhoggart",
+                            "willhutton",
+                            "zoewilliams",
+                        ]
+                    ),
+                    # There are book reviews, and articles on the following four topics
+                    "topic": datasets.features.ClassLabel(names=["Politics", "Society", "UK", "World", "Books"]),
+                    "article": datasets.Value("string"),
+                }
+            ),
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=[("article", "author")],
+            # Homepage of the dataset for documentation
+            homepage="http://www.icsd.aegean.gr/lecturers/stamatatos/papers/JLP2013.pdf",
+            citation=_CITATION,
+        )
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+        # download and extract URLs
+        dl_dir = dl_manager.download_and_extract(_URL)
+        # This folder contains the orginal/2013 dataset
+        data_dir = os.path.join(dl_dir, "Guardian", "Guardian_original")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"data_dir": data_dir, "samples_folders": self.config.train_folder, "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"data_dir": data_dir, "samples_folders": self.config.test_folder, "split": "test"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"data_dir": data_dir, "samples_folders": self.config.valid_folder, "split": "valid"},
+            ),
+        ]
+    def _generate_examples(self, data_dir, samples_folders, split):
+        """ Yields examples. """
+        # Yields (key, example) tuples from the dataset
+        # Training and validation are on 1 topic/genre, while testing is on multiple topics
+        # We convert the sample folders into list (from string)
+        if samples_folders.count(",") == 0:
+            samples_folders = [samples_folders]
+        else:
+            samples_folders = samples_folders.split(",")
+        # the dataset is structured as:
+        # |-Topic1
+        # |---author 1
+        # |------- article-1
+        # |------- article-2
+        # |---author 2
+        # |------- article-1
+        # |------- article-2
+        # |-Topic2
+        # ...
+        for topic in samples_folders:
+            full_path = os.path.join(data_dir, topic)
+            for author in os.listdir(full_path):
+                list_articles = os.listdir(os.path.join(full_path, author))
+                if len(list_articles) == 0:
+                    # Some authors have no articles on certain topics
+                    continue
+                for id_, article in enumerate(list_articles):
+                    path_2_author = os.path.join(full_path, author)
+                    path_2_article = os.path.join(path_2_author, article)
+                    with open(path_2_article, "r", encoding="utf8", errors="ignore") as f:
+                        art = f.readlines()
+                    # The whole article is stored as one line. We access the 1st element of the list
+                    # to store it as string, not as a list
+                    yield id_, {
+                        "article": art[0],
+                        "author": author,
+                        "topic": topic,
+                    }