Datasets:

Efstathios
/

guardian_authorship

- {"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 because 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 because 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 because 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 because 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 because 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 because 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 because 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 because 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 because 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 because 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 because 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 because 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 because 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 because 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 because 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 because 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}}