Change uint32s to int32s

dataset_infos.json

@@ -1 +1 @@
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The morphosyntactic tags and syntactic dependencies are included both in the \nJOS/MULTEXT-East framework, as well as in the framework of Universal Dependencies.\n", "citation": "@InProceedings{krek2020ssj500k,\ntitle = {The ssj500k Training Corpus for Slovene Language Processing},\nauthor={Krek, Simon and Erjavec, Toma\u017e and Dobrovoljc, Kaja and Gantar, Polona and Arhar Holdt, Spela and \u010cibej, Jaka and Brank, Janez},\nbooktitle={Proceedings of the Conference on Language Technologies and Digital Humanities},\nyear={2020},\npages={24-33}\n}\n", "homepage": "http://hdl.handle.net/11356/1434", "license": "Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)", "features": {"id_doc": {"dtype": "string", "id": null, "_type": "Value"}, "idx_par": {"dtype": "int32", "id": null, "_type": "Value"}, "idx_sent": {"dtype": "int32", "id": null, "_type": "Value"}, "id_words": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "words": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "lemmas": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "msds": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "ud_dep_head": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "ud_dep_rel": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "ssj500k", "config_name": "dependency_parsing_ud", "version": {"version_str": "2.3.0", "description": null, "major": 2, "minor": 3, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 14048597, "num_examples": 8000, "dataset_name": "ssj500k"}}, "download_checksums": {"https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1434/ssj500k-en.TEI.zip": {"num_bytes": 13021836, "checksum": "08ac4d6cf74a45bc81f6e9ca53e7406c96c906c218cbb8ff2f7365e96655c460"}}, "download_size": 13021836, "post_processing_size": null, "dataset_size": 14048597, "size_in_bytes": 27070433}, "dependency_parsing_jos": {"description": "The ssj500k training corpus contains about 500 000 tokens manually annotated on the levels of tokenisation,\nsentence segmentation, morphosyntactic tagging, and lemmatisation. 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The morphosyntactic tags and syntactic dependencies are included both in the \nJOS/MULTEXT-East framework, as well as in the framework of Universal Dependencies.\n", "citation": "@InProceedings{krek2020ssj500k,\ntitle = {The ssj500k Training Corpus for Slovene Language Processing},\nauthor={Krek, Simon and Erjavec, Toma\u017e and Dobrovoljc, Kaja and Gantar, Polona and Arhar Holdt, Spela and \u010cibej, Jaka and Brank, Janez},\nbooktitle={Proceedings of the Conference on Language Technologies and Digital Humanities},\nyear={2020},\npages={24-33}\n}\n", "homepage": "http://hdl.handle.net/11356/1434", "license": "Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)", "features": {"id_doc": {"dtype": "string", "id": null, "_type": "Value"}, "idx_par": {"dtype": "int32", "id": null, "_type": "Value"}, "idx_sent": {"dtype": "int32", "id": null, "_type": "Value"}, "id_words": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "words": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "lemmas": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "msds": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "jos_dep_head": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "jos_dep_rel": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "ssj500k", "config_name": "dependency_parsing_jos", "version": {"version_str": "2.3.0", "description": null, "major": 2, "minor": 3, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 23027788, "num_examples": 11411, "dataset_name": "ssj500k"}}, "download_checksums": {"https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1434/ssj500k-en.TEI.zip": {"num_bytes": 13021836, "checksum": "08ac4d6cf74a45bc81f6e9ca53e7406c96c906c218cbb8ff2f7365e96655c460"}}, "download_size": 13021836, "post_processing_size": null, "dataset_size": 23027788, "size_in_bytes": 36049624}, "semantic_role_labeling": {"description": "The ssj500k training corpus contains about 500 000 tokens manually annotated on the levels of tokenisation,\nsentence segmentation, morphosyntactic tagging, and lemmatisation. 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The morphosyntactic tags and syntactic dependencies are included both in the \nJOS/MULTEXT-East framework, as well as in the framework of Universal Dependencies.\n", "citation": "@InProceedings{krek2020ssj500k,\ntitle = {The ssj500k Training Corpus for Slovene Language Processing},\nauthor={Krek, Simon and Erjavec, Toma\u017e and Dobrovoljc, Kaja and Gantar, Polona and Arhar Holdt, Spela and \u010cibej, Jaka and Brank, Janez},\nbooktitle={Proceedings of the Conference on Language Technologies and Digital Humanities},\nyear={2020},\npages={24-33}\n}\n", "homepage": "http://hdl.handle.net/11356/1434", "license": "Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)", "features": {"id_doc": {"dtype": "string", "id": null, "_type": "Value"}, "idx_par": {"dtype": "int32", "id": null, "_type": "Value"}, "idx_sent": {"dtype": "int32", "id": null, "_type": "Value"}, "id_words": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "words": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "lemmas": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "msds": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "mwe_info": [{"type": {"dtype": "string", "id": null, "_type": "Value"}, "word_indices": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}]}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "ssj500k", "config_name": "multiword_expressions", "version": {"version_str": "2.3.0", "description": null, "major": 2, "minor": 3, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 24215008, "num_examples": 13516, "dataset_name": "ssj500k"}}, "download_checksums": {"https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1434/ssj500k-en.TEI.zip": {"num_bytes": 13021836, "checksum": "08ac4d6cf74a45bc81f6e9ca53e7406c96c906c218cbb8ff2f7365e96655c460"}}, "download_size": 13021836, "post_processing_size": null, "dataset_size": 24215008, "size_in_bytes": 37236844}}

ssj500k.py

@@ -119,8 +119,8 @@ class Ssj500k(datasets.GeneratorBasedBuilder):
         if ret_all_data or self.config.name == "semantic_role_labeling":
             features_dict.update({
                 "srl_info": [{
-                    "idx_arg": datasets.Value("uint32"),
-                    "idx_head": datasets.Value("uint32"),
+                    "idx_arg": datasets.Value("int32"),
+                    "idx_head": datasets.Value("int32"),
                     "role": datasets.Value("string")
                 }]
             })
@@ -128,7 +128,7 @@ class Ssj500k(datasets.GeneratorBasedBuilder):
         if ret_all_data or self.config.name == "multiword_expressions":
             features_dict["mwe_info"] = [{
                 "type": datasets.Value("string"),
-                "word_indices": datasets.Sequence(datasets.Value("uint32"))
+                "word_indices": datasets.Sequence(datasets.Value("int32"))
             }]
 
         features = datasets.Features(features_dict)