Update files from the datasets library (from 1.1.3)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.1.3

conll2000.py

@@ -77,9 +77,86 @@ class Conll2000(datasets.GeneratorBasedBuilder):
             features=datasets.Features(
                 {
                     "id": datasets.Value("string"),
-                    "words": datasets.Sequence(datasets.Value("string")),
-                    "pos": datasets.Sequence(datasets.Value("string")),
-                    "chunk": datasets.Sequence(datasets.Value("string")),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "pos_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "''",
+                                "#",
+                                "$",
+                                "(",
+                                ")",
+                                ",",
+                                ".",
+                                ":",
+                                "``",
+                                "CC",
+                                "CD",
+                                "DT",
+                                "EX",
+                                "FW",
+                                "IN",
+                                "JJ",
+                                "JJR",
+                                "JJS",
+                                "MD",
+                                "NN",
+                                "NNP",
+                                "NNPS",
+                                "NNS",
+                                "PDT",
+                                "POS",
+                                "PRP",
+                                "PRP$",
+                                "RB",
+                                "RBR",
+                                "RBS",
+                                "RP",
+                                "SYM",
+                                "TO",
+                                "UH",
+                                "VB",
+                                "VBD",
+                                "VBG",
+                                "VBN",
+                                "VBP",
+                                "VBZ",
+                                "WDT",
+                                "WP",
+                                "WP$",
+                                "WRB",
+                            ]
+                        )
+                    ),
+                    "chunk_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "O",
+                                "B-ADJP",
+                                "I-ADJP",
+                                "B-ADVP",
+                                "I-ADVP",
+                                "B-CONJP",
+                                "I-CONJP",
+                                "B-INTJ",
+                                "I-INTJ",
+                                "B-LST",
+                                "I-LST",
+                                "B-NP",
+                                "I-NP",
+                                "B-PP",
+                                "I-PP",
+                                "B-PRT",
+                                "I-PRT",
+                                "B-SBAR",
+                                "I-SBAR",
+                                "B-UCP",
+                                "I-UCP",
+                                "B-VP",
+                                "I-VP",
+                            ]
+                        )
+                    ),
                 }
             ),
             supervised_keys=None,
@@ -104,22 +181,22 @@ class Conll2000(datasets.GeneratorBasedBuilder):
         logging.info("⏳ Generating examples from = %s", filepath)
         with open(filepath, encoding="utf-8") as f:
             guid = 0
-            words = []
-            pos = []
-            chunk = []
+            tokens = []
+            pos_tags = []
+            chunk_tags = []
             for line in f:
                 if line == "" or line == "\n":
-                    if words:
-                        yield guid, {"id": str(guid), "words": words, "pos": pos, "chunk": chunk}
+                    if tokens:
+                        yield guid, {"id": str(guid), "tokens": tokens, "pos_tags": pos_tags, "chunk_tags": chunk_tags}
                         guid += 1
-                        words = []
-                        pos = []
-                        chunk = []
+                        tokens = []
+                        pos_tags = []
+                        chunk_tags = []
                 else:
                     # conll2000 tokens are space separated
                     splits = line.split(" ")
-                    words.append(splits[0])
-                    pos.append(splits[1])
-                    chunk.append(splits[2].rstrip())
+                    tokens.append(splits[0])
+                    pos_tags.append(splits[1])
+                    chunk_tags.append(splits[2].rstrip())
             # last example
-            yield guid, {"id": str(guid), "words": words, "pos": pos, "chunk": chunk}
+            yield guid, {"id": str(guid), "tokens": tokens, "pos_tags": pos_tags, "chunk_tags": chunk_tags}

dataset_infos.json

@@ -1 +1 @@
-{"conll2000": {"description": " Text chunking consists of dividing a text in syntactically correlated parts of words. For example, the sentence\n He reckons the current account deficit will narrow to only # 1.8 billion in September . can be divided as follows:\n[NP He ] [VP reckons ] [NP the current account deficit ] [VP will narrow ] [PP to ] [NP only # 1.8 billion ]\n[PP in ] [NP September ] .\n\nText chunking is an intermediate step towards full parsing. It was the shared task for CoNLL-2000. Training and test\ndata for this task is available. This data consists of the same partitions of the Wall Street Journal corpus (WSJ)\nas the widely used data for noun phrase chunking: sections 15-18 as training data (211727 tokens) and section 20 as\ntest data (47377 tokens). The annotation of the data has been derived from the WSJ corpus by a program written by\nSabine Buchholz from Tilburg University, The Netherlands.\n", "citation": "@inproceedings{tksbuchholz2000conll,\n   author     = \"Tjong Kim Sang, Erik F. and Sabine Buchholz\",\n   title      = \"Introduction to the CoNLL-2000 Shared Task: Chunking\",\n   editor     = \"Claire Cardie and Walter Daelemans and Claire\n                 Nedellec and Tjong Kim Sang, Erik\",\n   booktitle  = \"Proceedings of CoNLL-2000 and LLL-2000\",\n   publisher  = \"Lisbon, Portugal\",\n   pages      = \"127--132\",\n   year       = \"2000\"\n}\n", "homepage": "https://www.clips.uantwerpen.be/conll2000/chunking/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "words": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "pos": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "chunk": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "conll2000", "config_name": "conll2000", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 4916429, "num_examples": 8937, "dataset_name": "conll2000"}, "test": {"name": "test", "num_bytes": 1102955, "num_examples": 2013, "dataset_name": "conll2000"}}, "download_checksums": {"https://github.com/teropa/nlp/raw/master/resources/corpora/conll2000/train.txt": {"num_bytes": 2842164, "checksum": "82033cd7a72b209923a98007793e8f9de3abc1c8b79d646c50648eb949b87cea"}, "https://github.com/teropa/nlp/raw/master/resources/corpora/conll2000/test.txt": {"num_bytes": 639396, "checksum": "73b7b1e565fa75a1e22fe52ecdf41b6624d6f59dacb591d44252bf4d692b1628"}}, "download_size": 3481560, "post_processing_size": null, "dataset_size": 6019384, "size_in_bytes": 9500944}}
+{"conll2000": {"description": " Text chunking consists of dividing a text in syntactically correlated parts of words. For example, the sentence\n He reckons the current account deficit will narrow to only # 1.8 billion in September . can be divided as follows:\n[NP He ] [VP reckons ] [NP the current account deficit ] [VP will narrow ] [PP to ] [NP only # 1.8 billion ]\n[PP in ] [NP September ] .\n\nText chunking is an intermediate step towards full parsing. It was the shared task for CoNLL-2000. Training and test\ndata for this task is available. This data consists of the same partitions of the Wall Street Journal corpus (WSJ)\nas the widely used data for noun phrase chunking: sections 15-18 as training data (211727 tokens) and section 20 as\ntest data (47377 tokens). The annotation of the data has been derived from the WSJ corpus by a program written by\nSabine Buchholz from Tilburg University, The Netherlands.\n", "citation": "@inproceedings{tksbuchholz2000conll,\n   author     = \"Tjong Kim Sang, Erik F. and Sabine Buchholz\",\n   title      = \"Introduction to the CoNLL-2000 Shared Task: Chunking\",\n   editor     = \"Claire Cardie and Walter Daelemans and Claire\n                 Nedellec and Tjong Kim Sang, Erik\",\n   booktitle  = \"Proceedings of CoNLL-2000 and LLL-2000\",\n   publisher  = \"Lisbon, Portugal\",\n   pages      = \"127--132\",\n   year       = \"2000\"\n}\n", "homepage": "https://www.clips.uantwerpen.be/conll2000/chunking/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "pos_tags": {"feature": {"num_classes": 44, "names": ["''", "#", "$", "(", ")", ",", ".", ":", "``", "CC", "CD", "DT", "EX", "FW", "IN", "JJ", "JJR", "JJS", "MD", "NN", "NNP", "NNPS", "NNS", "PDT", "POS", "PRP", "PRP$", "RB", "RBR", "RBS", "RP", "SYM", "TO", "UH", "VB", "VBD", "VBG", "VBN", "VBP", "VBZ", "WDT", "WP", "WP$", "WRB"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}, "chunk_tags": {"feature": {"num_classes": 23, "names": ["O", "B-ADJP", "I-ADJP", "B-ADVP", "I-ADVP", "B-CONJP", "I-CONJP", "B-INTJ", "I-INTJ", "B-LST", "I-LST", "B-NP", "I-NP", "B-PP", "I-PP", "B-PRT", "I-PRT", "B-SBAR", "I-SBAR", "B-UCP", "I-UCP", "B-VP", "I-VP"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "conll2000", "config_name": "conll2000", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 5356965, "num_examples": 8937, "dataset_name": "conll2000"}, "test": {"name": "test", "num_bytes": 1201151, "num_examples": 2013, "dataset_name": "conll2000"}}, "download_checksums": {"https://github.com/teropa/nlp/raw/master/resources/corpora/conll2000/train.txt": {"num_bytes": 2842164, "checksum": "82033cd7a72b209923a98007793e8f9de3abc1c8b79d646c50648eb949b87cea"}, "https://github.com/teropa/nlp/raw/master/resources/corpora/conll2000/test.txt": {"num_bytes": 639396, "checksum": "73b7b1e565fa75a1e22fe52ecdf41b6624d6f59dacb591d44252bf4d692b1628"}}, "download_size": 3481560, "post_processing_size": null, "dataset_size": 6558116, "size_in_bytes": 10039676}}