Dataset infos in yaml (#4926)

* wip

* fix Features yaml

* splits to yaml

* add _to_yaml_list

* style

* example: conll2000

* example: crime_and_punish

* add pyyaml dependency

* remove unused imports

* remove validation tests

* style

* allow dataset_infos to be struct or list in YAML

* fix test

* style

* update "datasets-cli test" + remove "version"

* remove config definitions in conll2000 and crime_and_punish

* remove versions for conll2000 and crime_and_punish

* move conll2000 and cap dummy data

* fix test

* add tests

* comments and tests

* more test

* don't mention the dataset_infos.json file in docs

* nit in docs

* docs

* dataset_infos -> dataset_info

* again

* use id2label in class_label

* update conll2000

* fix utf-8 yaml dump

* --save_infos -> --save_info

* Apply suggestions from code review

Co-authored-by: Polina Kazakova <[email protected]>

* style

* fix reloading a single dataset_info

* push info to README.md in push_to_hub

* update test

Co-authored-by: Polina Kazakova <[email protected]>

Commit from https://github.com/huggingface/datasets/commit/67e65c90e9490810b89ee140da11fdd13c356c9c

README.md

@@ -3,6 +3,96 @@ language:
 - en
 paperswithcode_id: conll-2000-1
 pretty_name: CoNLL-2000
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: tokens
+    sequence: string
+  - name: pos_tags
+    sequence:
+      class_label:
+        names:
+          0: ''''''
+          1: '#'
+          2: $
+          3: (
+          4: )
+          5: ','
+          6: .
+          7: ':'
+          8: '``'
+          9: CC
+          10: CD
+          11: DT
+          12: EX
+          13: FW
+          14: IN
+          15: JJ
+          16: JJR
+          17: JJS
+          18: MD
+          19: NN
+          20: NNP
+          21: NNPS
+          22: NNS
+          23: PDT
+          24: POS
+          25: PRP
+          26: PRP$
+          27: RB
+          28: RBR
+          29: RBS
+          30: RP
+          31: SYM
+          32: TO
+          33: UH
+          34: VB
+          35: VBD
+          36: VBG
+          37: VBN
+          38: VBP
+          39: VBZ
+          40: WDT
+          41: WP
+          42: WP$
+          43: WRB
+  - name: chunk_tags
+    sequence:
+      class_label:
+        names:
+          0: O
+          1: B-ADJP
+          2: I-ADJP
+          3: B-ADVP
+          4: I-ADVP
+          5: B-CONJP
+          6: I-CONJP
+          7: B-INTJ
+          8: I-INTJ
+          9: B-LST
+          10: I-LST
+          11: B-NP
+          12: I-NP
+          13: B-PP
+          14: I-PP
+          15: B-PRT
+          16: I-PRT
+          17: B-SBAR
+          18: I-SBAR
+          19: B-UCP
+          20: I-UCP
+          21: B-VP
+          22: I-VP
+  splits:
+  - name: test
+    num_bytes: 1201151
+    num_examples: 2013
+  - name: train
+    num_bytes: 5356965
+    num_examples: 8937
+  download_size: 3481560
+  dataset_size: 6558116
 ---
 
 # Dataset Card for "conll2000"
@@ -173,4 +263,4 @@ The data fields are the same among all splits.
 
 ### Contributions
 
-Thanks to [@vblagoje](https://github.com/vblagoje), [@jplu](https://github.com/jplu) for adding this dataset.
+Thanks to [@vblagoje](https://github.com/vblagoje), [@jplu](https://github.com/jplu) for adding this dataset.

conll2000.py

@@ -53,25 +53,9 @@ _TRAINING_FILE = "train.txt"
 _TEST_FILE = "test.txt"
 
 
-class Conll2000Config(datasets.BuilderConfig):
-    """BuilderConfig for Conll2000"""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig forConll2000.
-
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(Conll2000Config, self).__init__(**kwargs)
-
-
 class Conll2000(datasets.GeneratorBasedBuilder):
     """Conll2000 dataset."""
 
-    BUILDER_CONFIGS = [
-        Conll2000Config(name="conll2000", version=datasets.Version("1.0.0"), description="Conll2000 dataset"),
-    ]
-
     def _info(self):
         return datasets.DatasetInfo(
             description=_DESCRIPTION,

dataset_infos.json

@@ -1 +0,0 @@
-{"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}}