Support streaming cfq dataset (#4579)

* Support streaming cfq dataset

* Fix style

* Fix remaining code

* Fix tags and documentation card

* Fix task tags

* Fix task tag

* Refactor parsing to reduce RAM usage

* Add license

* Update metadata JSON

* Update dummy data

* Use less RAM by loading only samples needed

* Yield immediately each sample or buffer it

* Update dummy data to have dataset.json as last archive member

* Rename license tag

Commit from https://github.com/huggingface/datasets/commit/de2f6ef2bc14022d0e9212f293b8e7b200aa7e75

README.md

@@ -1,8 +1,27 @@
 ---
+annotations_creators:
+- no-annotation
+language_creators:
+- expert-generated
 language:
 - en
-paperswithcode_id: cfq
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
 pretty_name: Compositional Freebase Questions
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- question-answering
+- other
+task_ids:
+- open-domain-qa
+- closed-domain-qa
+- other-compositionality
+paperswithcode_id: cfq
 ---
 
 # Dataset Card for "cfq"
@@ -35,7 +54,7 @@ pretty_name: Compositional Freebase Questions
 
 - **Homepage:** [https://github.com/google-research/google-research/tree/master/cfq](https://github.com/google-research/google-research/tree/master/cfq)
 - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Paper:** https://arxiv.org/abs/1912.09713
 - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 - **Size of downloaded dataset files:** 2041.62 MB
 - **Size of the generated dataset:** 345.30 MB
@@ -43,12 +62,10 @@ pretty_name: Compositional Freebase Questions
 
 ### Dataset Summary
 
-The CFQ dataset (and it's splits) for measuring compositional generalization.
-
-See https://arxiv.org/abs/1912.09713.pdf for background.
-
-Example usage:
-data = datasets.load_dataset('cfq/mcd1')
+The Compositional Freebase Questions (CFQ) is a dataset that is specifically designed to measure compositional
+generalization. CFQ is a simple yet realistic, large dataset of natural language questions and answers that also
+provides for each question a corresponding SPARQL query against the Freebase knowledge base. This means that CFQ can
+also be used for semantic parsing.
 
 ### Supported Tasks and Leaderboards
 
@@ -56,7 +73,7 @@ data = datasets.load_dataset('cfq/mcd1')
 
 ### Languages
 
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+English (`en`).
 
 ## Dataset Structure
 
@@ -71,8 +88,8 @@ data = datasets.load_dataset('cfq/mcd1')
 An example of 'train' looks as follows.
 ```
 {
-    "query": "SELECT /producer M0 . /director M0 . ",
-    "question": "Who produced and directed M0?"
+  'query': 'SELECT count(*) WHERE {\n?x0 a ns:people.person .\n?x0 ns:influence.influence_node.influenced M1 .\n?x0 ns:influence.influence_node.influenced M2 .\n?x0 ns:people.person.spouse_s/ns:people.marriage.spouse|ns:fictional_universe.fictional_character.married_to/ns:fictional_universe.marriage_of_fictional_characters.spouses ?x1 .\n?x1 a ns:film.cinematographer .\nFILTER ( ?x0 != ?x1 )\n}',
+  'question': 'Did a person marry a cinematographer , influence M1 , and influence M2'
 }
 ```
 
@@ -85,8 +102,8 @@ An example of 'train' looks as follows.
 An example of 'train' looks as follows.
 ```
 {
-    "query": "SELECT /producer M0 . /director M0 . ",
-    "question": "Who produced and directed M0?"
+  'query': 'SELECT count(*) WHERE {\n?x0 ns:people.person.parents|ns:fictional_universe.fictional_character.parents|ns:organization.organization.parent/ns:organization.organization_relationship.parent ?x1 .\n?x1 a ns:people.person .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person ?x0 .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person M2 .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person M3 .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person M4 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person ?x0 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person M2 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person M3 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person M4\n}',
+  'question': "Did M1 and M5 employ M2 , M3 , and M4 and employ a person 's child"
 }
 ```
 
@@ -134,37 +151,22 @@ An example of 'train' looks as follows.
 
 ### Data Fields
 
-The data fields are the same among all splits.
-
-#### mcd1
-- `question`: a `string` feature.
-- `query`: a `string` feature.
-
-#### mcd2
-- `question`: a `string` feature.
-- `query`: a `string` feature.
-
-#### mcd3
-- `question`: a `string` feature.
-- `query`: a `string` feature.
-
-#### query_complexity_split
-- `question`: a `string` feature.
-- `query`: a `string` feature.
-
-#### query_pattern_split
+The data fields are the same among all splits and configurations:
 - `question`: a `string` feature.
 - `query`: a `string` feature.
 
 ### Data Splits
 
-|         name         |train |test |
-|----------------------|-----:|----:|
-|mcd1                  | 95743|11968|
-|mcd2                  | 95743|11968|
-|mcd3                  | 95743|11968|
-|query_complexity_split|100654| 9512|
-|query_pattern_split   | 94600|12589|
+| name                      |  train |  test |
+|---------------------------|-------:|------:|
+| mcd1                      |  95743 | 11968 |
+| mcd2                      |  95743 | 11968 |
+| mcd3                      |  95743 | 11968 |
+| query_complexity_split    | 100654 |  9512 |
+| query_pattern_split       |  94600 | 12589 |
+| question_complexity_split |  98999 | 10340 |
+| question_pattern_split    |  95654 | 11909 |
+| random_split              |  95744 | 11967 |
 
 ## Dataset Creation
 

cfq.py

@@ -18,7 +18,6 @@
 
 
 import json
-import os
 import re
 
 import datasets
@@ -27,6 +26,10 @@ import datasets
 logger = datasets.logging.get_logger(__name__)
 
 
+_HOMEPAGE = "https://github.com/google-research/google-research/tree/master/cfq"
+
+_LICENSE = "CC BY 4.0"
+
 _CITATION = """
 @inproceedings{Keysers2020,
   title={Measuring Compositional Generalization: A Comprehensive Method on
@@ -69,7 +72,7 @@ class CfqConfig(datasets.BuilderConfig):
         super(CfqConfig, self).__init__(
             name=name, version=datasets.Version("1.0.1"), description=_DESCRIPTION, **kwargs
         )
-        self.split_file = os.path.join(directory, name + ".json")
+        self.splits_path = f"cfq/{directory}/{name}.json"
 
 
 _QUESTION = "question"
@@ -102,27 +105,23 @@ class Cfq(datasets.GeneratorBasedBuilder):
                 }
             ),
             supervised_keys=(_QUESTION, _QUERY),
-            homepage="https://github.com/google-research/google-research/tree/master/cfq",
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
             citation=_CITATION,
         )
 
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
-        data_dir = dl_manager.download_and_extract(_DATA_URL)
-        data_dir = os.path.join(data_dir, "cfq")
+        archive_path = dl_manager.download(_DATA_URL)
         return [
             datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
+                name=split,
                 gen_kwargs={
-                    "base_directory": data_dir,
-                    "splits_file": self.config.split_file,
-                    "split_id": "trainIdxs",
+                    "data_files": dl_manager.iter_archive(archive_path),
+                    "split_id": f"{split}Idxs",
                 },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"base_directory": data_dir, "splits_file": self.config.split_file, "split_id": "testIdxs"},
-            ),
+            )
+            for split in [datasets.Split.TRAIN, datasets.Split.TEST]
         ]
 
     def _scrub_json(self, content):
@@ -131,22 +130,38 @@ class Cfq(datasets.GeneratorBasedBuilder):
         # For the 4GB dataset file it requires more than 40GB of RAM and takes 3min.
         # There are more efficient libraries but in order to avoid additional
         # dependencies we use a simple (perhaps somewhat brittle) regexp to reduce
-        # the content to only what is needed. This takes 1min to execute but
-        # afterwards loading requires only 500MB or RAM and is done in 2s.
-        regex = re.compile(r'("%s":\s*"[^"]*").*?("%s":\s*"[^"]*")' % (_QUESTION_FIELD, _QUERY_FIELD), re.DOTALL)
-        return "[" + ",".join(["{" + m.group(1) + "," + m.group(2) + "}" for m in regex.finditer(content)]) + "]"
-
-    def _generate_examples(self, base_directory, splits_file, split_id):
+        # the content to only what is needed.
+        question_regex = re.compile(r'("%s":\s*"[^"]*")' % _QUESTION_FIELD)
+        query_regex = re.compile(r'("%s":\s*"[^"]*")' % _QUERY_FIELD)
+        question_match = None
+        for line in content:
+            line = line.decode("utf-8")
+            if not question_match:
+                question_match = question_regex.match(line)
+            else:
+                query_match = query_regex.match(line)
+                if query_match:
+                    yield json.loads("{" + question_match.group(1) + "," + query_match.group(1) + "}")
+                    question_match = None
+
+    def _generate_examples(self, data_files, split_id):
         """Yields examples."""
-        samples_path = os.path.join(base_directory, "dataset.json")
-        splits_path = os.path.join(base_directory, splits_file)
-        with open(samples_path, encoding="utf-8") as samples_file:
-            with open(splits_path, encoding="utf-8") as splits_file:
-                logger.info("Reading json from %s into memory...", samples_path)
-                samples = json.loads(self._scrub_json(samples_file.read()))
-                logger.info("%d samples loaded", len(samples))
-                logger.info("Loaded json data from %s.", samples_path)
-                splits = json.load(splits_file)
-                for idx in splits[split_id]:
-                    sample = samples[idx]
-                    yield idx, {_QUESTION: sample[_QUESTION_FIELD], _QUERY: sample[_QUERY_FIELD]}
+        samples_path = "cfq/dataset.json"
+        for path, file in data_files:
+            if path == self.config.splits_path:
+                splits = json.load(file)[split_id]
+            elif path == samples_path:
+                # The samples_path is the last path inside the archive
+                generator = enumerate(self._scrub_json(file))
+                samples = {}
+                splits_set = set(splits)
+                for split_idx in splits:
+                    if split_idx in samples:
+                        sample = samples.pop(split_idx)
+                    else:
+                        for sample_idx, sample in generator:
+                            if sample_idx == split_idx:
+                                break
+                            elif sample_idx in splits_set:
+                                samples[sample_idx] = sample
+                    yield split_idx, {_QUESTION: sample[_QUESTION_FIELD], _QUERY: sample[_QUERY_FIELD]}

dataset_infos.json

@@ -1 +1 @@
-{"mcd1": {"description": "\nThe CFQ dataset (and it's splits) for measuring compositional generalization.\n\nSee https://arxiv.org/abs/1912.09713.pdf for background.\n\nExample usage:\ndata = datasets.load_dataset('cfq/mcd1')\n", "citation": "\n@inproceedings{Keysers2020,\n  title={Measuring Compositional Generalization: A Comprehensive Method on\n         Realistic Data},\n  author={Daniel Keysers and Nathanael Sch\"{a}rli and Nathan Scales and\n          Hylke Buisman and Daniel Furrer and Sergii Kashubin and\n          Nikola Momchev and Danila Sinopalnikov and Lukasz Stafiniak and\n          Tibor Tihon and Dmitry Tsarkov and Xiao Wang and Marc van Zee and\n          Olivier Bousquet},\n  booktitle={ICLR},\n  year={2020},\n  url={https://arxiv.org/abs/1912.09713.pdf},\n}\n", "homepage": "https://github.com/google-research/google-research/tree/master/cfq", "license": "", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "query": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": {"input": "question", "output": "query"}, "builder_name": "cfq", "config_name": "mcd1", "version": {"version_str": "1.0.1", "description": null, "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 1}, "splits": {"test": {"name": "test", "num_bytes": 5450999, "num_examples": 11968, "dataset_name": "cfq"}, "train": {"name": "train", "num_bytes": 37444718, "num_examples": 95743, "dataset_name": "cfq"}}, "download_checksums": {"https://storage.googleapis.com/cfq_dataset/cfq.tar.gz": {"num_bytes": 267599061, "checksum": "979d719271eae12611643b89151f639d94092800e7e71f2d23a754c43f3eb1ba"}}, "download_size": 267599061, "dataset_size": 42895717, "size_in_bytes": 310494778}, "mcd2": {"description": "\nThe CFQ dataset (and it's splits) for measuring compositional generalization.\n\nSee https://arxiv.org/abs/1912.09713.pdf for background.\n\nExample usage:\ndata = datasets.load_dataset('cfq/mcd1')\n", "citation": "\n@inproceedings{Keysers2020,\n  title={Measuring Compositional Generalization: A Comprehensive Method on\n         Realistic Data},\n  author={Daniel Keysers and Nathanael Sch\"{a}rli and Nathan Scales and\n          Hylke Buisman and Daniel Furrer and Sergii Kashubin and\n          Nikola Momchev and Danila Sinopalnikov and Lukasz Stafiniak and\n          Tibor Tihon and Dmitry Tsarkov and Xiao Wang and Marc van Zee and\n          Olivier Bousquet},\n  booktitle={ICLR},\n  year={2020},\n  url={https://arxiv.org/abs/1912.09713.pdf},\n}\n", "homepage": "https://github.com/google-research/google-research/tree/master/cfq", "license": "", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "query": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": {"input": "question", "output": "query"}, "builder_name": "cfq", "config_name": "mcd2", "version": {"version_str": "1.0.1", "description": null, "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 1}, "splits": {"test": {"name": "test", "num_bytes": 5318515, "num_examples": 11968, "dataset_name": "cfq"}, "train": {"name": "train", "num_bytes": 39460569, "num_examples": 95743, "dataset_name": "cfq"}}, "download_checksums": {"https://storage.googleapis.com/cfq_dataset/cfq.tar.gz": {"num_bytes": 267599061, "checksum": "979d719271eae12611643b89151f639d94092800e7e71f2d23a754c43f3eb1ba"}}, "download_size": 267599061, "dataset_size": 44779084, "size_in_bytes": 312378145}, "mcd3": {"description": "\nThe CFQ dataset (and it's splits) for measuring compositional generalization.\n\nSee https://arxiv.org/abs/1912.09713.pdf for background.\n\nExample usage:\ndata = datasets.load_dataset('cfq/mcd1')\n", "citation": "\n@inproceedings{Keysers2020,\n  title={Measuring Compositional Generalization: A Comprehensive Method on\n         Realistic Data},\n  author={Daniel Keysers and Nathanael Sch\"{a}rli and Nathan Scales and\n          Hylke Buisman and Daniel Furrer and Sergii Kashubin and\n          Nikola Momchev and Danila Sinopalnikov and Lukasz Stafiniak and\n          Tibor Tihon and Dmitry Tsarkov and Xiao Wang and Marc van Zee and\n          Olivier Bousquet},\n  booktitle={ICLR},\n  year={2020},\n  url={https://arxiv.org/abs/1912.09713.pdf},\n}\n", "homepage": "https://github.com/google-research/google-research/tree/master/cfq", "license": "", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "query": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": {"input": "question", "output": "query"}, "builder_name": "cfq", "config_name": "mcd3", "version": {"version_str": "1.0.1", "description": null, "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 1}, "splits": {"test": {"name": "test", "num_bytes": 5248999, "num_examples": 11968, "dataset_name": "cfq"}, "train": {"name": "train", "num_bytes": 38352257, "num_examples": 95743, "dataset_name": "cfq"}}, "download_checksums": {"https://storage.googleapis.com/cfq_dataset/cfq.tar.gz": {"num_bytes": 267599061, "checksum": "979d719271eae12611643b89151f639d94092800e7e71f2d23a754c43f3eb1ba"}}, "download_size": 267599061, "dataset_size": 43601256, "size_in_bytes": 311200317}, "question_complexity_split": {"description": "\nThe CFQ dataset (and it's splits) for measuring compositional generalization.\n\nSee https://arxiv.org/abs/1912.09713.pdf for background.\n\nExample usage:\ndata = datasets.load_dataset('cfq/mcd1')\n", "citation": "\n@inproceedings{Keysers2020,\n  title={Measuring Compositional Generalization: A Comprehensive Method on\n         Realistic Data},\n  author={Daniel Keysers and Nathanael Sch\"{a}rli and Nathan Scales and\n          Hylke Buisman and Daniel Furrer and Sergii Kashubin and\n          Nikola Momchev and Danila Sinopalnikov and Lukasz Stafiniak and\n          Tibor Tihon and Dmitry Tsarkov and Xiao Wang and Marc van Zee and\n          Olivier Bousquet},\n  booktitle={ICLR},\n  year={2020},\n  url={https://arxiv.org/abs/1912.09713.pdf},\n}\n", "homepage": "https://github.com/google-research/google-research/tree/master/cfq", "license": "", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "query": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": {"input": "question", "output": "query"}, "builder_name": "cfq", "config_name": "question_complexity_split", "version": {"version_str": "1.0.1", "description": null, "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 1}, "splits": {"test": {"name": "test", "num_bytes": 5785448, "num_examples": 10340, "dataset_name": "cfq"}, "train": {"name": "train", "num_bytes": 40026566, "num_examples": 98999, "dataset_name": "cfq"}}, "download_checksums": {"https://storage.googleapis.com/cfq_dataset/cfq.tar.gz": {"num_bytes": 267599061, "checksum": "979d719271eae12611643b89151f639d94092800e7e71f2d23a754c43f3eb1ba"}}, "download_size": 267599061, "dataset_size": 45812014, "size_in_bytes": 313411075}, "question_pattern_split": {"description": "\nThe CFQ dataset (and it's splits) for measuring compositional generalization.\n\nSee https://arxiv.org/abs/1912.09713.pdf for background.\n\nExample usage:\ndata = datasets.load_dataset('cfq/mcd1')\n", "citation": "\n@inproceedings{Keysers2020,\n  title={Measuring Compositional Generalization: A Comprehensive Method on\n         Realistic Data},\n  author={Daniel Keysers and Nathanael Sch\"{a}rli and Nathan Scales and\n          Hylke Buisman and Daniel Furrer and Sergii Kashubin and\n          Nikola Momchev and Danila Sinopalnikov and Lukasz Stafiniak and\n          Tibor Tihon and Dmitry Tsarkov and Xiao Wang and Marc van Zee and\n          Olivier Bousquet},\n  booktitle={ICLR},\n  year={2020},\n  url={https://arxiv.org/abs/1912.09713.pdf},\n}\n", "homepage": "https://github.com/google-research/google-research/tree/master/cfq", "license": "", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "query": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": {"input": "question", "output": "query"}, "builder_name": "cfq", "config_name": "question_pattern_split", "version": {"version_str": "1.0.1", "description": null, "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 1}, "splits": {"test": {"name": "test", "num_bytes": 5184411, "num_examples": 11909, "dataset_name": "cfq"}, "train": {"name": "train", "num_bytes": 41253229, "num_examples": 95654, "dataset_name": "cfq"}}, "download_checksums": {"https://storage.googleapis.com/cfq_dataset/cfq.tar.gz": {"num_bytes": 267599061, "checksum": "979d719271eae12611643b89151f639d94092800e7e71f2d23a754c43f3eb1ba"}}, "download_size": 267599061, "dataset_size": 46437640, "size_in_bytes": 314036701}, "query_complexity_split": {"description": "\nThe CFQ dataset (and it's splits) for measuring compositional generalization.\n\nSee https://arxiv.org/abs/1912.09713.pdf for background.\n\nExample usage:\ndata = datasets.load_dataset('cfq/mcd1')\n", "citation": "\n@inproceedings{Keysers2020,\n  title={Measuring Compositional Generalization: A Comprehensive Method on\n         Realistic Data},\n  author={Daniel Keysers and Nathanael Sch\"{a}rli and Nathan Scales and\n          Hylke Buisman and Daniel Furrer and Sergii Kashubin and\n          Nikola Momchev and Danila Sinopalnikov and Lukasz Stafiniak and\n          Tibor Tihon and Dmitry Tsarkov and Xiao Wang and Marc van Zee and\n          Olivier Bousquet},\n  booktitle={ICLR},\n  year={2020},\n  url={https://arxiv.org/abs/1912.09713.pdf},\n}\n", "homepage": "https://github.com/google-research/google-research/tree/master/cfq", "license": "", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "query": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": {"input": "question", "output": "query"}, "builder_name": "cfq", "config_name": "query_complexity_split", 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author={Daniel Keysers and Nathanael Sch\"{a}rli and Nathan Scales and\n          Hylke Buisman and Daniel Furrer and Sergii Kashubin and\n          Nikola Momchev and Danila Sinopalnikov and Lukasz Stafiniak and\n          Tibor Tihon and Dmitry Tsarkov and Xiao Wang and Marc van Zee and\n          Olivier Bousquet},\n  booktitle={ICLR},\n  year={2020},\n  url={https://arxiv.org/abs/1912.09713.pdf},\n}\n", "homepage": "https://github.com/google-research/google-research/tree/master/cfq", "license": "", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "query": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": {"input": "question", "output": "query"}, "builder_name": "cfq", "config_name": "query_pattern_split", "version": {"version_str": "1.0.1", "description": null, "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 1}, "splits": {"test": {"name": "test", "num_bytes": 5273088, "num_examples": 12589, "dataset_name": 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