Update files from the datasets library (from 1.17.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.17.0

README.md

@@ -128,7 +128,7 @@ An example from the HEAD-QA dataset looks as follows:
 		'atext': 'Presentan un periodo refractario.'
 	}],
 'ra': '3',
-'image': '',
+'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=675x538 at 0x1B42B6A1668>,
 'name': 'Cuaderno_2013_1_B',
 'year': '2013'
 }
@@ -143,7 +143,7 @@ An example from the HEAD-QA dataset looks as follows:
     - `aid`: answer identifier (int)
     - `atext`: answer text
 - `ra`: `aid` of the right answer (int)
-- `image`: optional, some of the questions refer to an image
+- `image`: (optional) a `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
 - `name`: name of the exam from which the question was extracted
 - `year`: year in which the exam took place
 

dataset_infos.json

@@ -1 +1 @@
-{"es": {"description": "HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to access a specialized position in the\nSpanish healthcare system, and are challenging even for highly specialized humans. They are designed by the Ministerio\nde Sanidad, Consumo y Bienestar Social.\n\nThe dataset contains questions about the following topics: medicine, nursing, psychology, chemistry, pharmacology and biology.\n", "citation": "@inproceedings{vilares-gomez-rodriguez-2019-head,\n    title = \"{HEAD}-{QA}: A Healthcare Dataset for Complex Reasoning\",\n    author = \"Vilares, David  and\n      G{'o}mez-Rodr{'i}guez, Carlos\",\n    booktitle = \"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics\",\n    month = jul,\n    year = \"2019\",\n    address = \"Florence, Italy\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/P19-1092\",\n    doi = \"10.18653/v1/P19-1092\",\n    pages = \"960--966\",\n    abstract = \"We present HEAD-QA, a multi-choice question answering testbed to encourage research on complex reasoning. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. We then consider monolingual (Spanish) and cross-lingual (to English) experiments with information retrieval and neural techniques. We show that: (i) HEAD-QA challenges current methods, and (ii) the results lag well behind human performance, demonstrating its usefulness as a benchmark for future work.\",\n}\n", "homepage": "https://aghie.github.io/head-qa/", "license": "MIT License", "features": {"name": {"dtype": "string", "id": null, "_type": "Value"}, "year": {"dtype": "string", "id": null, "_type": "Value"}, "category": {"dtype": "string", "id": null, "_type": "Value"}, "qid": {"dtype": "int32", "id": null, "_type": "Value"}, "qtext": {"dtype": "string", "id": null, "_type": "Value"}, "ra": {"dtype": "int32", "id": null, "_type": "Value"}, "image": {"dtype": "string", "id": null, "_type": "Value"}, "answers": [{"aid": {"dtype": "int32", "id": null, "_type": "Value"}, "atext": {"dtype": "string", "id": null, "_type": "Value"}}]}, "post_processed": null, "supervised_keys": null, "builder_name": "head_qa", "config_name": "es", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1207986, "num_examples": 2657, "dataset_name": "head_qa"}, "test": {"name": "test", "num_bytes": 1182358, "num_examples": 2742, "dataset_name": "head_qa"}, "validation": {"name": "validation", "num_bytes": 563002, "num_examples": 1366, "dataset_name": "head_qa"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1dUIqVwvoZAtbX_-z5axCoe97XNcFo1No": {"num_bytes": 1856679, "checksum": "6bb1cf3bbf8eccab2c5be33c2eb63896f1bd8c14c7a305a9449b254be3d0bfc5"}}, "download_size": 1856679, "post_processing_size": null, "dataset_size": 2953346, "size_in_bytes": 4810025}, "en": {"description": "HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to access a specialized position in the\nSpanish healthcare system, and are challenging even for highly specialized humans. They are designed by the Ministerio\nde Sanidad, Consumo y Bienestar Social.\n\nThe dataset contains questions about the following topics: medicine, nursing, psychology, chemistry, pharmacology and biology.\n", "citation": "@inproceedings{vilares-gomez-rodriguez-2019-head,\n    title = \"{HEAD}-{QA}: A Healthcare Dataset for Complex Reasoning\",\n    author = \"Vilares, David  and\n      G{'o}mez-Rodr{'i}guez, Carlos\",\n    booktitle = \"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics\",\n    month = jul,\n    year = \"2019\",\n    address = \"Florence, Italy\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/P19-1092\",\n    doi = \"10.18653/v1/P19-1092\",\n    pages = \"960--966\",\n    abstract = \"We present HEAD-QA, a multi-choice question answering testbed to encourage research on complex reasoning. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. We then consider monolingual (Spanish) and cross-lingual (to English) experiments with information retrieval and neural techniques. We show that: (i) HEAD-QA challenges current methods, and (ii) the results lag well behind human performance, demonstrating its usefulness as a benchmark for future work.\",\n}\n", "homepage": "https://aghie.github.io/head-qa/", "license": "MIT License", "features": {"name": {"dtype": "string", "id": null, "_type": "Value"}, "year": {"dtype": "string", "id": null, "_type": "Value"}, "category": {"dtype": "string", "id": null, "_type": "Value"}, "qid": {"dtype": "int32", "id": null, "_type": "Value"}, "qtext": {"dtype": "string", "id": null, "_type": "Value"}, "ra": {"dtype": "int32", "id": null, "_type": "Value"}, "image": {"dtype": "string", "id": null, "_type": "Value"}, "answers": [{"aid": {"dtype": "int32", "id": null, "_type": "Value"}, "atext": {"dtype": "string", "id": null, "_type": "Value"}}]}, "post_processed": null, "supervised_keys": null, "builder_name": "head_qa", "config_name": "en", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1135116, "num_examples": 2657, "dataset_name": "head_qa"}, "test": {"name": "test", "num_bytes": 1109888, "num_examples": 2742, "dataset_name": "head_qa"}, "validation": {"name": "validation", "num_bytes": 529540, "num_examples": 1366, "dataset_name": "head_qa"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1phryJg4FjCFkn0mSCqIOP2-FscAeKGV0": {"num_bytes": 1749836, "checksum": "21ca4e48930a3afe63d50d722b6b335ecb636e283489713ac3e8706db20ed92e"}}, "download_size": 1749836, "post_processing_size": null, "dataset_size": 2774544, "size_in_bytes": 4524380}}
+{"es": {"description": "HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to access a specialized position in the\nSpanish healthcare system, and are challenging even for highly specialized humans. They are designed by the Ministerio\nde Sanidad, Consumo y Bienestar Social.\n\nThe dataset contains questions about the following topics: medicine, nursing, psychology, chemistry, pharmacology and biology.\n", "citation": "@inproceedings{vilares-gomez-rodriguez-2019-head,\n    title = \"{HEAD}-{QA}: A Healthcare Dataset for Complex Reasoning\",\n    author = \"Vilares, David  and\n      G{'o}mez-Rodr{'i}guez, Carlos\",\n    booktitle = \"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics\",\n    month = jul,\n    year = \"2019\",\n    address = \"Florence, Italy\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/P19-1092\",\n    doi = \"10.18653/v1/P19-1092\",\n    pages = \"960--966\",\n    abstract = \"We present HEAD-QA, a multi-choice question answering testbed to encourage research on complex reasoning. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. We then consider monolingual (Spanish) and cross-lingual (to English) experiments with information retrieval and neural techniques. We show that: (i) HEAD-QA challenges current methods, and (ii) the results lag well behind human performance, demonstrating its usefulness as a benchmark for future work.\",\n}\n", "homepage": "https://aghie.github.io/head-qa/", "license": "MIT License", "features": {"name": {"dtype": "string", "id": null, "_type": "Value"}, "year": {"dtype": "string", "id": null, "_type": "Value"}, "category": {"dtype": "string", "id": null, "_type": "Value"}, "qid": {"dtype": "int32", "id": null, "_type": "Value"}, "qtext": {"dtype": "string", "id": null, "_type": "Value"}, "ra": {"dtype": "int32", "id": null, "_type": "Value"}, "image": {"id": null, "_type": "Image"}, "answers": [{"aid": {"dtype": "int32", "id": null, "_type": "Value"}, "atext": {"dtype": "string", "id": null, "_type": "Value"}}]}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "head_qa", "config_name": "es", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1227988, "num_examples": 2657, "dataset_name": "head_qa"}, "test": {"name": "test", "num_bytes": 1202592, "num_examples": 2742, "dataset_name": "head_qa"}, "validation": {"name": "validation", "num_bytes": 572652, "num_examples": 1366, "dataset_name": "head_qa"}}, "download_checksums": {"https://drive.google.com/u/0/uc?export=download&id=1a_95N5zQQoUCq8IBNVZgziHbeM-QxG2t": {"num_bytes": 79365502, "checksum": "6ec29a3f55153d167f0bdf05395558919ba0b1df9c63e79ffceda2a09884ad8b"}}, "download_size": 79365502, "post_processing_size": null, "dataset_size": 3003232, "size_in_bytes": 82368734}, "en": {"description": "HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to access a specialized position in the\nSpanish healthcare system, and are challenging even for highly specialized humans. They are designed by the Ministerio\nde Sanidad, Consumo y Bienestar Social.\n\nThe dataset contains questions about the following topics: medicine, nursing, psychology, chemistry, pharmacology and biology.\n", "citation": "@inproceedings{vilares-gomez-rodriguez-2019-head,\n    title = \"{HEAD}-{QA}: A Healthcare Dataset for Complex Reasoning\",\n    author = \"Vilares, David  and\n      G{'o}mez-Rodr{'i}guez, Carlos\",\n    booktitle = \"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics\",\n    month = jul,\n    year = \"2019\",\n    address = \"Florence, Italy\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/P19-1092\",\n    doi = \"10.18653/v1/P19-1092\",\n    pages = \"960--966\",\n    abstract = \"We present HEAD-QA, a multi-choice question answering testbed to encourage research on complex reasoning. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. We then consider monolingual (Spanish) and cross-lingual (to English) experiments with information retrieval and neural techniques. We show that: (i) HEAD-QA challenges current methods, and (ii) the results lag well behind human performance, demonstrating its usefulness as a benchmark for future work.\",\n}\n", "homepage": "https://aghie.github.io/head-qa/", "license": "MIT License", "features": {"name": {"dtype": "string", "id": null, "_type": "Value"}, "year": {"dtype": "string", "id": null, "_type": "Value"}, "category": {"dtype": "string", "id": null, "_type": "Value"}, "qid": {"dtype": "int32", "id": null, "_type": "Value"}, "qtext": {"dtype": "string", "id": null, "_type": "Value"}, "ra": {"dtype": "int32", "id": null, "_type": "Value"}, "image": {"id": null, "_type": "Image"}, "answers": [{"aid": {"dtype": "int32", "id": null, "_type": "Value"}, "atext": {"dtype": "string", "id": null, "_type": "Value"}}]}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "head_qa", "config_name": "en", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1155118, "num_examples": 2657, "dataset_name": "head_qa"}, "test": {"name": "test", "num_bytes": 1130122, "num_examples": 2742, "dataset_name": "head_qa"}, "validation": {"name": "validation", "num_bytes": 539190, "num_examples": 1366, "dataset_name": "head_qa"}}, "download_checksums": {"https://drive.google.com/u/0/uc?export=download&id=1a_95N5zQQoUCq8IBNVZgziHbeM-QxG2t": {"num_bytes": 79365502, "checksum": "6ec29a3f55153d167f0bdf05395558919ba0b1df9c63e79ffceda2a09884ad8b"}}, "download_size": 79365502, "post_processing_size": null, "dataset_size": 2824430, "size_in_bytes": 82189932}}

dummy/en/1.1.0/dummy_data.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a4444c7ec54df2feb8264c0e3e0fd4423ff6a834e8557e261ae5845ff9df8ca6
-size 2533
+oid sha256:273e9c051b7deafeef2b3d21e632cb681e37bd857e2abf75d20f5465ce2fa18e
+size 2532

dummy/es/1.1.0/dummy_data.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e32d340330882f3487b3ff718464864cbefe9e987781976e6d502dadca039bc0
-size 2533
+oid sha256:9da2da566d4d95069bf0d360a78c882762c171ecc42f14895bcf00ed6e1d4526
+size 2530

head_qa.py

@@ -50,10 +50,7 @@ _HOMEPAGE = "https://aghie.github.io/head-qa/"
 
 _LICENSE = "MIT License"
 
-_URLs = {
-    "es": "https://drive.google.com/uc?export=download&id=1dUIqVwvoZAtbX_-z5axCoe97XNcFo1No",
-    "en": "https://drive.google.com/uc?export=download&id=1phryJg4FjCFkn0mSCqIOP2-FscAeKGV0",
-}
+_URL = "https://drive.google.com/u/0/uc?export=download&id=1a_95N5zQQoUCq8IBNVZgziHbeM-QxG2t"
 
 _DIRS = {"es": "HEAD", "en": "HEAD_EN"}
 
@@ -81,7 +78,7 @@ class HeadQA(datasets.GeneratorBasedBuilder):
                     "qid": datasets.Value("int32"),
                     "qtext": datasets.Value("string"),
                     "ra": datasets.Value("int32"),
-                    "image": datasets.Value("string"),
+                    "image": datasets.Image(),
                     "answers": [
                         {
                             "aid": datasets.Value("int32"),
@@ -98,25 +95,27 @@ class HeadQA(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
-        data_dir = dl_manager.download_and_extract(_URLs[self.config.name])
+        data_dir = dl_manager.download_and_extract(_URL)
 
         dir = _DIRS[self.config.name]
-        data_dir = os.path.join(data_dir, dir)
+        data_lang_dir = os.path.join(data_dir, dir)
 
         return [
             datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, f"train_{dir}.json")}
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_lang_dir, f"train_{dir}.json")},
             ),
             datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, f"test_{dir}.json")}
+                name=datasets.Split.TEST,
+                gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_lang_dir, f"test_{dir}.json")},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepath": os.path.join(data_dir, f"dev_{dir}.json")},
+                gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_lang_dir, f"dev_{dir}.json")},
             ),
         ]
 
-    def _generate_examples(self, filepath):
+    def _generate_examples(self, data_dir, filepath):
         """Yields examples."""
         with open(filepath, encoding="utf-8") as f:
             head_qa = json.load(f)
@@ -129,7 +128,7 @@ class HeadQA(datasets.GeneratorBasedBuilder):
                     qid = int(question["qid"].strip())
                     qtext = question["qtext"].strip()
                     ra = int(question["ra"].strip())
-                    image = question["image"].strip()
+                    image_path = question["image"].strip()
 
                     aids = [answer["aid"] for answer in question["answers"]]
                     atexts = [answer["atext"].strip() for answer in question["answers"]]
@@ -143,6 +142,6 @@ class HeadQA(datasets.GeneratorBasedBuilder):
                         "qid": qid,
                         "qtext": qtext,
                         "ra": ra,
-                        "image": image,
+                        "image": os.path.join(data_dir, image_path) if image_path else None,
                         "answers": answers,
                     }