Update demo2.py

demo2.py

@@ -1,4 +1,5 @@
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -11,128 +12,101 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-# TODO: Address all TODOs and remove all explanatory comments
-"""TODO: Add a description here."""
-
 
+# Lint as: python3
+"""SQUAD: The Stanford Question Answering Dataset."""
 import csv
 import json
-import os
 
 import datasets
+from datasets import TextClassification
+
 
+logger = datasets.logging.get_logger(__name__)
 
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """
-@InProceedings{huggingface:dataset,
-title = {A great new dataset},
-author={huggingface, Inc.
-},
-year={2020}
+
+_CITATION = """\
+@article{2016arXiv160605250R,
+       author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
+                 Konstantin and {Liang}, Percy},
+        title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
+      journal = {arXiv e-prints},
+         year = 2016,
+          eid = {arXiv:1606.05250},
+        pages = {arXiv:1606.05250},
+archivePrefix = {arXiv},
+       eprint = {1606.05250},
 }
 """
 
-# TODO: Add description of the dataset here
-# You can copy an official description
-_DESCRIPTION = """
-This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+_DESCRIPTION = """\
+Stanford Question Answering Dataset (SQuAD) is a reading comprehension \
+dataset, consisting of questions posed by crowdworkers on a set of Wikipedia \
+articles, where the answer to every question is a segment of text, or span, \
+from the corresponding reading passage, or the question might be unanswerable.
 """
 
-# TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = ""
-
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = ""
-
-# TODO: Add link to the official dataset URLs here
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-train_url = "https://dev-s3.outer.fuxi.netease.com/models/075d0c8a362646ad98a0c7a70286e93a/train.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220620T023414Z&X-Amz-SignedHeaders=host&X-Amz-Expires=14515&X-Amz-Credential=HHRC8E0HQWZYBIVXJSZE%2F20220620%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=10b7e6d8b179ef7849241fbadac6d29be4723fc25d15a5d5bd541082e2b81931"
-test_url = "https://dev-s3.outer.fuxi.netease.com/models/075d0c8a362646ad98a0c7a70286e93a/test.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220620T023414Z&X-Amz-SignedHeaders=host&X-Amz-Expires=14515&X-Amz-Credential=HHRC8E0HQWZYBIVXJSZE%2F20220620%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=11c3ae701c7aa1e2444e479b76eb1a1bf1258c8afa2f67ab759beb3956350e2e"
+train_url = "https://dev-s3.outer.fuxi.netease.com/models/075d0c8a362646ad98a0c7a70286e93a/train.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220620T072759Z&X-Amz-SignedHeaders=host&X-Amz-Expires=604&X-Amz-Credential=HHRC8E0HQWZYBIVXJSZE%2F20220620%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=8c6493e5c5b9375d4b4af496e90b7767cc8a22abb247d562993b2593fce71873"
+test_url = "https://dev-s3.outer.fuxi.netease.com/models/075d0c8a362646ad98a0c7a70286e93a/test.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220620T072759Z&X-Amz-SignedHeaders=host&X-Amz-Expires=604&X-Amz-Credential=HHRC8E0HQWZYBIVXJSZE%2F20220620%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=2711f1125f654b392ca7a57809dc39b0819f2211dd77510a6fe9ea84dd164f1a"
 
 _URLS = {
-
     "train": train_url,
     "test": test_url,
 }
 
 
-# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
-class DemoDataset(datasets.GeneratorBasedBuilder):
-    """TODO: Short description of my dataset."""
-
-    VERSION = datasets.Version("1.0.0")
+class Demo2Config(datasets.BuilderConfig):
 
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+    def __init__(self, **kwargs):
+        super(Demo2Config, self).__init__(**kwargs)
 
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
 
-    # You will be able to load one or the other configurations in the following list with
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
+class Demo2(datasets.GeneratorBasedBuilder):
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="plain_text", version=VERSION, description="Plain text")
+        Demo2Config(
+            name="plain_text",
+            version=datasets.Version("1.0.0", ""),
+            description="Plain text",
+        ),
     ]
 
     def _info(self):
-        features = datasets.Features(
-            {
-                "index": datasets.Value("string"),
-                "text": datasets.Value("string"),
-                "label": datasets.Value("string")
-            }
-        )
-
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
-            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-            # supervised_keys=("sentence", "label"),
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
+            features=datasets.Features(
+                {
+                    "index": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "label": datasets.Value("string")
+                }
+            ),
+            # No default supervised_keys (as we have to pass both question
+            # and context as input).
+            supervised_keys=None,
+            homepage="https://rajpurkar.github.io/SQuAD-explorer/",
             citation=_CITATION,
+            task_templates=[
+                TextClassification(
+                    text_column="text", label_column="label"
+                )
+            ],
         )
 
     def _split_generators(self, dl_manager):
-        download_files = dl_manager.download_and_extract(_URLS)
-        print('download_files:', download_files)
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+
         return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": download_files['train']
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": download_files['test']
-                },
-            ),
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["test"]}),
         ]
 
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     def _generate_examples(self, filepath):
+        logger.info("generating examples from = %s", filepath)
         with open(filepath, encoding="utf-8") as f:
-            reader = csv.DictReader(f)
-            for key, row in enumerate(reader):
-                    print(key)
-                    print(row["index"])
-                    print(row["text"])
-                    print(row["label"])
-                    yield key, {
-                        "index": row["index"],
-                        "text": row["text"],
-                        "label": row["label"]
-                    }
+            demo2 = csv.DictReader(f)
+            for key, row in enumerate(demo2):
+                yield key, {
+                    "id": key,
+                    "text": row['text'],
+                    "label": row['label'],
+                }