Initial Commit

split-test.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -12,238 +11,141 @@
 # 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.
-"""The Tweet Eval Datasets"""
+"""Indo Wordnet dataset"""
 
 
+import csv
+import json
+import os
+
 import datasets
 
 
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
 _CITATION = """\
-@inproceedings{barbieri2020tweeteval,
-  title={{TweetEval:Unified Benchmark and Comparative Evaluation for Tweet Classification}},
-  author={Barbieri, Francesco and Camacho-Collados, Jose and Espinosa-Anke, Luis and Neves, Leonardo},
-  booktitle={Proceedings of Findings of EMNLP},
-  year={2020}
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={huggingface, Inc.
+},
+year={2020}
 }
 """
 
+# TODO: Add description of the dataset here
+# You can copy an official description
 _DESCRIPTION = """\
-TweetEval consists of seven heterogenous tasks in Twitter, all framed as multi-class tweet classification. All tasks have been unified into the same benchmark, with each dataset presented in the same format and with fixed training, validation and test splits.
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
 """
 
-_HOMEPAGE = "https://github.com/cardiffnlp/tweeteval"
+# 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 = ""
 
-URL = "https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/"
-
-_URLs = {
-    "emoji": {
-        "train_text": URL + "emoji/train_text.txt",
-        "train_labels": URL + "emoji/train_labels.txt",
-        "test_text": URL + "emoji/test_text.txt",
-        "test_labels": URL + "emoji/test_labels.txt",
-        "val_text": URL + "emoji/val_text.txt",
-        "val_labels": URL + "emoji/val_labels.txt",
-    },
-    "emotion": {
-        "train_text": URL + "emotion/train_text.txt",
-        "train_labels": URL + "emotion/train_labels.txt",
-        "test_text": URL + "emotion/test_text.txt",
-        "test_labels": URL + "emotion/test_labels.txt",
-        "val_text": URL + "emotion/val_text.txt",
-        "val_labels": URL + "emotion/val_labels.txt",
-    },
-    "hate": {
-        "train_text": URL + "hate/train_text.txt",
-        "train_labels": URL + "hate/train_labels.txt",
-        "test_text": URL + "hate/test_text.txt",
-        "test_labels": URL + "hate/test_labels.txt",
-        "val_text": URL + "hate/val_text.txt",
-        "val_labels": URL + "hate/val_labels.txt",
-    },
-    "irony": {
-        "train_text": URL + "irony/train_text.txt",
-        "train_labels": URL + "irony/train_labels.txt",
-        "test_text": URL + "irony/test_text.txt",
-        "test_labels": URL + "irony/test_labels.txt",
-        "val_text": URL + "irony/val_text.txt",
-        "val_labels": URL + "irony/val_labels.txt",
-    },
-    "offensive": {
-        "train_text": URL + "offensive/train_text.txt",
-        "train_labels": URL + "offensive/train_labels.txt",
-        "test_text": URL + "offensive/test_text.txt",
-        "test_labels": URL + "offensive/test_labels.txt",
-        "val_text": URL + "offensive/val_text.txt",
-        "val_labels": URL + "offensive/val_labels.txt",
-    },
-    "sentiment": {
-        "train_text": URL + "sentiment/train_text.txt",
-        "train_labels": URL + "sentiment/train_labels.txt",
-        "test_text": URL + "sentiment/test_text.txt",
-        "test_labels": URL + "sentiment/test_labels.txt",
-        "val_text": URL + "sentiment/val_text.txt",
-        "val_labels": URL + "sentiment/val_labels.txt",
-    },
-    "stance": {
-        "abortion": {
-            "train_text": URL + "stance/abortion/train_text.txt",
-            "train_labels": URL + "stance/abortion/train_labels.txt",
-            "test_text": URL + "stance/abortion/test_text.txt",
-            "test_labels": URL + "stance/abortion/test_labels.txt",
-            "val_text": URL + "stance/abortion/val_text.txt",
-            "val_labels": URL + "stance/abortion/val_labels.txt",
-        },
-        "atheism": {
-            "train_text": URL + "stance/atheism/train_text.txt",
-            "train_labels": URL + "stance/atheism/train_labels.txt",
-            "test_text": URL + "stance/atheism/test_text.txt",
-            "test_labels": URL + "stance/atheism/test_labels.txt",
-            "val_text": URL + "stance/atheism/val_text.txt",
-            "val_labels": URL + "stance/atheism/val_labels.txt",
-        },
-        "climate": {
-            "train_text": URL + "stance/climate/train_text.txt",
-            "train_labels": URL + "stance/climate/train_labels.txt",
-            "test_text": URL + "stance/climate/test_text.txt",
-            "test_labels": URL + "stance/climate/test_labels.txt",
-            "val_text": URL + "stance/climate/val_text.txt",
-            "val_labels": URL + "stance/climate/val_labels.txt",
-        },
-        "feminist": {
-            "train_text": URL + "stance/feminist/train_text.txt",
-            "train_labels": URL + "stance/feminist/train_labels.txt",
-            "test_text": URL + "stance/feminist/test_text.txt",
-            "test_labels": URL + "stance/feminist/test_labels.txt",
-            "val_text": URL + "stance/feminist/val_text.txt",
-            "val_labels": URL + "stance/feminist/val_labels.txt",
-        },
-        "hillary": {
-            "train_text": URL + "stance/hillary/train_text.txt",
-            "train_labels": URL + "stance/hillary/train_labels.txt",
-            "test_text": URL + "stance/hillary/test_text.txt",
-            "test_labels": URL + "stance/hillary/test_labels.txt",
-            "val_text": URL + "stance/hillary/val_text.txt",
-            "val_labels": URL + "stance/hillary/val_labels.txt",
-        },
-    },
+# 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)
+_URLS = {
+    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
+    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
 }
 
+_LANGS = {
+    "english": "en",
+    "hindi": "hi"
+}
 
-class TweetEvalConfig(datasets.BuilderConfig):
-    def __init__(self, *args, type=None, sub_type=None, **kwargs):
-        super().__init__(
-            *args,
-            name=f"{type}" if type != "stance" else f"{type}_{sub_type}",
-            **kwargs,
+class SplitTestConfig(datasets.BuilderConfig):
+    """BuilderConfig for SplitTest."""
+
+    def __init__(self, name, **kwargs):
+        """
+        Args:
+          name: `string`, name of dataset config
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(SplitTestConfig, self).__init__(
+            version=datasets.Version("2.1.0", ""), name=name, **kwargs
         )
-        self.type = type
-        self.sub_type = sub_type
 
 
-class TweetEval(datasets.GeneratorBasedBuilder):
-    """TweetEval Dataset."""
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class SplitTest(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # 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.
+
+    # 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')
     BUILDER_CONFIGS = [
-        TweetEvalConfig(
-            type=key,
-            sub_type=None,
-            version=datasets.Version("1.1.0"),
-            description=f"This part of my dataset covers {key} part of TweetEval Dataset.",
-        )
-        for key in list(_URLs.keys())
-        if key != "stance"
-    ] + [
-        TweetEvalConfig(
-            type="stance",
-            sub_type=key,
-            version=datasets.Version("1.1.0"),
-            description=f"This part of my dataset covers stance_{key} part of TweetEval Dataset.",
-        )
-        for key in list(_URLs["stance"].keys())
+        SplitTestConfig(name="english", version=VERSION, description="English"),
+        SplitTestConfig(name="hindi", version=VERSION, description="Hindi"),
     ]
 
+    DEFAULT_CONFIG_NAME = "english"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
     def _info(self):
-        if self.config.type == "stance":
-            names = ["none", "against", "favor"]
-        elif self.config.type == "sentiment":
-            names = ["negative", "neutral", "positive"]
-        elif self.config.type == "offensive":
-            names = ["non-offensive", "offensive"]
-        elif self.config.type == "irony":
-            names = ["non_irony", "irony"]
-        elif self.config.type == "hate":
-            names = ["non-hate", "hate"]
-        elif self.config.type == "emoji":
-            names = [
-                "❤",
-                "😍",
-                "😂",
-                "💕",
-                "🔥",
-                "😊",
-                "😎",
-                "✨",
-                "💙",
-                "😘",
-                "📷",
-                "🇺🇸",
-                "☀",
-                "💜",
-                "��",
-                "💯",
-                "😁",
-                "🎄",
-                "📸",
-                "😜",
-            ]
-
-        else:
-            names = ["anger", "joy", "optimism", "sadness"]
 
+        features = datasets.Features(
+            {
+                "word": datasets.Value("string")
+            }
+        )
         return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
             description=_DESCRIPTION,
-            features=datasets.Features(
-                {"text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=names)}
-            ),
-            supervised_keys=None,
+            # 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
             citation=_CITATION,
         )
 
     def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        if self.config.type != "stance":
-            my_urls = _URLs[self.config.type]
-        else:
-            my_urls = _URLs[self.config.type][self.config.sub_type]
-        data_dir = dl_manager.download_and_extract(my_urls)
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+
+        file_name = "text." + _LANGS[self.config.name]
+
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 # These kwargs will be passed to _generate_examples
-                gen_kwargs={"text_path": data_dir["train_text"], "labels_path": data_dir["train_labels"]},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"text_path": data_dir["test_text"], "labels_path": data_dir["test_labels"]},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"text_path": data_dir["val_text"], "labels_path": data_dir["val_labels"]},
-            ),
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, file_name),
+                    "split": "train",
+                },
+            )
         ]
 
-    def _generate_examples(self, text_path, labels_path):
-        """Yields examples."""
-
-        with open(text_path, encoding="utf-8") as f:
-            texts = f.readlines()
-        with open(labels_path, encoding="utf-8") as f:
-            labels = f.readlines()
-        for i, text in enumerate(texts):
-            yield i, {"text": text.strip(), "label": int(labels[i].strip())}
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        with open(filepath, encoding="utf-8") as f:
+            for key, row in enumerate(f):
+                data = json.loads(row)
+                yield key, {
+                    "word": data["word"]
+                }