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indolem_sentiment.py

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+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
+"""
+This template serves as a starting point for contributing a dataset to the Nusantara Dataset repo.
+
+When modifying it for your dataset, look for TODO items that offer specific instructions.
+
+Full documentation on writing dataset loading scripts can be found here:
+https://huggingface.co/docs/datasets/add_dataset.html
+
+To create a dataset loading script you will create a class and implement 3 methods:
+  * `_info`: Establishes the schema for the dataset, and returns a datasets.DatasetInfo object.
+  * `_split_generators`: Downloads and extracts data for each split (e.g. train/val/test) or associate local data with each split.
+  * `_generate_examples`: Creates examples from data on disk that conform to each schema defined in `_info`.
+
+TODO: Before submitting your script, delete this doc string and replace it with a description of your dataset.
+
+[nusantara_schema_name] = (kb, pairs, qa, text, t2t, entailment)
+"""
+from base64 import encode
+import json
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from nusacrowd.utils import schemas
+from nusacrowd.utils.common_parser import load_conll_data
+from nusacrowd.utils.configs import NusantaraConfig
+from nusacrowd.utils.constants import Tasks, DEFAULT_SOURCE_VIEW_NAME, DEFAULT_NUSANTARA_VIEW_NAME
+
+# TODO: Add BibTeX citation
+_CITATION = """\
+@article{DBLP:journals/corr/abs-2011-00677,
+  author    = {Fajri Koto and
+               Afshin Rahimi and
+               Jey Han Lau and
+               Timothy Baldwin},
+  title     = {IndoLEM and IndoBERT: {A} Benchmark Dataset and Pre-trained Language
+               Model for Indonesian {NLP}},
+  journal   = {CoRR},
+  volume    = {abs/2011.00677},
+  year      = {2020},
+  url       = {https://arxiv.org/abs/2011.00677},
+  eprinttype = {arXiv},
+  eprint    = {2011.00677},
+  timestamp = {Fri, 06 Nov 2020 15:32:47 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2011-00677.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+# TODO: create a module level variable with your dataset name (should match script name)
+#  E.g. Hallmarks of Cancer: [dataset_name] --> hallmarks_of_cancer
+_DATASETNAME = "indolem_sentiment"
+_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
+_UNIFIED_VIEW_NAME = DEFAULT_NUSANTARA_VIEW_NAME
+
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_LOCAL = False
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+IndoLEM (Indonesian Language Evaluation Montage) is a comprehensive Indonesian benchmark that comprises of seven tasks for the Indonesian language. This benchmark is categorized into three pillars of NLP tasks: morpho-syntax, semantics, and discourse.
+
+This dataset is based on binary classification (positive and negative), with distribution:
+* Train: 3638 sentences
+* Development: 399 sentences
+* Test: 1011 sentences
+
+The data is sourced from 1) Twitter [(Koto and Rahmaningtyas, 2017)](https://www.researchgate.net/publication/321757985_InSet_Lexicon_Evaluation_of_a_Word_List_for_Indonesian_Sentiment_Analysis_in_Microblogs)
+and 2) [hotel reviews](https://github.com/annisanurulazhar/absa-playground/).
+
+The experiment is based on 5-fold cross validation.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here (if possible)
+_HOMEPAGE = "https://indolem.github.io/"
+
+# TODO: Add the licence for the dataset here (if possible)
+# Note that this doesn't have to be a common open source license.
+# Some datasets have custom licenses. In this case, simply put the full license terms
+# into `_LICENSE`
+_LICENSE = "Creative Commons Attribution Share-Alike 4.0 International"
+
+# TODO: Add links to the urls needed to download your dataset files.
+#  For local datasets, this variable can be an empty dictionary.
+
+# For publicly available datasets you will most likely end up passing these URLs to dl_manager in _split_generators.
+# In most cases the URLs will be the same for the source and nusantara config.
+# However, if you need to access different files for each config you can have multiple entries in this dict.
+# This can be an arbitrarily nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    _DATASETNAME: {
+        'train': 'https://raw.githubusercontent.com/indolem/indolem/main/sentiment/data/train0.csv',
+        'dev': 'https://raw.githubusercontent.com/indolem/indolem/main/sentiment/data/dev0.csv',
+        'test': 'https://raw.githubusercontent.com/indolem/indolem/main/sentiment/data/test0.csv'
+    }
+}
+
+# TODO: add supported task by dataset. One dataset may support multiple tasks
+_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS]  # example: [Tasks.TRANSLATION, Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]
+
+# TODO: set this to a version that is associated with the dataset. if none exists use "1.0.0"
+#  This version doesn't have to be consistent with semantic versioning. Anything that is
+#  provided by the original dataset as a version goes.
+_SOURCE_VERSION = "1.0.0"
+
+_NUSANTARA_VERSION = "1.0.0"
+
+
+# TODO: Name the dataset class to match the script name using CamelCase instead of snake_case
+class IndolemSentimentDataset(datasets.GeneratorBasedBuilder):
+
+    label_classes = ['negative','positive']
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+
+    # You will be able to load the "source" or "nusanrata" configurations with
+    # ds_source = datasets.load_dataset('my_dataset', name='source')
+    # ds_nusantara = datasets.load_dataset('my_dataset', name='nusantara')
+
+    # For local datasets you can make use of the `data_dir` and `data_files` kwargs
+    # https://huggingface.co/docs/datasets/add_dataset.html#downloading-data-files-and-organizing-splits
+    # ds_source = datasets.load_dataset('my_dataset', name='source', data_dir="/path/to/data/files")
+    # ds_nusantara = datasets.load_dataset('my_dataset', name='nusantara', data_dir="/path/to/data/files")
+
+    # TODO: For each dataset, implement Config for Source and Nusantara;
+    #  If dataset contains more than one subset (see nusantara/nusa_datasets/smsa.py) implement for EACH of them.
+    #  Each of them should contain:
+    #   - name: should be unique for each dataset config eg. smsa_(source|nusantara)_[nusantara_schema_name]
+    #   - version: option = (SOURCE_VERSION|NUSANTARA_VERSION)
+    #   - description: one line description for the dataset
+    #   - schema: options = (source|nusantara_[nusantara_schema_name])
+    #   - subset_id: subset id is the canonical name for the dataset (eg. smsa)
+    #  where [nusantara_schema_name] = (kb, pairs, qa, text, t2t)
+
+    BUILDER_CONFIGS = [
+        NusantaraConfig(
+            name="indolem_sentiment_source",
+            version=SOURCE_VERSION,
+            description="indolem_sentiment source schema",
+            schema="source",
+            subset_id="indolem_sentiment",
+        ),
+        NusantaraConfig(
+            name="indolem_sentiment_nusantara_text",
+            version=NUSANTARA_VERSION,
+            description="indolem_sentiment Nusantara schema",
+            schema="nusantara_text",
+            subset_id="indolem_sentiment",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "indolem_sentiment_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        # Create the source schema; this schema will keep all keys/information/labels as close to the original dataset as possible.
+        # You can arbitrarily nest lists and dictionaries.
+        # For iterables, use lists over tuples or `datasets.Sequence`
+
+        if self.config.schema == "source":
+            features = datasets.Features({"sentence":datasets.Value("string"), "sentiment": datasets.Value("int32")})
+        elif self.config.schema == "nusantara_text":
+            features = schemas.text_features(self.label_classes)
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If you need to access the "source" or "nusantara" config choice, that will be in self.config.name
+        # LOCAL DATASETS: You do not need the dl_manager; you can ignore this argument. Make sure `gen_kwargs` in the return gets passed the right filepath
+        # PUBLIC DATASETS: Assign your data-dir based on the dl_manager.
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs; many examples use the download_and_extract method; see the DownloadManager docs here: https://huggingface.co/docs/datasets/package_reference/builder_classes.html#datasets.DownloadManager
+        # dl_manager can accept any type of nested list/dict and will give back the same structure with the url replaced with the path to local files.
+
+        # TODO: KEEP if your dataset is PUBLIC; remove if not
+        urls = _URLS[_DATASETNAME]
+        train_data = Path(dl_manager.download(urls['train']))
+        test_data = Path(dl_manager.download(urls['test']))
+        dev_data = Path(dl_manager.download(urls['dev']))
+        
+        # Not all datasets have predefined canonical train/val/test splits.
+        # If your dataset has no predefined splits, use datasets.Split.TRAIN for all of the data.
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # Whatever you put in gen_kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": train_data,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": test_data,
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": dev_data,
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    # TODO: change the args of this function to match the keys in `gen_kwargs`. You may add any necessary kwargs.
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        # 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.
+        # NOTE: For local datasets you will have access to self.config.data_dir and self.config.data_files
+
+        with filepath.open('r', encoding='utf-8') as f:
+            line = f.readline()
+            id = 0
+            while line:
+                line = f.readline().strip()
+                if len(line) == 0: break
+                
+                ex = {}
+                id += 1
+                sentence = line[:-2].strip('"')
+                sentiment = int(line[-1])
+                if self.config.schema == 'source':
+                    ex = {'sentence': sentence, 'sentiment': sentiment}
+                elif self.config.schema == 'nusantara_text':
+                    ex = {'id': str(id), 'text': str(sentence), 'label': self.label_classes[sentiment]}
+                else:
+                    raise ValueError(f"Invalid config: {self.config.name}")
+                
+                yield id, ex
+
+# This template is based on the following template from the datasets package:
+# https://github.com/huggingface/datasets/blob/master/templates/new_dataset_script.py