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README.md

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+---
+annotations_creators:
+- found
+language:
+- en
+language_creators:
+- found
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+pretty_name: 'SemEval 2015: Aspect-based Sentiement Analysis'
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+tags:
+- aspect-based-sentiment-analysis
+- semeval
+- semeval2015
+task_categories:
+- text-classification
+task_ids:
+- sentiment-classification
+---
+
+# Dataset Card for SemEval Task 5: Aspect-based Sentiment Analysis
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:**
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+This dataset is orignally from [SemEval-2015 Task 12](https://alt.qcri.org/semeval2015/task12/).
+From the page:
+> SE-ABSA15 will focus on the same domains as SE-ABSA14 (restaurants and laptops). However, unlike SE-ABSA14, the input datasets of SE-ABSA15 will contain entire reviews, not isolated (potentially out of context) sentences.  SE-ABSA15 consolidates the four subtasks of SE-ABSA14 within a unified framework. In addition, SE-ABSA15 will include an out-of-domain ABSA subtask, involving test data from a domain unknown to the participants, other than the domains that will be considered during training. In particular, SE-ABSA15 consists of the following two subtasks.
+
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.

dataset_infos.json

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+{"laptop": {"description": "This dataset is built as a playground for aspect-based sentiment analysis.\n", "citation": "", "homepage": "https://alt.qcri.org/semeval2015/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "aspects": {"feature": {"term": {"dtype": "string", "id": null, "_type": "Value"}, "polarity": {"dtype": "string", "id": null, "_type": "Value"}, "from": {"dtype": "int16", "id": null, "_type": "Value"}, "to": {"dtype": "int16", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "absa", "config_name": "laptop", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 410525, "num_examples": 3048, "dataset_name": "absa"}, "validation": {"name": "validation", "num_bytes": 101593, "num_examples": 800, "dataset_name": "absa"}}, "download_checksums": {"https://drive.google.com/uc?id=1Zvh4bZOZgSkIHrrA5WVvyPQO6-wWk4xQ": {"num_bytes": 568072, "checksum": "061e7902171bc3e08bd1bdc79c5766423c36cf29c29b4c9df5a53de800d5e9af"}, "https://drive.google.com/uc?id=14NgRdqcEHFfki0z49iMR8wqOEBnqdLH9": {"num_bytes": 142849, "checksum": "98c0459acb7daa1546916ea3fa5e795ceb3eacae0c5747206a559f0e8d46a7cd"}}, "download_size": 710921, "post_processing_size": null, "dataset_size": 512118, "size_in_bytes": 1223039}, "restaurant": {"description": "This dataset is built as a playground for aspect-based sentiment analysis.\n", "citation": "", "homepage": "https://alt.qcri.org/semeval2015/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "aspects": {"feature": {"term": {"dtype": "string", "id": null, "_type": "Value"}, "polarity": {"dtype": "string", "id": null, "_type": "Value"}, "from": {"dtype": "int16", "id": null, "_type": "Value"}, "to": {"dtype": "int16", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "category": {"feature": {"category": {"dtype": "string", "id": null, "_type": "Value"}, "polarity": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "absa", "config_name": "restaurant", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 545642, "num_examples": 3044, "dataset_name": "absa"}, "validation": {"name": "validation", "num_bytes": 160312, "num_examples": 800, "dataset_name": "absa"}}, "download_checksums": {"https://drive.google.com/uc?id=1fx1fWemdTYjonYSVfX-vcgU3KQa7C85V": {"num_bytes": 831483, "checksum": "6ff945386c4d0cab23728fe316298c7c534a7cc713b5f9a40349722d0fa7e0f2"}, "https://drive.google.com/uc?id=1fHD0USeUgiLrnTo6zvRajk8whvsTVdAX": {"num_bytes": 239963, "checksum": "2600b4af013590b4c613e1cbae12071fcb097860f5e3253d0cab2a9f886648cd"}}, "download_size": 1071446, "post_processing_size": null, "dataset_size": 705954, "size_in_bytes": 1777400}}

semeval-absa.py

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+# Copyright 2020 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.
+# TODO: Address all TODOs and remove all explanatory comments
+"""SemEval 2015: Aspect-based Sentiment Analysis"""
+
+
+import csv
+import json
+import os
+
+import datasets
+
+_DESCRIPTION = """\
+This dataset is built as a playground for aspect-based sentiment analysis.
+"""
+
+_HOMEPAGE = "https://alt.qcri.org/semeval2015/"
+
+# 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_LAPTOP_URL = "https://drive.google.com/uc?id=1Zvh4bZOZgSkIHrrA5WVvyPQO6-wWk4xQ"
+_VAL_LAPTOP_URL = "https://drive.google.com/uc?id=14NgRdqcEHFfki0z49iMR8wqOEBnqdLH9"
+_TRAIN_RESTAURANT_URL = "https://drive.google.com/uc?id=1fx1fWemdTYjonYSVfX-vcgU3KQa7C85V"
+_VAL_RESTAURANT_URL = "https://drive.google.com/uc?id=1fHD0USeUgiLrnTo6zvRajk8whvsTVdAX"
+
+DOMAINS = ['laptop', 'restaurant']
+
+class ABSAConfig(datasets.BuilderConfig):
+    """SemEval 2015 - ABSA Configs"""
+    
+    def __init__(self, domain: str, **kwargs):
+        if domain not in DOMAINS:
+            raise ValueError(f"Invalild domain: {domain}. Available domains: {DOMAINS}",)
+        
+        name = domain
+        super(ABSAConfig, self).__init__(name=name, description=_DESCRIPTION, **kwargs)
+        
+        self.domain = domain
+        
+        self.url_train = _TRAIN_LAPTOP_URL if domain == 'laptop' else _TRAIN_RESTAURANT_URL
+        self.url_val = _VAL_LAPTOP_URL if domain == 'laptop' else _VAL_RESTAURANT_URL
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class ABSA(datasets.GeneratorBasedBuilder):
+    """SemEval 2015: Aspect-based Sentiment Analysis."""
+
+    _VERSION = datasets.Version("1.0.0")
+    
+    BUILDER_CONFIGS = [
+        ABSAConfig(
+            domain='laptop',
+            version=_VERSION
+        ),
+        ABSAConfig(
+            domain='restaurant',
+            version=_VERSION
+        )
+    ]
+
+    def _info(self):
+        if self.config.domain == 'restaurant':
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "aspects": datasets.Sequence({
+                        'term': datasets.Value("string"),
+                        'polarity': datasets.Value("string"),
+                        'from': datasets.Value("int16"),
+                        'to': datasets.Value("int16"),
+                    }),
+                    "category": datasets.Sequence({
+                        'category': datasets.Value("string"),
+                        'polarity': datasets.Value("string")
+                    })
+                }
+            )
+        else:
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "aspects": datasets.Sequence({
+                        'term': datasets.Value("string"),
+                        'polarity': datasets.Value("string"),
+                        'from': datasets.Value("int16"),
+                        'to': datasets.Value("int16"),
+                    })
+                }
+            )
+#         features = datasets.Features(
+#             {
+#                 "id": datasets.Value("int16"),
+#                 "text": datasets.Value("string"),
+#                 "aspects": datasets.Sequence([{
+#                     'term': datasets.Value("string"),
+#                     'polarity': datasets.Value("string"),
+#                     'from': datasets.Value("int8"),
+#                     'to': datasets.Value("int8"),
+#                 }]),
+#                 "category": datasets.Sequence([{
+#                     'category': datasets.Value("string"),
+#                     'polarity': datasets.Value("string")
+#                 }])
+#             }
+#         )
+        
+        
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE
+        )
+
+    def _split_generators(self, dl_manager):
+
+        train_path = dl_manager.download(self.config.url_train)
+        val_path = dl_manager.download(self.config.url_val)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": val_path})
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath):
+        """Generate examples."""
+        with open(filepath, 'r') as f:
+            contents = json.load(f)
+            for id_, row in enumerate(contents):
+                yield id_, row