adding the files

README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+languages:
+- fa
+licenses:
+- cc-by-nc-sa-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- extended|translated|mnli
+task_categories:
+- sentiment-analysis
+task_ids:
+- sentiment-analysis
+---
+
+# Dataset Card for PersiNLU (Textual Entailment)
+
+## Table of Contents
+- [Dataset Card for PersiNLU (Sentiment Analysis)](#dataset-card-for-persi_sentiment)
+  - [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)
+      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
+      - [Who are the source language producers?](#who-are-the-source-language-producers)
+    - [Annotations](#annotations)
+      - [Annotation process](#annotation-process)
+      - [Who are the annotators?](#who-are-the-annotators)
+    - [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:** [Github](https://github.com/persiannlp/parsinlu/)
+- **Repository:** [Github](https://github.com/persiannlp/parsinlu/)
+- **Paper:** [Arxiv](https://arxiv.org/abs/2012.06154)
+- **Leaderboard:** 
+- **Point of Contact:** d.khashabi@gmail.com
+
+### Dataset Summary
+
+A Persian sentiment analysis dataset.  
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+The text dataset is in Persian (`fa`).
+
+## Dataset Structure
+
+### Data Instances
+
+Here is an example from the dataset: 
+```json 
+{
+  "review": "خوب بود ولی خیلی گرون شده دیگه...فک نکنم به این قیمت ارزش خرید داشته باشد",
+  "review_id": "1538",
+  "example_id": "4",
+  "excel_id": "food_194",
+  "question": "نظر شما در مورد بسته بندی و نگهداری این حلوا شکری، ارده و کنجد چیست؟",
+  "category": "حلوا شکری، ارده و کنجد",
+  "aspect": "بسته بندی",
+  "label": "-3",
+  "guid": "food-dev-r1538-e4"
+}
+```
+
+### Data Fields
+- `review`: the review text.
+- `review_id`: a unique id associated with the review.
+- `example_id`: a unique id associated with a particular attribute being addressed about the review.
+- `question`: a natural language question about a particular attribute.
+- `category`: the subject discussed in the review.
+- `aspect`: the aspect mentioned in the input question. 
+- `label`: the overall sentiment towards this particular subject, in the context of the mentioned aspect. Here are the definition of the labels: 
+```
+    '-3': 'no sentiment expressed',
+    '-2': 'very negative',
+    '-1': 'negative',
+    '0': 'neutral',
+    '1': 'positive',
+    '2': 'very positive',
+    '3': 'mixed',
+```
+     
+
+### Data Splits
+
+See the data. 
+
+## Dataset Creation
+
+### Curation Rationale
+
+For details, check [the corresponding draft](https://arxiv.org/abs/2012.06154).
+
+### 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
+
+CC BY-NC-SA 4.0 License 
+
+### Citation Information
+```bibtex 
+@article{huggingface:dataset,
+    title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian},
+    authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others},
+    year={2020}
+    journal = {arXiv e-prints},
+    eprint = {2012.06154},    
+}
+```
+
+### Contributions
+
+Thanks to [@danyaljj](https://github.com/danyaljj) for adding this dataset.

dataset_infos.json

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+{"parsinlu-repo": {"description": "A Persian sentiment analysis task (deciding whether a given sentence contains a particular sentiment).     \n", "citation": "@article{huggingface:dataset,\n    title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian},\n    authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others},\n    year={2020}\n    journal = {arXiv e-prints},\n    eprint = {2012.06154},    \n}\n", "homepage": "https://github.com/persiannlp/parsinlu/", "license": "CC BY-NC-SA 4.0", "features": {"review": {"dtype": "string", "id": null, "_type": "Value"}, "review_id": {"dtype": "string", "id": null, "_type": "Value"}, "example_id": {"dtype": "string", "id": null, "_type": "Value"}, "excel_id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "category": {"dtype": "string", "id": null, "_type": "Value"}, "aspect": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}, "guid": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "parsinlu_reading_comprehension", "config_name": "parsinlu-repo", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 5205739, "num_examples": 13617, "dataset_name": "parsinlu_reading_comprehension"}, "test_food": {"name": "test_food", "num_bytes": 480528, "num_examples": 1344, "dataset_name": "parsinlu_reading_comprehension"}, "test_movies": {"name": "test_movies", "num_bytes": 399773, "num_examples": 816, "dataset_name": "parsinlu_reading_comprehension"}, "validation_food": {"name": "validation_food", "num_bytes": 482942, "num_examples": 1330, "dataset_name": "parsinlu_reading_comprehension"}, "validation_movies": {"name": "validation_movies", "num_bytes": 168771, "num_examples": 360, "dataset_name": "parsinlu_reading_comprehension"}}, "download_checksums": {"https://raw.githubusercontent.com/persiannlp/parsinlu/master/data/sentiment-analysis/ABSA_Dataset_train.jsonl": {"num_bytes": 6580984, "checksum": "06ce2f89c5ce95d271a57857087fe983751c58067c70de414ad9e55c9ffb5716"}, "https://raw.githubusercontent.com/persiannlp/parsinlu/master/data/sentiment-analysis/food_dev.jsonl": {"num_bytes": 617236, "checksum": "2d60d49125ebc77cad125bf0cb3dd737909575bc55dbc69f71df0c1477e1f2a3"}, "https://raw.githubusercontent.com/persiannlp/parsinlu/master/data/sentiment-analysis/movie_dev.jsonl": {"num_bytes": 205095, "checksum": "efaca51170a804c336d98f2e2443536ce4201449c76e8dabf0815fc505cb3219"}, "https://raw.githubusercontent.com/persiannlp/parsinlu/master/data/sentiment-analysis/food_test.jsonl": {"num_bytes": 616236, "checksum": "688c2a108735307669e06359d138be4bf49a62b970a2ff3347802714296b69a4"}, "https://raw.githubusercontent.com/persiannlp/parsinlu/master/data/sentiment-analysis/movie_test.jsonl": {"num_bytes": 482153, "checksum": "209a7c9169a5e2ba8dc57cd727197f4230377a6093e740bfbce04013599e5239"}}, "download_size": 8501704, "post_processing_size": null, "dataset_size": 6737753, "size_in_bytes": 15239457}}

parsinlu_sentiment.py

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+# 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");
+# 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.
+"""ParsiNLU Persian reading comprehension task"""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import json
+
+import datasets
+from datasets import NamedSplit
+
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = """\
+@article{huggingface:dataset,
+    title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian},
+    authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others},
+    year={2020}
+    journal = {arXiv e-prints},
+    eprint = {2012.06154},    
+}
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+A Persian sentiment analysis task (deciding whether a given sentence contains a particular sentiment).     
+"""
+
+_HOMEPAGE = "https://github.com/persiannlp/parsinlu/"
+
+_LICENSE = "CC BY-NC-SA 4.0"
+
+_URL = "https://raw.githubusercontent.com/persiannlp/parsinlu/master/data/sentiment-analysis/"
+_URLs = {
+    "train": _URL + "ABSA_Dataset_train.jsonl",
+    "dev_food": _URL + "food_dev.jsonl",
+    "dev_movies": _URL + "movie_dev.jsonl",
+    "test_food": _URL + "food_test.jsonl",
+    "test_movies": _URL + "movie_test.jsonl",
+}
+
+TRAIN_ALL = NamedSplit("train")
+TEST_FOOD = NamedSplit("test_food")
+TEST_MOVIES = NamedSplit("test_movies")
+VALIDATION_FOOD = NamedSplit("validation_food")
+VALIDATION_MOVIES = NamedSplit("validation_movies")
+
+
+class ParsinluReadingComprehension(datasets.GeneratorBasedBuilder):
+    """ParsiNLU Persian reading comprehension task."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="parsinlu-repo", version=VERSION, description="ParsiNLU repository: sentiment-analysis"
+        ),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "review": datasets.Value("string"),
+                "review_id": datasets.Value("string"),
+                "example_id": datasets.Value("string"),
+                "excel_id": datasets.Value("string"),
+                "question": datasets.Value("string"),
+                "category": datasets.Value("string"),
+                "aspect": datasets.Value("string"),
+                "label": datasets.Value("string"),
+                "guid": datasets.Value("string"),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            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,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # 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):
+        data_dir = dl_manager.download_and_extract(_URLs)
+        return [
+            datasets.SplitGenerator(
+                name=TRAIN_ALL,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir["train"],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=TEST_FOOD,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": data_dir["test_food"], "split": "test_food"},
+            ),
+            datasets.SplitGenerator(
+                name=TEST_MOVIES,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": data_dir["test_movies"], "split": "test_movies"},
+            ),
+            datasets.SplitGenerator(
+                name=VALIDATION_FOOD,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir["dev_food"],
+                    "split": "dev_food",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=VALIDATION_MOVIES,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir["dev_movies"],
+                    "split": "dev_movies",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        logger.info("generating examples from = %s", filepath)
+
+        with open(filepath, encoding="utf-8") as f:
+            for id_, row in enumerate(f.readlines()):
+                row = json.loads(row)
+                yield id_, row