Update files from the datasets library (from 1.5.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.5.0

.gitattributes

@@ -0,0 +1,27 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,174 @@
+---
+annotations_creators:
+- found
+language_creators:
+- found
+languages:
+- ro
+licenses:
+- cc-by-4-0
+multilinguality:
+- monolingual
+size_categories:
+- n=15K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- sentiment-classification
+---
+
+# Dataset Card for LaRoSeDa
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [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:** [Github](https://github.com/ancatache/LaRoSeDa)
+- **Repository:** [Github](https://github.com/ancatache/LaRoSeDa)
+- **Paper:** [Arxiv](https://arxiv.org/pdf/2101.04197.pdf)
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** raducu.ionescu@gmail.com
+
+### Dataset Summary
+
+LaRoSeDa - A **La**rge and **Ro**manian **Se**ntiment **Da**ta Set. LaRoSeDa contains 15,000 reviews written in Romanian, of which 7,500 are positive and 7,500 negative. 
+The samples have one of four star ratings: 1 or 2 - for reviews that can be considered of negative polarity, and 4 or 5 for the positive ones.
+The 15,000 samples featured in the corpus and labelled with the star rating, are splitted in a train and test subsets, with 12,000 and 3,000 samples in each subset.
+
+### Supported Tasks and Leaderboards
+
+[LiRo Benchmark and Leaderboard](https://eemlcommunity.github.io/ro_benchmark_leaderboard/site/)
+
+### Languages
+
+The text dataset is in Romanian (`ro`).
+
+## Dataset Structure
+
+### Data Instances
+
+Below we have an example of sample from LaRoSeDa:
+
+```
+{
+    "index": "9675",
+    "title": "Nu recomand",
+    "content": "probleme cu localizarea, mari...",
+    "starRating": 1,
+}
+```
+
+where "9675" is the sample index, followed by the title of the review, review content and then the star rating given by the user.
+
+
+### Data Fields
+
+- `index`: string, the unique indentifier of a sample.
+- `title`: string, the review title.
+- `content`: string, the content of the review.
+- `starRating`: integer, with values in the following set {1, 2, 4, 5}.
+
+### Data Splits
+
+The train/test split contains 12,000/3,000 samples tagged with the star rating assigned to each sample in the dataset.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The samples are preprocessed in order to eliminate named entities. This is required to prevent classifiers from taking the decision based on features that are not related to the topics. 
+For example, named entities that refer to politicians or football players names can provide clues about the topic. For more details, please read the [paper](https://arxiv.org/abs/1901.06543).
+
+### Source Data
+
+
+#### Data Collection and Normalization
+
+For the data collection, one of the largest Romanian e-commerce platform was targetted. Along with the textual content of each review, the associated star ratings was also collected in order to automatically assign labels to
+the collected text samples. 
+
+
+#### Who are the source language producers?
+
+The original text comes from one of the largest e-commerce platforms in Romania.
+
+### Annotations
+
+#### Annotation process
+
+As mentioned above, LaRoSeDa is composed of product reviews from one of the largest e-commerce websites in Romania. The resulting samples are automatically tagged with the star rating assigned by the users.
+
+#### Who are the annotators?
+
+N/A
+
+### Personal and Sensitive Information
+
+The textual data collected for LaRoSeDa consists in product reviews freely available on the Internet. 
+To the best of authors' knowledge, there is no personal or sensitive information that needed to be considered in the said textual inputs collected.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+This dataset is part of an effort to encourage text classification research in languages other than English. Such work increases the accessibility of natural language technology to more regions and cultures. 
+In the past three years there was a growing interest for studying Romanian from a Computational Linguistics perspective. However, we are far from having enough datasets and resources in this particular language.
+
+### Discussion of Biases
+
+*We note that most of the negative reviews (5,561) are rated with one star. Similarly, most of the positive reviews (6,238) are rated with five stars. Hence, the corpus is highly polarized.*
+
+### Other Known Limitations
+
+*The star rating might not always reflect the polarity of the text. We thus acknowledge that the automatic labeling process is not optimal, i.e. some labels might be noisy.*
+
+## Additional Information
+
+### Dataset Curators
+
+Published and managed by Anca Tache, Mihaela Gaman and Radu Tudor Ionescu.
+
+### Licensing Information
+
+CC BY-SA 4.0 License
+
+### Citation Information
+
+```
+@article{
+    tache2101clustering,
+    title={Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa -- A Large Romanian Sentiment Data Set},
+    author={Anca Maria Tache and Mihaela Gaman and Radu Tudor Ionescu},
+    journal={ArXiv},
+    year = {2021}
+}
+```
+
+### Contributions
+
+Thanks to [@MihaelaGaman](https://github.com/MihaelaGaman) for adding this dataset.
+

dataset_infos.json

@@ -0,0 +1 @@
+{"laroseda": {"description": "        LaRoSeDa (A Large Romanian Sentiment Data Set) contains 15,000 reviews written in Romanian, of which 7,500 are positive and 7,500 negative.\n        Star ratings of 1 and 2 and of 4 and 5 are provided for negative and positive reviews respectively.\n        The current dataset uses star rating as the label for multi-class classification.\n", "citation": "@article{\n    tache2101clustering,\n    title={Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa -- A Large Romanian Sentiment Data Set},\n    author={Anca Maria Tache and Mihaela Gaman and Radu Tudor Ionescu},\n    journal={ArXiv},\n    year = {2021}\n}\n", "homepage": "https://github.com/ancatache/LaRoSeDa", "license": "CC BY-SA 4.0 License", "features": {"index": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "content": {"dtype": "string", "id": null, "_type": "Value"}, "starRating": {"dtype": "int64", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "laroseda", "config_name": "laroseda", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2932819, "num_examples": 12000, "dataset_name": "laroseda"}, "test": {"name": "test", "num_bytes": 700834, "num_examples": 3000, "dataset_name": "laroseda"}}, "download_checksums": {"https://raw.githubusercontent.com/ancatache/LaRoSeDa/main/data_splitted/laroseda_train.json": {"num_bytes": 4231664, "checksum": "808ae4c9c17d6d75801c48e838d8a700fd950ec9654ab60c386e38193b8f1b8e"}, "https://raw.githubusercontent.com/ancatache/LaRoSeDa/main/data_splitted/laroseda_test.json": {"num_bytes": 1025519, "checksum": "6084cabe923da9f71413357eaa84f3ea4523ef5ff0e81e2c3ac3861405e2151a"}}, "download_size": 5257183, "post_processing_size": null, "dataset_size": 3633653, "size_in_bytes": 8890836}}

dummy/laroseda/1.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3825f6ee3316fdad0281026937b9f533950706d17f37042038f44064b5df1f2c
+size 1806

laroseda.py

@@ -0,0 +1,136 @@
+# coding=utf-8
+# Copyright 2021 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.
+"""LaRoSeDa: A Large Romanian Sentiment Data Set"""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+
+import datasets
+
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@article{
+    tache2101clustering,
+    title={Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa -- A Large Romanian Sentiment Data Set},
+    author={Anca Maria Tache and Mihaela Gaman and Radu Tudor Ionescu},
+    journal={ArXiv},
+    year = {2021}
+}
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+        LaRoSeDa (A Large Romanian Sentiment Data Set) contains 15,000 reviews written in Romanian, of which 7,500 are positive and 7,500 negative.
+        Star ratings of 1 and 2 and of 4 and 5 are provided for negative and positive reviews respectively.
+        The current dataset uses star rating as the label for multi-class classification.
+"""
+
+_HOMEPAGE = "https://github.com/ancatache/LaRoSeDa"
+
+_LICENSE = "CC BY-SA 4.0 License"
+
+# The HuggingFace dataset library don't host the datasets but only point to the original files
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URL = "https://raw.githubusercontent.com/ancatache/LaRoSeDa/main/data_splitted/"
+
+_TRAIN_FILE = "laroseda_train.json"
+_TEST_FILE = "laroseda_test.json"
+
+
+class LarosedaConfig(datasets.BuilderConfig):
+    """BuilderConfig for the LaRoSeDa dataset"""
+
+    def __init__(self, **kwargs):
+        super(LarosedaConfig, self).__init__(**kwargs)
+
+
+class Laroseda(datasets.GeneratorBasedBuilder):
+    """LaRoSeDa dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        LarosedaConfig(name="laroseda", version=VERSION, description="LaRoSeDa dataset"),
+    ]
+
+    def _info(self):
+
+        features = datasets.Features(
+            {
+                "index": datasets.Value("string"),
+                "title": datasets.Value("string"),
+                "content": datasets.Value("string"),
+                "starRating": datasets.Value("int64"),
+            }
+        )
+
+        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):
+        """Returns SplitGenerators."""
+
+        urls_to_download = {
+            "train": _URL + _TRAIN_FILE,
+            "test": _URL + _TEST_FILE,
+        }
+
+        downloaded_files = dl_manager.download(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": downloaded_files["train"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": downloaded_files["test"],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+
+        with open(filepath, "r", encoding="utf-8") as f:
+            data_list = json.load(f)["reviews"]
+
+            for i, d in enumerate(data_list):
+                yield i, {
+                    "index": d["index"],
+                    "title": d["title"],
+                    "content": d["content"],
+                    "starRating": int(d["starRating"]),
+                }