Update files from the datasets library (from 1.13.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.13.0

.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 +1,166 @@
+---
+annotations_creators:
+- expert-generated
+language_creators:
+- crowdsourced
+languages:
+- en-IN
+- kn-IN
+licenses:
+- cc-by-4-0
+multilinguality:
+- multilingual
+pretty_name: KanHope
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- multi-label-classification
+- text-classification-other-Hope Speech Detection
+---
+
+# Dataset Card for KanHope
+
+## 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)
+
+## Dataset Description
+
+- **Homepage:** https://zenodo.org/record/4904729 
+- **Repository:** [KanHope](https://github.com/adeepH/KanHope)
+- **Paper:** [Hope speech detection in Under-resourced Kannada langauge](https://arxiv.org/abs/2108.04616)
+- **Leaderboard:** [N/A]
+- **Point of Contact:** [Adeep Hande](adeeph18c@iiitt.ac.in)
+
+### Dataset Summary
+
+KanHope dataset is a code-mixed Kannada-English dataset for hope speech detection. All texts are scraped from the comments section of YouTube. The dataset consists of 6,176 user-generated comments in code mixed Kannada scraped from YouTube and manually annotated as bearing hope speech or Not-hope speech. 
+
+### Supported Tasks and Leaderboards
+
+This task aims to detect Hope speech content of the code-mixed dataset of comments/posts in Dravidian Languages ( Kannada-English) collected from social media. The comment/post may contain more than one sentence, but the average sentence length of the corpora is 1. Each comment/post is annotated at the comment/post level. This dataset also has class imbalance problems depicting real-world scenarios.
+
+### Languages
+
+Code-mixed text in Dravidian languages (Kannada-English).
+
+
+## Dataset Structure
+
+### Data Instances
+
+An example from the Kannada dataset looks as follows:
+
+| text   | label |
+| :------ | :----- |
+| ���������  ��ͭ� heartly heltidini... plz avrigella namma nimmellara supprt beku          | 0 (Non_hope speech) |
+| Next song gu kuda alru andre evaga yar comment  madidera alla alrru like madi share madi nam industry na next level ge togond hogaona.      | 1 (Hope Speech) |
+
+
+### Data Fields
+
+Kannada
+- `text`: Kannada-English code mixed comment.
+- `label`: integer from either of 0 or 1 that corresponds to these values: "Non_hope Speech", "Hope Speech"
+
+### Data Splits
+
+|              | Tain   | Valid | Test |
+| -----        | ------: | -----: | ----:|
+| Kannada    |  4941  | 618  | 617 |
+
+## Dataset Creation
+
+### Curation Rationale
+
+Numerous methods have been developed to monitor the spread of negativity in modern years by eliminating vulgar, offensive, and fierce comments from social media platforms. However, there are relatively lesser amounts of study that converges on embracing positivity, reinforcing supportive and reassuring content in online forums.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[Needs More Information]
+
+#### Who are the source language producers?
+
+Youtube users
+
+### Annotations
+
+#### Annotation process
+
+[Needs More Information]
+
+#### Who are the annotators?
+
+[Needs More Information]
+
+
+### Personal and Sensitive Information
+
+[Needs More Information]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[Needs More Information]
+
+### Discussion of Biases
+
+[Needs More Information]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+[Needs More Information]
+
+
+### Licensing Information
+
+[Needs More Information]
+
+
+### Citation Information
+```
+@misc{hande2021hope,
+      title={Hope Speech detection in under-resourced Kannada language}, 
+      author={Adeep Hande and Ruba Priyadharshini and Anbukkarasi Sampath and Kingston Pal Thamburaj and Prabakaran Chandran and Bharathi Raja Chakravarthi},
+      year={2021},
+      eprint={2108.04616},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+### Contributions
+
+Thanks to [@adeepH](https://github.com/adeepH) for adding this dataset.

dataset_infos.json

@@ -0,0 +1 @@
+{"default": {"description": "Numerous methods have been developed to monitor the spread of negativity in modern years by\neliminating vulgar, offensive, and fierce comments from social media platforms. However, there are relatively\nlesser amounts of study that converges on embracing positivity, reinforcing supportive and reassuring content in online forums.\nConsequently, we propose creating an English Kannada Hope speech dataset, KanHope and comparing several experiments to benchmark the dataset.\nThe dataset consists of 6,176 user generated comments in code mixed Kannada scraped from YouTube and manually annotated as bearing hope\nspeech or Not-hope speech.\nThis dataset was prepared for hope-speech text classification benchmark on code-mixed Kannada, an under-resourced language.\n", "citation": "@misc{hande2021hope,\n      title={Hope Speech detection in under-resourced Kannada language},\n      author={Adeep Hande and Ruba Priyadharshini and Anbukkarasi Sampath and Kingston Pal Thamburaj and Prabakaran Chandran and Bharathi Raja Chakravarthi},\n      year={2021},\n      eprint={2108.04616},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n", "homepage": "https://github.com/adeepH/kan_hope", "license": "Creative Commons Attribution 4.0 International Licence", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["Not-Hope", "Hope"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "kan_hope", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 494898, "num_examples": 4940, "dataset_name": "kan_hope"}, "test": {"name": "test", "num_bytes": 65722, "num_examples": 618, "dataset_name": "kan_hope"}}, "download_checksums": {"https://raw.githubusercontent.com/adeepH/kan_hope/main/dataset/KanHope_train.csv": {"num_bytes": 502381, "checksum": "1dbf257728dc2788a35c63396f9827e4f984904ae948d83bbacc49f68632b0f7"}, "https://raw.githubusercontent.com/adeepH/kan_hope/main/dataset/KanHope_test.csv": {"num_bytes": 66591, "checksum": "4d11cab46f93dfbf552712e8a7e6089f8482941e9f31b5259507d9b673ba6872"}}, "download_size": 568972, "post_processing_size": null, "dataset_size": 560620, "size_in_bytes": 1129592}}

dummy/0.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:49e682ebb4c73f4dea56f190e0f86d8fe6b059ab518aaa43d992b8fdbda78916
+size 1348

kan_hope.py

@@ -0,0 +1,89 @@
+# 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.
+"""Kannada Hope Speech Dataset"""
+
+
+import csv
+
+import datasets
+
+
+_CITATION = """\
+@misc{hande2021hope,
+      title={Hope Speech detection in under-resourced Kannada language},
+      author={Adeep Hande and Ruba Priyadharshini and Anbukkarasi Sampath and Kingston Pal Thamburaj and Prabakaran Chandran and Bharathi Raja Chakravarthi},
+      year={2021},
+      eprint={2108.04616},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+"""
+
+_DESCRIPTION = """\
+Numerous methods have been developed to monitor the spread of negativity in modern years by
+eliminating vulgar, offensive, and fierce comments from social media platforms. However, there are relatively
+lesser amounts of study that converges on embracing positivity, reinforcing supportive and reassuring content in online forums.
+Consequently, we propose creating an English Kannada Hope speech dataset, KanHope and comparing several experiments to benchmark the dataset.
+The dataset consists of 6,176 user generated comments in code mixed Kannada scraped from YouTube and manually annotated as bearing hope
+speech or Not-hope speech.
+This dataset was prepared for hope-speech text classification benchmark on code-mixed Kannada, an under-resourced language.
+"""
+
+_HOMEPAGE = "https://github.com/adeepH/kan_hope"
+
+_LICENSE = "Creative Commons Attribution 4.0 International Licence"
+
+_TRAIN_DOWNLOAD_URL = "https://raw.githubusercontent.com/adeepH/kan_hope/main/dataset/KanHope_train.csv"
+_TEST_DOWNLOAD_URL = "https://raw.githubusercontent.com/adeepH/kan_hope/main/dataset/KanHope_test.csv"
+
+
+class kan_hope(datasets.GeneratorBasedBuilder):
+    """Code-Mixed Kannada Hope Speech Dataset"""
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "text": datasets.Value("string"),
+                "label": datasets.features.ClassLabel(names=["Not-Hope", "Hope"]),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators"""
+
+        train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
+        test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Generate Kannada Hope Speech examples."""
+
+        with open(filepath, encoding="utf-8") as csv_file:
+            reader = csv.reader(csv_file, quotechar='"', delimiter="\t", quoting=csv.QUOTE_ALL, skipinitialspace=True)
+            next(reader, None)
+            for id_, row in enumerate(reader):
+                dummy, text, label = row
+                yield id_, {"text": text, "label": label}