Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+languages:
+- en
+licenses:
+- gpl-3-0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- sentiment-classification
+---
+
+# Dataset Card for [Dataset Name]
+
+## 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: [Home](https://github.com/sharmaroshan/Twitter-Sentiment-Analysis)
+- **Repository:[Repo](https://github.com/sharmaroshan/Twitter-Sentiment-Analysis/blob/master/train_tweet.csv)
+- **Paper:
+- **Leaderboard:**
+- **Point of Contact:[Darshan Gandhi](darshangandhi1151@gmail.com)
+
+### Dataset Summary
+
+The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets.
+
+Formally, given a training sample of tweets and labels, where label ‘1’ denotes the tweet is racist/sexist and label ‘0’ denotes the tweet is not racist/sexist, your objective is to predict the labels on the given test dataset.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+The tweets are primarily in English Language
+
+## Dataset Structure
+
+### Data Instances
+
+The dataset contains a label denoting is the tweet a hate speech or not
+
+```
+{'label': 0,  # not a hate speech
+ 'tweet': ' @user when a father is dysfunctional and is so selfish he drags his kids into his dysfunction.   #run'}
+```
+
+
+### Data Fields
+
+* label : 1 - it is a hate specch, 0 - not a hate speech 
+* tweet: content of the tweet as a string
+
+### Data Splits
+ 
+The data contains training data with :31962 entries
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+Crowdsourced from tweets of users 
+
+#### Who are the source language producers?
+
+Cwodsourced from twitter
+
+### Annotations
+
+#### Annotation process
+
+The data has been precprocessed and a model has been trained to assign the relevant label to the tweet 
+
+#### Who are the annotators?
+
+The data has been provided by Roshan Sharma 
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+With the help of this dataset, one can understand more about the human sentiments and also analye the situations when a particular person intends to make use of   hatred/racist comments 
+
+### Discussion of Biases
+
+The data could be cleaned up further for additional purposes such as applying a better feature extraction techniques
+
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+Roshan Sharma 
+
+### Licensing Information
+
+[Information](https://github.com/sharmaroshan/Twitter-Sentiment-Analysis/blob/master/LICENSE)
+
+### Citation Information
+
+[Citation](https://github.com/sharmaroshan/Twitter-Sentiment-Analysis/blob/master/CONTRIBUTING.md)

dataset_infos.json

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+{"default": {"description": "The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets.\n\nFormally, given a training sample of tweets and labels, where label \u20181\u2019 denotes the tweet is racist/sexist and label \u20180\u2019 denotes the tweet is not racist/sexist, your objective is to predict the labels on the given test dataset.\n", "citation": "@InProceedings{Z\nRoshan Sharma:dataset,\ntitle = {Sentimental Analysis of Tweets for Detecting Hate/Racist Speeches},\nauthors={Roshan Sharma},\nyear={2018}\n}\n", "homepage": "https://github.com/sharmaroshan/Twitter-Sentiment-Analysis", "license": "", "features": {"label": {"num_classes": 2, "names": ["no-hate-speech", "hate-speech"], "names_file": null, "id": null, "_type": "ClassLabel"}, "tweet": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "tweets_hate_speech_detection", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3191776, "num_examples": 31962, "dataset_name": "tweets_hate_speech_detection"}}, "download_checksums": {"https://raw.githubusercontent.com/sharmaroshan/Twitter-Sentiment-Analysis/master/train_tweet.csv": {"num_bytes": 3103165, "checksum": "4f1bfabc2908029734fb2acd34028a8dfd1b92098bddfe60b0f0836c964e26ab"}}, "download_size": 3103165, "post_processing_size": null, "dataset_size": 3191776, "size_in_bytes": 6294941}}

dummy/0.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:09a256280267e7670dfdf70cba0c10d94af707d6ef972a5844bcee12d3598ca8
+size 506

tweets_hate_speech_detection.py

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+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+"""Detecing which tweets showcase hate or racist remarks."""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+
+import datasets
+
+
+_DESCRIPTION = """\
+The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets.
+
+Formally, given a training sample of tweets and labels, where label ‘1’ denotes the tweet is racist/sexist and label ‘0’ denotes the tweet is not racist/sexist, your objective is to predict the labels on the given test dataset.
+"""
+
+_CITATION = """\
+@InProceedings{Z
+Roshan Sharma:dataset,
+title = {Sentimental Analysis of Tweets for Detecting Hate/Racist Speeches},
+authors={Roshan Sharma},
+year={2018}
+}
+"""
+
+_TRAIN_DOWNLOAD_URL = (
+    "https://raw.githubusercontent.com/sharmaroshan/Twitter-Sentiment-Analysis/master/train_tweet.csv"
+)
+
+
+class TweetsHateSpeechDetection(datasets.GeneratorBasedBuilder):
+    """Detecing which tweets showcase hate or racist remarks."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "label": datasets.ClassLabel(names=["no-hate-speech", "hate-speech"]),
+                    "tweet": datasets.Value("string"),
+                }
+            ),
+            homepage="https://github.com/sharmaroshan/Twitter-Sentiment-Analysis",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Generate Tweet examples."""
+        with open(filepath, encoding="utf-8") as csv_file:
+            csv_reader = csv.reader(
+                csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
+            )
+            next(csv_reader, None)
+            for id_, row in enumerate(csv_reader):
+                row = row[1:]
+                (label, tweet) = row
+
+                yield id_, {
+                    "label": int(label),
+                    "tweet": (tweet),
+                }