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README.md
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# SuperTweetEval
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# Dataset Card for "
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## Table of Contents
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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- [Annotations](#annotations)
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- [Personal and Sensitive Information](#personal-and-sensitive-information)
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- [Considerations for Using the Data](#considerations-for-using-the-data)
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- [Social Impact of Dataset](#social-impact-of-dataset)
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- [Discussion of Biases](#discussion-of-biases)
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- [Other Known Limitations](#other-known-limitations)
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- [Additional Information](#additional-information)
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- [Dataset Curators](#dataset-curators)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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- [Contributions](#contributions)
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## Dataset Description
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- **Homepage:**
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- **Repository:**
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- **Paper:**
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- **Point of Contact:**
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- **Size of downloaded dataset files:** 58.36 MB
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- **Size of the generated dataset:** 249.57 MB
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- **Total amount of disk used:** 307.94 MB
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### Dataset Summary
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SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
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GLUE with a new set of more difficult language understanding tasks, improved
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resources, and a new public leaderboard.
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BoolQ (Boolean Questions, Clark et al., 2019a) is a QA task where each example consists of a short
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passage and a yes/no question about the passage. The questions are provided anonymously and
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unsolicited by users of the Google search engine, and afterwards paired with a paragraph from a
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Wikipedia article containing the answer. Following the original work, we evaluate with accuracy.
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### Supported Tasks and Leaderboards
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[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Languages
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## Dataset Structure
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### Data Fields
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- `date`: a `string` feature.
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#### tweet_ner7
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- `id`: a `string` feature.
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- `date`: a `string` feature.
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####
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- `label`: a classification label, with possible values including `entailment` (0), `not_entailment` (1).
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#### boolq
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- `question`: a `string` feature.
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- `passage`: a `string` feature.
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- `idx`: a `int32` feature.
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- `label`: a classification label, with possible values including `False` (0), `True` (1).
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#### cb
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- `premise`: a `string` feature.
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- `hypothesis`: a `string` feature.
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- `idx`: a `int32` feature.
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- `label`: a classification label, with possible values including `entailment` (0), `contradiction` (1), `neutral` (2).
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#### copa
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- `premise`: a `string` feature.
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- `choice1`: a `string` feature.
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- `choice2`: a `string` feature.
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- `question`: a `string` feature.
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### Data Splits
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# SuperTweetEval
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# Dataset Card for "super_tweet_eval"
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## Dataset Description
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- **Homepage:** TBA
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- **Repository:** TBA
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- **Paper:** TBA
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- **Point of Contact:** TBA
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### Dataset Summary
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TBA
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### Languages
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English
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## Dataset Structure
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### Data Fields
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- `date`: a `string` feature.
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#### tweet_ner7
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- `text_tokenized`: a list of `string` feature.
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- `label_sequence`: a list of `string` feature.
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- `id`: a `string` feature.
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- `date`: a `string` feature.
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#### tweet_qa
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- `text`: a `string` feature.
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- `label_str`: a `string` feature.
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- `pargraph`: a `string` feature.
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- `question`: a `string` feature.
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#### tweet_intimacy
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- `text`: a `string` feature.
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- `labe_float`: a `float` feature.
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#### tweet_similarity
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- `text_1`: a `string` feature.
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- `text_2`: a `string` feature.
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- `labe_float`: a `float` feature.
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### Data Splits
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get_stats.py
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import pandas as pd
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from datasets import load_dataset
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table = []
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task_description = {
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'tweet_intimacy': "regression on a single text",
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'tweet_ner': "sequence labeling",
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'tweet_qa': "generation",
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'tweet_sim': "regression on two texts",
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'tweet_topic': "multi-label classification"
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}
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for task in ['tweet_intimacy', 'tweet_ner', 'tweet_qa', 'tweet_sim', 'tweet_topic']:
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data = load_dataset("cardiffnlp/super_tweet_eval", task)
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tmp_table = {"task": task, "description": task_description[task]}
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tmp_table['number of instances'] = " / ".join([str(len(data[s])) for s in ['train', 'validation', 'test']])
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df = pd.DataFrame(table)
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print(df)
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