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README.md

@@ -1,987 +0,0 @@
----
-annotations_creators:
-- found
-language_creators:
-- found
-language:
-- en
-license:
-- unknown
-multilinguality:
-- monolingual
-size_categories:
-- 100K<n<1M
-- 10K<n<100K
-- 1K<n<10K
-- n<1K
-source_datasets:
-- extended|other-tweet-datasets
-task_categories:
-- text-classification
-task_ids:
-- intent-classification
-- multi-class-classification
-- sentiment-classification
-paperswithcode_id: tweeteval
-pretty_name: TweetEval
-train-eval-index:
-- config: emotion
-  task: text-classification
-  task_id: multi_class_classification
-  splits:
-    train_split: train
-    eval_split: test
-  col_mapping:
-    text: text
-    label: target
-  metrics:
-  - type: accuracy
-    name: Accuracy
-  - type: f1
-    name: F1 macro
-    args:
-      average: macro
-  - type: f1
-    name: F1 micro
-    args:
-      average: micro
-  - type: f1
-    name: F1 weighted
-    args:
-      average: weighted
-  - type: precision
-    name: Precision macro
-    args:
-      average: macro
-  - type: precision
-    name: Precision micro
-    args:
-      average: micro
-  - type: precision
-    name: Precision weighted
-    args:
-      average: weighted
-  - type: recall
-    name: Recall macro
-    args:
-      average: macro
-  - type: recall
-    name: Recall micro
-    args:
-      average: micro
-  - type: recall
-    name: Recall weighted
-    args:
-      average: weighted
-- config: hate
-  task: text-classification
-  task_id: binary_classification
-  splits:
-    train_split: train
-    eval_split: test
-  col_mapping:
-    text: text
-    label: target
-  metrics:
-  - type: accuracy
-    name: Accuracy
-  - type: f1
-    name: F1 binary
-    args:
-      average: binary
-  - type: precision
-    name: Precision macro
-    args:
-      average: macro
-  - type: precision
-    name: Precision micro
-    args:
-      average: micro
-  - type: precision
-    name: Precision weighted
-    args:
-      average: weighted
-  - type: recall
-    name: Recall macro
-    args:
-      average: macro
-  - type: recall
-    name: Recall micro
-    args:
-      average: micro
-  - type: recall
-    name: Recall weighted
-    args:
-      average: weighted
-- config: irony
-  task: text-classification
-  task_id: binary_classification
-  splits:
-    train_split: train
-    eval_split: test
-  col_mapping:
-    text: text
-    label: target
-  metrics:
-  - type: accuracy
-    name: Accuracy
-  - type: f1
-    name: F1 binary
-    args:
-      average: binary
-  - type: precision
-    name: Precision macro
-    args:
-      average: macro
-  - type: precision
-    name: Precision micro
-    args:
-      average: micro
-  - type: precision
-    name: Precision weighted
-    args:
-      average: weighted
-  - type: recall
-    name: Recall macro
-    args:
-      average: macro
-  - type: recall
-    name: Recall micro
-    args:
-      average: micro
-  - type: recall
-    name: Recall weighted
-    args:
-      average: weighted
-- config: offensive
-  task: text-classification
-  task_id: binary_classification
-  splits:
-    train_split: train
-    eval_split: test
-  col_mapping:
-    text: text
-    label: target
-  metrics:
-  - type: accuracy
-    name: Accuracy
-  - type: f1
-    name: F1 binary
-    args:
-      average: binary
-  - type: precision
-    name: Precision macro
-    args:
-      average: macro
-  - type: precision
-    name: Precision micro
-    args:
-      average: micro
-  - type: precision
-    name: Precision weighted
-    args:
-      average: weighted
-  - type: recall
-    name: Recall macro
-    args:
-      average: macro
-  - type: recall
-    name: Recall micro
-    args:
-      average: micro
-  - type: recall
-    name: Recall weighted
-    args:
-      average: weighted
-- config: sentiment
-  task: text-classification
-  task_id: multi_class_classification
-  splits:
-    train_split: train
-    eval_split: test
-  col_mapping:
-    text: text
-    label: target
-  metrics:
-  - type: accuracy
-    name: Accuracy
-  - type: f1
-    name: F1 macro
-    args:
-      average: macro
-  - type: f1
-    name: F1 micro
-    args:
-      average: micro
-  - type: f1
-    name: F1 weighted
-    args:
-      average: weighted
-  - type: precision
-    name: Precision macro
-    args:
-      average: macro
-  - type: precision
-    name: Precision micro
-    args:
-      average: micro
-  - type: precision
-    name: Precision weighted
-    args:
-      average: weighted
-  - type: recall
-    name: Recall macro
-    args:
-      average: macro
-  - type: recall
-    name: Recall micro
-    args:
-      average: micro
-  - type: recall
-    name: Recall weighted
-    args:
-      average: weighted
-configs:
-- emoji
-- emotion
-- hate
-- irony
-- offensive
-- sentiment
-- stance_abortion
-- stance_atheism
-- stance_climate
-- stance_feminist
-- stance_hillary
-dataset_info:
-- config_name: emoji
-  features:
-  - name: text
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          0: ❤
-          1: 😍
-          2: 😂
-          3: 💕
-          4: 🔥
-          5: 😊
-          6: 😎
-          7: ✨
-          8: 💙
-          9: 😘
-          10: 📷
-          11: 🇺🇸
-          12: ☀
-          13: 💜
-          14: 😉
-          15: 💯
-          16: 😁
-          17: 🎄
-          18: 📸
-          19: 😜
-  splits:
-  - name: train
-    num_bytes: 3803187
-    num_examples: 45000
-  - name: test
-    num_bytes: 4255921
-    num_examples: 50000
-  - name: validation
-    num_bytes: 396083
-    num_examples: 5000
-  download_size: 7628721
-  dataset_size: 8455191
-- config_name: emotion
-  features:
-  - name: text
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          0: anger
-          1: joy
-          2: optimism
-          3: sadness
-  splits:
-  - name: train
-    num_bytes: 338875
-    num_examples: 3257
-  - name: test
-    num_bytes: 146649
-    num_examples: 1421
-  - name: validation
-    num_bytes: 38277
-    num_examples: 374
-  download_size: 483813
-  dataset_size: 523801
-- config_name: hate
-  features:
-  - name: text
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          0: non-hate
-          1: hate
-  splits:
-  - name: train
-    num_bytes: 1223654
-    num_examples: 9000
-  - name: test
-    num_bytes: 428938
-    num_examples: 2970
-  - name: validation
-    num_bytes: 154148
-    num_examples: 1000
-  download_size: 1703208
-  dataset_size: 1806740
-- config_name: irony
-  features:
-  - name: text
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          0: non_irony
-          1: irony
-  splits:
-  - name: train
-    num_bytes: 259191
-    num_examples: 2862
-  - name: test
-    num_bytes: 75901
-    num_examples: 784
-  - name: validation
-    num_bytes: 86021
-    num_examples: 955
-  download_size: 385613
-  dataset_size: 421113
-- config_name: offensive
-  features:
-  - name: text
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          0: non-offensive
-          1: offensive
-  splits:
-  - name: train
-    num_bytes: 1648069
-    num_examples: 11916
-  - name: test
-    num_bytes: 135477
-    num_examples: 860
-  - name: validation
-    num_bytes: 192421
-    num_examples: 1324
-  download_size: 1863383
-  dataset_size: 1975967
-- config_name: sentiment
-  features:
-  - name: text
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          0: negative
-          1: neutral
-          2: positive
-  splits:
-  - name: train
-    num_bytes: 5425142
-    num_examples: 45615
-  - name: test
-    num_bytes: 1279548
-    num_examples: 12284
-  - name: validation
-    num_bytes: 239088
-    num_examples: 2000
-  download_size: 6465841
-  dataset_size: 6943778
-- config_name: stance_abortion
-  features:
-  - name: text
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          0: none
-          1: against
-          2: favor
-  splits:
-  - name: train
-    num_bytes: 68698
-    num_examples: 587
-  - name: test
-    num_bytes: 33175
-    num_examples: 280
-  - name: validation
-    num_bytes: 7661
-    num_examples: 66
-  download_size: 102062
-  dataset_size: 109534
-- config_name: stance_atheism
-  features:
-  - name: text
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          0: none
-          1: against
-          2: favor
-  splits:
-  - name: train
-    num_bytes: 54779
-    num_examples: 461
-  - name: test
-    num_bytes: 25720
-    num_examples: 220
-  - name: validation
-    num_bytes: 6324
-    num_examples: 52
-  download_size: 80947
-  dataset_size: 86823
-- config_name: stance_climate
-  features:
-  - name: text
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          0: none
-          1: against
-          2: favor
-  splits:
-  - name: train
-    num_bytes: 40253
-    num_examples: 355
-  - name: test
-    num_bytes: 19929
-    num_examples: 169
-  - name: validation
-    num_bytes: 4805
-    num_examples: 40
-  download_size: 60463
-  dataset_size: 64987
-- config_name: stance_feminist
-  features:
-  - name: text
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          0: none
-          1: against
-          2: favor
-  splits:
-  - name: train
-    num_bytes: 70513
-    num_examples: 597
-  - name: test
-    num_bytes: 33309
-    num_examples: 285
-  - name: validation
-    num_bytes: 8039
-    num_examples: 67
-  download_size: 104257
-  dataset_size: 111861
-- config_name: stance_hillary
-  features:
-  - name: text
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          0: none
-          1: against
-          2: favor
-  splits:
-  - name: train
-    num_bytes: 69600
-    num_examples: 620
-  - name: test
-    num_bytes: 34491
-    num_examples: 295
-  - name: validation
-    num_bytes: 7536
-    num_examples: 69
-  download_size: 103745
-  dataset_size: 111627
----
-
-# Dataset Card for tweet_eval
-
-## 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)
-  - [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:** [Needs More Information]
-- **Repository:** [GitHub](https://github.com/cardiffnlp/tweeteval)
-- **Paper:** [EMNLP Paper](https://arxiv.org/pdf/2010.12421.pdf)
-- **Leaderboard:** [GitHub Leaderboard](https://github.com/cardiffnlp/tweeteval)
-- **Point of Contact:** [Needs More Information]
-
-### Dataset Summary
-
-TweetEval consists of seven heterogenous tasks in Twitter, all framed as multi-class tweet classification. The tasks include - irony, hate, offensive, stance, emoji, emotion, and sentiment. All tasks have been unified into the same benchmark, with each dataset presented in the same format and with fixed training, validation and test splits.
-
-### Supported Tasks and Leaderboards
-
-- `text_classification`: The dataset can be trained using a SentenceClassification model from HuggingFace transformers.
-
-### Languages
-
-The text in the dataset is in English, as spoken by Twitter users.
-
-## Dataset Structure
-
-### Data Instances
-
-An instance from `emoji` config:
-
-```
-{'label': 12, 'text': 'Sunday afternoon walking through Venice in the sun with @user ️ ️ ️ @ Abbot Kinney, Venice'}
-```
-
-An instance from `emotion` config:
-
-```
-{'label': 2, 'text': "“Worry is a down payment on a problem you may never have'. \xa0Joyce Meyer.  #motivation #leadership #worry"}
-```
-
-An instance from `hate` config:
-
-```
-{'label': 0, 'text': '@user nice new signage. Are you not concerned by Beatlemania -style hysterical crowds crongregating on you…'}
-```
-
-An instance from `irony` config:
-
-```
-{'label': 1, 'text': 'seeing ppl walking w/ crutches makes me really excited for the next 3 weeks of my life'}
-```
-
-An instance from `offensive` config:
-
-```
-{'label': 0, 'text': '@user Bono... who cares. Soon people will understand that they gain nothing from following a phony celebrity. Become a Leader of your people instead or help and support your fellow countrymen.'}
-```
-
-An instance from `sentiment` config:
-
-```
-{'label': 2, 'text': '"QT @user In the original draft of the 7th book, Remus Lupin survived the Battle of Hogwarts. #HappyBirthdayRemusLupin"'}
-```
-
-An instance from `stance_abortion` config:
-
-```
-{'label': 1, 'text': 'we remind ourselves that love means to be willing to give until it hurts - Mother Teresa'}
-```
-
-An instance from `stance_atheism` config:
-
-```
-{'label': 1, 'text': '@user Bless Almighty God, Almighty Holy Spirit and the Messiah. #SemST'}
-```
-
-An instance from `stance_climate` config:
-
-```
-{'label': 0, 'text': 'Why Is The Pope Upset?  via @user #UnzippedTruth #PopeFrancis #SemST'}
-```
-
-An instance from `stance_feminist` config:
-
-```
-{'label': 1, 'text': "@user @user is the UK's answer to @user and @user  #GamerGate #SemST"}
-```
-
-An instance from `stance_hillary` config:
-
-```
-{'label': 1, 'text': "If a man demanded staff to get him an ice tea he'd be called a sexists elitist pig.. Oink oink #Hillary #SemST"}
-```
-
-### Data Fields
-For `emoji` config:
-
-- `text`: a `string` feature containing the tweet.
-
-- `label`: an `int` classification label with the following mapping:
-
-    `0`: ❤
-
-    `1`: 😍
-
-    `2`: 😂
-
-    `3`: 💕
-
-    `4`: 🔥
-
-    `5`: 😊
-
-    `6`: 😎
-
-    `7`: ✨
-
-    `8`: 💙
-
-    `9`: 😘
-
-    `10`: 📷
-
-    `11`: 🇺🇸
-
-    `12`: ☀
-
-    `13`: 💜
-
-    `14`: 😉
-
-    `15`: 💯
-
-    `16`: 😁
-
-    `17`: 🎄
-
-    `18`: 📸
-
-    `19`: 😜
-
-For `emotion` config:
-
-- `text`: a `string` feature containing the tweet.
-
-- `label`: an `int` classification label with the following mapping:
-
-    `0`: anger
-
-    `1`: joy
-
-    `2`: optimism
-
-    `3`: sadness
-
-For `hate` config:
-
-- `text`: a `string` feature containing the tweet.
-
-- `label`: an `int` classification label with the following mapping:
-
-    `0`: non-hate
-
-    `1`: hate
-
-For `irony` config:
-
-- `text`: a `string` feature containing the tweet.
-
-- `label`: an `int` classification label with the following mapping:
-
-    `0`: non_irony
-
-    `1`: irony
-
-For `offensive` config:
-
-- `text`: a `string` feature containing the tweet.
-
-- `label`: an `int` classification label with the following mapping:
-
-    `0`: non-offensive
-
-    `1`: offensive
-
-For `sentiment` config:
-
-- `text`: a `string` feature containing the tweet.
-
-- `label`: an `int` classification label with the following mapping:
-
-    `0`: negative
-
-    `1`: neutral
-
-    `2`: positive
-
-For `stance_abortion` config:
-
-- `text`: a `string` feature containing the tweet.
-
-- `label`: an `int` classification label with the following mapping:
-
-    `0`: none
-
-    `1`: against
-
-    `2`: favor
-
-For `stance_atheism` config:
-
-- `text`: a `string` feature containing the tweet.
-
-- `label`: an `int` classification label with the following mapping:
-
-    `0`: none
-
-    `1`: against
-
-    `2`: favor
-
-For `stance_climate` config:
-
-- `text`: a `string` feature containing the tweet.
-
-- `label`: an `int` classification label with the following mapping:
-
-    `0`: none
-
-    `1`: against
-
-    `2`: favor
-
-For `stance_feminist` config:
-
-- `text`: a `string` feature containing the tweet.
-
-- `label`: an `int` classification label with the following mapping:
-
-    `0`: none
-
-    `1`: against
-
-    `2`: favor
-
-For `stance_hillary` config:
-
-- `text`: a `string` feature containing the tweet.
-
-- `label`: an `int` classification label with the following mapping:
-
-    `0`: none
-
-    `1`: against
-
-    `2`: favor
-
-
-
-### Data Splits
-
-| name            | train | validation | test  |
-| --------------- | ----- | ---------- | ----- |
-| emoji           | 45000 | 5000       | 50000 |
-| emotion         | 3257  | 374        | 1421  |
-| hate            | 9000  | 1000       | 2970  |
-| irony           | 2862  | 955        | 784   |
-| offensive       | 11916 | 1324       | 860   |
-| sentiment       | 45615 | 2000       | 12284 |
-| stance_abortion | 587   | 66         | 280   |
-| stance_atheism  | 461   | 52         | 220   |
-| stance_climate  | 355   | 40         | 169   |
-| stance_feminist | 597   | 67         | 285   |
-| stance_hillary  | 620   | 69         | 295   |
-
-## Dataset Creation
-
-### Curation Rationale
-
-[Needs More Information]
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[Needs More Information]
-
-#### Who are the source language producers?
-
-[Needs More Information]
-
-### 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
-
-Francesco Barbieri, Jose Camacho-Collados, Luis Espiinosa-Anke and Leonardo Neves through Cardiff NLP.
-
-### Licensing Information
-
-This is not a single dataset, therefore each subset has its own license (the collection itself does not have additional restrictions).
-
-All of the datasets require complying with Twitter [Terms Of Service](https://twitter.com/tos) and Twitter API [Terms Of Service](https://developer.twitter.com/en/developer-terms/agreement-and-policy)
-
-Additionally the license are:
-- emoji: Undefined
-- emotion(EmoInt): Undefined
-- hate (HateEval): Need permission [here](http://hatespeech.di.unito.it/hateval.html)
-- irony: Undefined
-- Offensive: Undefined
-- Sentiment: [Creative Commons Attribution 3.0 Unported License](https://groups.google.com/g/semevaltweet/c/k5DDcvVb_Vo/m/zEOdECFyBQAJ)
-- Stance: Undefined
-
-
-### Citation Information
-
-```
-@inproceedings{barbieri2020tweeteval,
-title={{TweetEval:Unified Benchmark and Comparative Evaluation for Tweet Classification}},
-author={Barbieri, Francesco and Camacho-Collados, Jose and Espinosa-Anke, Luis and Neves, Leonardo},
-booktitle={Proceedings of Findings of EMNLP},
-year={2020}
-}
-```
-
-If you use any of the TweetEval datasets, please cite their original publications:
-
-#### Emotion Recognition:
-```
-@inproceedings{mohammad2018semeval,
-  title={Semeval-2018 task 1: Affect in tweets},
-  author={Mohammad, Saif and Bravo-Marquez, Felipe and Salameh, Mohammad and Kiritchenko, Svetlana},
-  booktitle={Proceedings of the 12th international workshop on semantic evaluation},
-  pages={1--17},
-  year={2018}
-}
-
-```
-#### Emoji Prediction:
-```
-@inproceedings{barbieri2018semeval,
-  title={Semeval 2018 task 2: Multilingual emoji prediction},
-  author={Barbieri, Francesco and Camacho-Collados, Jose and Ronzano, Francesco and Espinosa-Anke, Luis and
-    Ballesteros, Miguel and Basile, Valerio and Patti, Viviana and Saggion, Horacio},
-  booktitle={Proceedings of The 12th International Workshop on Semantic Evaluation},
-  pages={24--33},
-  year={2018}
-}
-```
-
-#### Irony Detection:
-```
-@inproceedings{van2018semeval,
-  title={Semeval-2018 task 3: Irony detection in english tweets},
-  author={Van Hee, Cynthia and Lefever, Els and Hoste, V{\'e}ronique},
-  booktitle={Proceedings of The 12th International Workshop on Semantic Evaluation},
-  pages={39--50},
-  year={2018}
-}
-```
-
-#### Hate Speech Detection:
-```
-@inproceedings{basile-etal-2019-semeval,
-    title = "{S}em{E}val-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in {T}witter",
-    author = "Basile, Valerio  and Bosco, Cristina  and Fersini, Elisabetta  and Nozza, Debora and Patti, Viviana and
-      Rangel Pardo, Francisco Manuel  and Rosso, Paolo  and Sanguinetti, Manuela",
-    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
-    year = "2019",
-    address = "Minneapolis, Minnesota, USA",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/S19-2007",
-    doi = "10.18653/v1/S19-2007",
-    pages = "54--63"
-}
-```
-#### Offensive Language Identification:
-```
-@inproceedings{zampieri2019semeval,
-  title={SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)},
-  author={Zampieri, Marcos and Malmasi, Shervin and Nakov, Preslav and Rosenthal, Sara and Farra, Noura and Kumar, Ritesh},
-  booktitle={Proceedings of the 13th International Workshop on Semantic Evaluation},
-  pages={75--86},
-  year={2019}
-}
-```
-
-#### Sentiment Analysis:
-```
-@inproceedings{rosenthal2017semeval,
-  title={SemEval-2017 task 4: Sentiment analysis in Twitter},
-  author={Rosenthal, Sara and Farra, Noura and Nakov, Preslav},
-  booktitle={Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017)},
-  pages={502--518},
-  year={2017}
-}
-```
-
-#### Stance Detection:
-```
-@inproceedings{mohammad2016semeval,
-  title={Semeval-2016 task 6: Detecting stance in tweets},
-  author={Mohammad, Saif and Kiritchenko, Svetlana and Sobhani, Parinaz and Zhu, Xiaodan and Cherry, Colin},
-  booktitle={Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)},
-  pages={31--41},
-  year={2016}
-}
-```
-
-### Contributions
-
-Thanks to [@gchhablani](https://github.com/gchhablani) and [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

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tweet_eval.py

@@ -1,249 +0,0 @@
-# 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.
-"""The Tweet Eval Datasets"""
-
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{barbieri2020tweeteval,
-  title={{TweetEval:Unified Benchmark and Comparative Evaluation for Tweet Classification}},
-  author={Barbieri, Francesco and Camacho-Collados, Jose and Espinosa-Anke, Luis and Neves, Leonardo},
-  booktitle={Proceedings of Findings of EMNLP},
-  year={2020}
-}
-"""
-
-_DESCRIPTION = """\
-TweetEval consists of seven heterogenous tasks in Twitter, all framed as multi-class tweet classification. All tasks have been unified into the same benchmark, with each dataset presented in the same format and with fixed training, validation and test splits.
-"""
-
-_HOMEPAGE = "https://github.com/cardiffnlp/tweeteval"
-
-_LICENSE = ""
-
-URL = "https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/"
-
-_URLs = {
-    "emoji": {
-        "train_text": URL + "emoji/train_text.txt",
-        "train_labels": URL + "emoji/train_labels.txt",
-        "test_text": URL + "emoji/test_text.txt",
-        "test_labels": URL + "emoji/test_labels.txt",
-        "val_text": URL + "emoji/val_text.txt",
-        "val_labels": URL + "emoji/val_labels.txt",
-    },
-    "emotion": {
-        "train_text": URL + "emotion/train_text.txt",
-        "train_labels": URL + "emotion/train_labels.txt",
-        "test_text": URL + "emotion/test_text.txt",
-        "test_labels": URL + "emotion/test_labels.txt",
-        "val_text": URL + "emotion/val_text.txt",
-        "val_labels": URL + "emotion/val_labels.txt",
-    },
-    "hate": {
-        "train_text": URL + "hate/train_text.txt",
-        "train_labels": URL + "hate/train_labels.txt",
-        "test_text": URL + "hate/test_text.txt",
-        "test_labels": URL + "hate/test_labels.txt",
-        "val_text": URL + "hate/val_text.txt",
-        "val_labels": URL + "hate/val_labels.txt",
-    },
-    "irony": {
-        "train_text": URL + "irony/train_text.txt",
-        "train_labels": URL + "irony/train_labels.txt",
-        "test_text": URL + "irony/test_text.txt",
-        "test_labels": URL + "irony/test_labels.txt",
-        "val_text": URL + "irony/val_text.txt",
-        "val_labels": URL + "irony/val_labels.txt",
-    },
-    "offensive": {
-        "train_text": URL + "offensive/train_text.txt",
-        "train_labels": URL + "offensive/train_labels.txt",
-        "test_text": URL + "offensive/test_text.txt",
-        "test_labels": URL + "offensive/test_labels.txt",
-        "val_text": URL + "offensive/val_text.txt",
-        "val_labels": URL + "offensive/val_labels.txt",
-    },
-    "sentiment": {
-        "train_text": URL + "sentiment/train_text.txt",
-        "train_labels": URL + "sentiment/train_labels.txt",
-        "test_text": URL + "sentiment/test_text.txt",
-        "test_labels": URL + "sentiment/test_labels.txt",
-        "val_text": URL + "sentiment/val_text.txt",
-        "val_labels": URL + "sentiment/val_labels.txt",
-    },
-    "stance": {
-        "abortion": {
-            "train_text": URL + "stance/abortion/train_text.txt",
-            "train_labels": URL + "stance/abortion/train_labels.txt",
-            "test_text": URL + "stance/abortion/test_text.txt",
-            "test_labels": URL + "stance/abortion/test_labels.txt",
-            "val_text": URL + "stance/abortion/val_text.txt",
-            "val_labels": URL + "stance/abortion/val_labels.txt",
-        },
-        "atheism": {
-            "train_text": URL + "stance/atheism/train_text.txt",
-            "train_labels": URL + "stance/atheism/train_labels.txt",
-            "test_text": URL + "stance/atheism/test_text.txt",
-            "test_labels": URL + "stance/atheism/test_labels.txt",
-            "val_text": URL + "stance/atheism/val_text.txt",
-            "val_labels": URL + "stance/atheism/val_labels.txt",
-        },
-        "climate": {
-            "train_text": URL + "stance/climate/train_text.txt",
-            "train_labels": URL + "stance/climate/train_labels.txt",
-            "test_text": URL + "stance/climate/test_text.txt",
-            "test_labels": URL + "stance/climate/test_labels.txt",
-            "val_text": URL + "stance/climate/val_text.txt",
-            "val_labels": URL + "stance/climate/val_labels.txt",
-        },
-        "feminist": {
-            "train_text": URL + "stance/feminist/train_text.txt",
-            "train_labels": URL + "stance/feminist/train_labels.txt",
-            "test_text": URL + "stance/feminist/test_text.txt",
-            "test_labels": URL + "stance/feminist/test_labels.txt",
-            "val_text": URL + "stance/feminist/val_text.txt",
-            "val_labels": URL + "stance/feminist/val_labels.txt",
-        },
-        "hillary": {
-            "train_text": URL + "stance/hillary/train_text.txt",
-            "train_labels": URL + "stance/hillary/train_labels.txt",
-            "test_text": URL + "stance/hillary/test_text.txt",
-            "test_labels": URL + "stance/hillary/test_labels.txt",
-            "val_text": URL + "stance/hillary/val_text.txt",
-            "val_labels": URL + "stance/hillary/val_labels.txt",
-        },
-    },
-}
-
-
-class TweetEvalConfig(datasets.BuilderConfig):
-    def __init__(self, *args, type=None, sub_type=None, **kwargs):
-        super().__init__(
-            *args,
-            name=f"{type}" if type != "stance" else f"{type}_{sub_type}",
-            **kwargs,
-        )
-        self.type = type
-        self.sub_type = sub_type
-
-
-class TweetEval(datasets.GeneratorBasedBuilder):
-    """TweetEval Dataset."""
-
-    BUILDER_CONFIGS = [
-        TweetEvalConfig(
-            type=key,
-            sub_type=None,
-            version=datasets.Version("1.1.0"),
-            description=f"This part of my dataset covers {key} part of TweetEval Dataset.",
-        )
-        for key in list(_URLs.keys())
-        if key != "stance"
-    ] + [
-        TweetEvalConfig(
-            type="stance",
-            sub_type=key,
-            version=datasets.Version("1.1.0"),
-            description=f"This part of my dataset covers stance_{key} part of TweetEval Dataset.",
-        )
-        for key in list(_URLs["stance"].keys())
-    ]
-
-    def _info(self):
-        if self.config.type == "stance":
-            names = ["none", "against", "favor"]
-        elif self.config.type == "sentiment":
-            names = ["negative", "neutral", "positive"]
-        elif self.config.type == "offensive":
-            names = ["non-offensive", "offensive"]
-        elif self.config.type == "irony":
-            names = ["non_irony", "irony"]
-        elif self.config.type == "hate":
-            names = ["non-hate", "hate"]
-        elif self.config.type == "emoji":
-            names = [
-                "❤",
-                "😍",
-                "😂",
-                "💕",
-                "🔥",
-                "😊",
-                "😎",
-                "✨",
-                "💙",
-                "😘",
-                "📷",
-                "🇺🇸",
-                "☀",
-                "💜",
-                "😉",
-                "💯",
-                "😁",
-                "🎄",
-                "📸",
-                "😜",
-            ]
-
-        else:
-            names = ["anger", "joy", "optimism", "sadness"]
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {"text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=names)}
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        if self.config.type != "stance":
-            my_urls = _URLs[self.config.type]
-        else:
-            my_urls = _URLs[self.config.type][self.config.sub_type]
-        data_dir = dl_manager.download_and_extract(my_urls)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"text_path": data_dir["train_text"], "labels_path": data_dir["train_labels"]},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"text_path": data_dir["test_text"], "labels_path": data_dir["test_labels"]},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"text_path": data_dir["val_text"], "labels_path": data_dir["val_labels"]},
-            ),
-        ]
-
-    def _generate_examples(self, text_path, labels_path):
-        """Yields examples."""
-
-        with open(text_path, encoding="utf-8") as f:
-            texts = f.readlines()
-        with open(labels_path, encoding="utf-8") as f:
-            labels = f.readlines()
-        for i, text in enumerate(texts):
-            yield i, {"text": text.strip(), "label": int(labels[i].strip())}