init
Browse files- README.md +43 -1
- super_tweet_eval.py +4 -4
README.md
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@@ -76,9 +76,21 @@ The data fields are the same among all splits.
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- `labe_float`: a `float` feature.
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#### tempo_wic
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- `text_1`: a `string` feature.
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- `text_2`: a `string` feature.
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- `
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### Data Splits
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| tweet_topic | multi-label classification | 4585 / 573 / 1679 |
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## Citation Information
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- TweetTopic
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```
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@inproceedings{antypas-etal-2022-twitter,
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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- `labe_float`: a `float` feature.
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#### tempo_wic
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- `label_binary`: a `int` feature.
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- `id`: a `string` feature.
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- `word`: a `string` feature.
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- `text_1`: a `string` feature.
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- `text_tokenized_1`: a list of `string` feature.
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- `token_idx_1`: a `string` feature.
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- `text_start_1`: a `string` feature.
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- `text_end_1`: a `string` feature.
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- `date_1`: a `string` feature.
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- `text_2`: a `string` feature.
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- `text_tokenized_2`: a list of `string` feature.
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- `token_idx_2`: a `string` feature.
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- `text_start_2`: a `string` feature.
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- `text_end_2`: a `string` feature.
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- `date_2`: a `string` feature.
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### Data Splits
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| tweet_topic | multi-label classification | 4585 / 573 / 1679 |
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## Citation Information
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- TweetTopic
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```
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@inproceedings{antypas-etal-2022-twitter,
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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- Tweet Similarity
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```
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TBA
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```
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- TempoWiC
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```
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@inproceedings{loureiro-etal-2022-tempowic,
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title = "{T}empo{W}i{C}: An Evaluation Benchmark for Detecting Meaning Shift in Social Media",
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author = "Loureiro, Daniel and
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D{'}Souza, Aminette and
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Muhajab, Areej Nasser and
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White, Isabella A. and
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Wong, Gabriel and
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Espinosa-Anke, Luis and
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Neves, Leonardo and
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Barbieri, Francesco and
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Camacho-Collados, Jose",
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booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
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month = oct,
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year = "2022",
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address = "Gyeongju, Republic of Korea",
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publisher = "International Committee on Computational Linguistics",
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url = "https://aclanthology.org/2022.coling-1.296",
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pages = "3353--3359",
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abstract = "Language evolves over time, and word meaning changes accordingly. This is especially true in social media, since its dynamic nature leads to faster semantic shifts, making it challenging for NLP models to deal with new content and trends. However, the number of datasets and models that specifically address the dynamic nature of these social platforms is scarce. To bridge this gap, we present TempoWiC, a new benchmark especially aimed at accelerating research in social media-based meaning shift. Our results show that TempoWiC is a challenging benchmark, even for recently-released language models specialized in social media.",
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}
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```
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super_tweet_eval.py
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@@ -172,8 +172,8 @@ class SuperTweetEval(datasets.GeneratorBasedBuilder):
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description=_TEMPO_WIC_DESCRIPTION,
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citation=_TEMPO_WIC_CITATION,
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features=['label_binary', 'id', 'word',
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'text_1', '
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'text_2', '
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data_url="https://huggingface.co/datasets/cardiffnlp/super_tweet_eval/resolve/main/data/tempo_wic",
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)
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]
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features["label_float"] = datasets.Value("float32")
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if self.config.name == "tempo_wic":
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features["label_binary"] = datasets.Value("int32")
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features["
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features["
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return datasets.DatasetInfo(
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description=_SUPER_TWEET_EVAL_DESCRIPTION + "\n" + self.config.description,
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description=_TEMPO_WIC_DESCRIPTION,
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citation=_TEMPO_WIC_CITATION,
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features=['label_binary', 'id', 'word',
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'text_1', 'text_1_tokenized', 'token_idx_1', 'text_start_1', 'text_end_1', 'date_1',
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'text_2', 'text_2_tokenized', 'token_idx_2', 'text_start_2', 'text_end_2', 'date_2'],
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data_url="https://huggingface.co/datasets/cardiffnlp/super_tweet_eval/resolve/main/data/tempo_wic",
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)
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]
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features["label_float"] = datasets.Value("float32")
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if self.config.name == "tempo_wic":
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features["label_binary"] = datasets.Value("int32")
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features["text_1_tokenized"] = datasets.Sequence(datasets.Value("string"))
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features["text_2_tokenized"] = datasets.Sequence(datasets.Value("string"))
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return datasets.DatasetInfo(
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description=_SUPER_TWEET_EVAL_DESCRIPTION + "\n" + self.config.description,
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