Fine-Grained Tasks: sentiment-classification
Languages: English
Multilinguality: monolingual
Size Categories: 1K<n<10K
Language Creators: found
Annotations Creators: crowdsourced
Source Datasets: original
Licenses: unknown
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Cannot get the split names for the dataset.
Error code:   SplitsNamesError
Exception:    ValueError
Message:      Invalid version '0.95'. Format should be x.y.z with {x,y,z} being digits.
Traceback:    Traceback (most recent call last):
                File "/src/workers/splits/src/splits/", line 127, in compute_splits_response
                  split_full_names = get_dataset_split_full_names(dataset=dataset, use_auth_token=use_auth_token)
                File "/src/workers/splits/src/splits/", line 57, in get_dataset_split_full_names
                  for config in sorted(get_dataset_config_names(path=dataset, use_auth_token=use_auth_token))
                File "/src/workers/splits/.venv/lib/python3.9/site-packages/datasets/", line 317, in get_dataset_config_names
                  builder_cls = import_main_class(dataset_module.module_path)
                File "/src/workers/splits/.venv/lib/python3.9/site-packages/datasets/", line 115, in import_main_class
                  module = importlib.import_module(module_path)
                File "/usr/local/lib/python3.9/importlib/", line 127, in import_module
                  return _bootstrap._gcd_import(name[level:], package, level)
                File "<frozen importlib._bootstrap>", line 1030, in _gcd_import
                File "<frozen importlib._bootstrap>", line 1007, in _find_and_load
                File "<frozen importlib._bootstrap>", line 986, in _find_and_load_unlocked
                File "<frozen importlib._bootstrap>", line 680, in _load_unlocked
                File "<frozen importlib._bootstrap_external>", line 850, in exec_module
                File "<frozen importlib._bootstrap>", line 228, in _call_with_frames_removed
                File "/tmp/modules-cache/datasets_modules/datasets/jpwahle--etpc/6d3d8c1b87335a911a17d430622673213f3cc089c699f60ceb96bf1019d55bd6/", line 58, in <module>
                  class ETPC(datasets.GeneratorBasedBuilder):
                File "/tmp/modules-cache/datasets_modules/datasets/jpwahle--etpc/6d3d8c1b87335a911a17d430622673213f3cc089c699f60ceb96bf1019d55bd6/", line 61, in ETPC
                  VERSION = datasets.Version("0.95")
                File "<string>", line 8, in __init__
                File "/src/workers/splits/.venv/lib/python3.9/site-packages/datasets/utils/", line 58, in __post_init__
                  self.major, self.minor, self.patch = _str_to_version_tuple(self.version_str)
                File "/src/workers/splits/.venv/lib/python3.9/site-packages/datasets/utils/", line 102, in _str_to_version_tuple
                  raise ValueError(f"Invalid version '{version_str}'. Format should be x.y.z with {{x,y,z}} being digits.")
              ValueError: Invalid version '0.95'. Format should be x.y.z with {x,y,z} being digits.

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Dataset Summary

We present the Extended Paraphrase Typology (EPT) and the Extended Typology Paraphrase Corpus (ETPC). The EPT typology addresses several practical limitations of existing paraphrase typologies: it is the first typology that copes with the non-paraphrase pairs in the paraphrase identification corpora and distinguishes between contextual and habitual paraphrase types. ETPC is the largest corpus to date annotated with atomic paraphrase types. It is the first corpus with detailed annotation of both the paraphrase and the non-paraphrase pairs and the first corpus annotated with paraphrase and negation. Both new resources contribute to better understanding the paraphrase phenomenon, and allow for studying the relationship between paraphrasing and negation. To the developers of Paraphrase Identification systems ETPC corpus offers better means for evaluation and error analysis. Furthermore, the EPT typology and ETPC corpus emphasize the relationship with other areas of NLP such as Semantic Similarity, Textual Entailment, Summarization and Simplification.

Supported Tasks and Leaderboards

  • text-classification


The text in the dataset is in English (en).

Dataset Structure

Data Fields

  • idx: Monotonically increasing index ID.
  • sentence1: Complete sentence expressing an opinion about a film.
  • sentence2: Complete sentence expressing an opinion about a film.
  • etpc_label: Whether the text-pair is a paraphrase, either "yes" (1) or "no" (0) according to etpc annotation schema.
  • mrpc_label: Whether the text-pair is a paraphrase, either "yes" (1) or "no" (0) according to mrpc annotation schema.
  • negation: Whether on sentence is a negation of another, either "yes" (1) or "no" (0).

Data Splits

train: 5801

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

Rotten Tomatoes reviewers.


Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information


Citation Information

    title = "{ETPC} - A Paraphrase Identification Corpus Annotated with Extended Paraphrase Typology and Negation",
    author = "Kovatchev, Venelin  and
      Mart{\'\i}, M. Ant{\`o}nia  and
      Salam{\'o}, Maria",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "",


Thanks to @jpwahle for adding this dataset.

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