--- license: mit language: - ru annotations_creators: - crowdsourced language_creators: - expert-generated multilinguality: - monolingual pretty_name: CEDR M7 size_categories: - 1K ## Fields | Column | Meaning | |---|---| | `text` | Transcript of the utterance | | `labels` | Emotion labels, multi-label | | `source` | Where the text came from | | `label2ids` | Label indices in the source ordering | ## Usage ```python from datasets import load_dataset ds = load_dataset("Aniemore/cedr-m7") print(ds["train"][0]["text"], ds["train"][0]["labels"]) ``` ## Limitations `disgust` is effectively absent: 17 rows in `train`, 3 rows in `test`. Any per-class number for it is noise, and it drags macro averages down. The class distribution is otherwise heavily skewed toward `neutral`. ## Citation ```bibtex @misc{Aniemore, author = {Артем Аментес, Илья Лубенец, Никита Давидчук}, title = {Открытая библиотека искусственного интеллекта для анализа и выявления эмоциональных оттенков речи человека}, year = {2022}, publisher = {Hugging Face}, journal = {Hugging Face Hub}, howpublished = {\url{https://huggingface.com/aniemore/Aniemore}}, email = {hello@socialcode.ru} } ``` Please also cite the original CEDR corpus, [`sagteam/cedr_v1`](https://huggingface.co/datasets/sagteam/cedr_v1). ## License MIT.