| dataset_info: | |
| features: | |
| - name: instruction | |
| dtype: string | |
| - name: input | |
| dtype: string | |
| - name: output | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 82110879 | |
| num_examples: 234040 | |
| - name: validation | |
| num_bytes: 1704864 | |
| num_examples: 4712 | |
| - name: test | |
| num_bytes: 4574111 | |
| num_examples: 13269 | |
| download_size: 30443057 | |
| dataset_size: 88389854 | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| - split: validation | |
| path: data/validation-* | |
| - split: test | |
| path: data/test-* | |
| ### Citation Information | |
| ``` | |
| Liu, Z., Yang, K., Xie, Q., Zhang, T., & Ananiadou, S. (2024, August). Emollms: A series of emotional large language models and annotation tools | |
| for comprehensive affective analysis. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 5487-5496). | |
| ``` |