All datasets / SarcasmNet

SarcasmNet/sarcasm

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Permissive or share-alike

The declared licence (apache-2.0) is classified as permissive.

Declared licence: apache-2.0, from the tagEvidence: README.md at ba76a8c
Licence files: No licence file at the top level of the repositoryEvidence: file list at ba76a8c
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

Each cell links to what was checked: a file, a file list or a Hub record.

Tasks

token-classification

Citation text from the card

Khodak, M., Saunshi, N., & Vodrahalli, K. (2018). A Large Self-Annotated Corpus for Sarcasm. In LREC 2018 (pp. 1-6).
Rahman M O, Hossain M S, Junaid T S, et al. Predicting prices of stock market using gated recurrent units (GRUs) neural networks[J]. Int. J. Comput. Sci. Netw. Secur, 2019, 19(1): 213-222.
Yu Y, Si X, Hu C, et al. A review of recurrent neural networks: LSTM cells and network architectures[J]. Neural computation, 2019, 31(7): 1235-1270.
Gole, M., Nwadiugwu, W. P., & Miranskyy, A. (2023). On Sarcasm Detection with OpenAI GPT-based Models.
B. Sonare, J. H. Dewan, S. D. Thepade, V. Dadape, T. Gadge and A. Gavali, "Detecting Sarcasm in Reddit Comments: A Comparative Analysis," 2023 4th International Conference for Emerging Technology (INCET), Belgaum, India, 2023, pp. 1-6, doi: 10.1109/INCET57972.2023.10170613.

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dataset_licence({ "id": "SarcasmNet/sarcasm" })
signal: "permissive"
stated_licence: "apache-2.0"
sources: 0