Omkar2118/datasets huggingface dataset:
LAMBADA

The **LAMBADA** (LAnguage Modeling Broadened to Account for Discourse Aspects) benchmark is an open-ended cloze task which consists of about 10,000 passages from BooksCorpus where a missing target word is predicted in the last sentence of each passage. The missing word is constrained to always be the last word of the last sentence and there are no candidate words to choose from. Examples were filtered by humans to ensure they were possible to guess given the context, i.e., the sentences in the passage leading up to the last sentence. Examples were further filtered to ensure that missing words could not be guessed without the context, ensuring that models attempting the dataset would need to reason over the entire paragraph to answer questions.

Source: [Recent Advances in Natural Language Inference:A Survey of Benchmarks, Resources, and Approaches](https://arxiv.org/abs/1904.01172)
Image Source: [https://arxiv.org/pdf/1606.06031.pdf](https://arxiv.org/pdf/1606.06031.pdf)

Introducing paper: The LAMBADA dataset: Word prediction requiring a broad discourse context
WSC
Full name: Winograd Schema Challenge

The **Winograd Schema Challenge** was introduced both as an alternative to the Turing Test and as a test of a system’s ability to do commonsense reasoning. A Winograd schema is a pair of sentences differing in one or two words with a highly ambiguous pronoun, resolved differently in the two sentences, that appears to require commonsense knowledge to be resolved correctly. The examples were designed to be easily solvable by humans but difficult for machines, in principle requiring a deep understanding of the content of the text and the situation it describes.

The original Winograd Schema Challenge dataset consisted of 100 Winograd schemas constructed manually by AI experts. As of 2020 there are 285 examples available; however, the last 12 examples were only added recently. To ensure consistency with earlier models, several authors often prefer to report the performance on the first 273 examples only. These datasets are usually referred to as **WSC**285 and WSC273, respectively.

Source: [https://arxiv.org/pdf/2004.13831.pdf](https://arxiv.org/pdf/2004.13831.pdf)
Image Source: [https://arxiv.org/pdf/1907.11983.pdf](https://arxiv.org/pdf/1907.11983.pdf)

Introducing paper: The Winograd Schema Challenge