Omkar2118/datasets huggingface dataset:
SILD
Full name: Survey Item Linking Dataset

This dataset contains a collection of texts from publications from a broad range of social science domains (e.g., economics, politics, psychology, etc.). The texts are annotated with labels for [Survey Item Linking (SIL)](https://github.com/e-tornike/SIL), an [Entity Linking (EL)](https://en.wikipedia.org/wiki/Entity_linking) task. SIL is divided into two sub-tasks: Mention Detection (MD), a binary text classification task, and Entity Disambiguation (ED), a sentence similarity task. Sentences that mention survey items are labeled with the IDs of entities from a knowledge base ([GSIM](https://github.com/e-tornike/SIL/tree/main/data/gsim)). SILD contains 20,454 sentences in English and German from 100 publications.
indirect-requests

IndirectRequests is an LLM-generated dataset of user utterances in a task-oriented dialogue setting where the user does not directly specify their preferred slot value.

IndirectRequests was generated by crowdsourcing human labels over a dataset generated using a combination of GPT-3.5 (turbo) and GPT-4. Each utterance is labelled along two dimensions:

World Understanding (the degree of world understanding it takes to understand the utterance)
Unambiguity (whether or not the generated utterance unambiguously entails a single target slot value among a set of candidate possible values).

Introducing paper: Making Task-Oriented Dialogue Datasets More Natural by Synthetically Generating Indirect User Requests
FGSCM-52

The development of the remote sensing fine-grained ship classification field necessitates large-scale realistic fine-grained ship datasets. The FGSC-23  and FGSCR-42 datasets have been pivotal for practical applications, yet they exhibit limitations when assessing advanced classification methods. Specifically, FGSC-23 categorizes data into relatively broad classes, such as lumping all auxiliary ships into one category, which does not align with the granularity required in real-world scenarios. Additionally, the dataset's limited number of categories fails to satisfy the diverse needs of practical applications. FGSCR-42, while providing finer classifications within some broader categories, still lacks comprehensive subcategory coverage. Moreover, the dataset suffers from an imbalance in sample distribution, with six categories containing fewer than ten samples each, which can hinder effective model training.

To overcome these limitations, we introduce the FGSCM-52 dataset, an enhanced version of FGSCR-42, designed to offer a more extensive collection of fine-grained remote sensing ship images. FGSCM-52 includes an additional ten categories and increases the number of samples across existing categories, thereby making it serve as a better testbed for fine-grained ship classification.The dataset features image sizes ranging from 50$\times$50 to 1,600$\times$1,700 pixels. Furthermore, FGSCM-52 is equipped with well-defined hierarchical labels, which facilitate a more nuanced understanding of ship classification.

Introducing paper: Efficient Prompt Tuning of Large Vision-Language Model for Fine-Grained Ship Classification