license:mit size_categories:n<1K format:csv modality:tabular modality:text library:datasets library:pandas library:mlcroissant library:polars region:us infinite-dataset-hub synthetic
---
license: mit
tags:
- infinite-dataset-hub
- synthetic
---

# AnomalyContextDetector

tags: Anomaly Detection, Contextualization, Detector

_Note: This is an AI-generated dataset so its content may be inaccurate or false_

**Dataset Description:**

The 'AnomalyContextDetector' dataset is designed to aid machine learning practitioners in developing models for the identification and extraction of anomalies within texts, emphasizing the importance of contextualization. Each entry in the dataset comprises a text snippet, possibly from social media posts, news articles, or reports, which may contain anomalies or unusual patterns that could be of interest. Along with the text, each snippet is annotated with labels indicating whether the context surrounding the snippet helps in identifying the anomaly (context-aware) or not (context-unaware). The labels are binary, with '1' representing context-aware and '0' representing context-unaware. This dataset is beneficial for training models to understand the significance of context in anomaly detection.

**CSV Content Preview:**

```
id,text,label
1,"The sudden drop in sales figures seems unusual considering the marketing campaign's success.",1
2,"In the report, it's noted that the temperature in the server room spiked to an abnormal level.",1
3,"There's a mention of a new type of virus affecting crops, which has not been previously identified.",1
4,"No significant changes were reported this quarter in the employee turnover rates.",0
5,"The CEO's statement was unexpectedly optimistic about the upcoming quarter, given recent losses.",1
```

This CSV preview illustrates five rows of data entries with an 'id' column for unique identification, a 'text' column containing the snippet, and a 'label' column indicating whether the context is helpful for anomaly detection ('1') or not ('0'). Each entry could be a potential use case for a model aimed at understanding and extracting anomalies with respect to their surrounding context.

**Source of the data:**

The dataset was generated using the [Infinite Dataset Hub](https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub) and microsoft/Phi-3-mini-4k-instruct using the query 'contextualization identification and extraction':

- **Dataset Generation Page**: https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub?q=contextualization+identification+and+extraction&dataset=AnomalyContextDetector&tags=Anomaly+Detection,+Contextualization,+Detector
- **Model**: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct
- **More Datasets**: https://huggingface.co/datasets?other=infinite-dataset-hub
