Update README.md

README.md

@@ -1,3 +1,106 @@
 ---
+language:
+- es
 license: apache-2.0
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- summarization
+pretty_name: NoticIA Human Validation
+dataset_info:
+  features:
+  - name: web_url
+    dtype: string
+  - name: web_headline
+    dtype: string
+  - name: summary
+    dtype: string
+  - name: web_text
+    dtype: string
+  - name: clean_web_text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 3966804
+    num_examples: 700
+  - name: validation
+    num_bytes: 352363
+    num_examples: 50
+  - name: test
+    num_bytes: 588932
+    num_examples: 100
+  download_size: 2908498
+  dataset_size: 4908099
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: test.jsonl
+tags:
+- summarization
+- clickbait
+- news
 ---
+
+<p align="center">
+    <img src="https://huggingface.co/datasets/Iker/NoticIA/resolve/main/assets/logo.png" style="height: 250px;">
+</p>
+
+<h3 align="center">"A Spanish dataset for Clickbait articles summarization"</h3>
+
+This repository contains the manual annotations from a second human in order to validate the test set of the NoticIA dataset. 
+
+The full NoticIA dataset is available here: [https://huggingface.co/datasets/Iker/NoticIA](https://huggingface.co/datasets/Iker/NoticIA)
+
+
+# Data explanation
+- **web_url** (int): The URL of the news article
+- **web_headline** (str):  The headline of the article, which is a Clickbait. 
+- **web_text** (int):  The body of the article. 
+- **clean_web_text** (str): The `web_text` has been downloaded from the web HTML and can contain undesired text not related to the news article. The `clean_web_text` has been cleaned using the OpenAI gpt-3.5-turbo-0125 model. We ask the model to remove any sentence unrelated to the article. 
+- **summary** (str):  The original summary in the NoticIA dataset.
+- **summary2** (str): The second summary written by another human to validate the quality of `summary`
+- 
+
+# Dataset Description
+- **Curated by:** [Iker García-Ferrero](https://ikergarcia1996.github.io/Iker-Garcia-Ferrero/), [Begoña Altura](https://www.linkedin.com/in/bego%C3%B1a-altuna-78014139)
+- **Language(s) (NLP):** Spanish
+- **License:** apache-2.0
+
+# Dataset Usage
+
+```Python
+from datasets import load_dataset
+from evaluate import load
+
+dataset = load_dataset("Iker/NoticIA_Human_Validation",split="test")
+rouge = load("rouge")
+results = rouge.compute(
+                predictions=[x["summary2"] for x in dataset],
+                references=[[x["summary"]] for x in dataset],
+                use_aggregator=True,
+            )
+print(results)
+```
+
+
+# Uses
+This dataset is intended to build models tailored for academic research that can extract information from large texts. The objective is to research whether current LLMs, given a question formulated as a Clickbait headline, can locate the answer within the article body and summarize the information in a few words. The dataset also aims to serve as a task to evaluate the performance of current LLMs in Spanish.
+
+# Out-of-Scope Use
+You cannot use this dataset to develop systems that directly harm the newspapers included in the dataset. This includes using the dataset to train profit-oriented LLMs capable of generating articles from a short text or headline, as well as developing profit-oriented bots that automatically summarize articles without the permission of the article's owner. Additionally, you are not permitted to train a system with this dataset that generates clickbait headlines.
+
+# Dataset Creation
+The dataset has been meticulously created by hand. We utilize two sources to compile Clickbait articles:
+- The Twitter user [@ahorrandoclick1](https://twitter.com/ahorrandoclick1), who reposts Clickbait articles along with a hand-crafted summary. Although we use their summaries as a reference, most of them have been rewritten (750 examples from this source).
+- The web demo [⚔️ClickbaitFighter⚔️](https://iker-clickbaitfighter.hf.space/), which operates a pre-trained model using an early iteration of our dataset. We collect all the model inputs/outputs and manually correct them (100 examples from this source).
+
+# Who are the annotators?
+The dataset was originaly by [Iker García-Ferrero](https://ikergarcia1996.github.io/Iker-Garcia-Ferrero/) and has been validated by [Begoña Altura](https://www.linkedin.com/in/bego%C3%B1a-altuna-78014139).
+The annotation took ~40 hours.
+
+