Update README.md

README.md

@@ -53,43 +53,64 @@ tags:
 size_categories:
 - 100K<n<1M
 ---
+# Naijaweb Dataset
 
-🇳🇬 Naijaweb
+**Naijaweb** is a dataset that contains over 270,000+ documents, totaling approximately **230 million GPT-2 tokens**. The data was web scraped from web pages popular among Nigerians, providing a rich resource for modeling Nigerian linguistic and cultural contexts.
 
-Naijaweb is a dataset consist about 270,000+ documents (230 Million GPT2 tokens), webscraped from pages interested in by Nigerians.
+## Dataset Summary
 
-Data Collection
-The data was colected by extracting 1,795,908 unique posts from 19 sections on Nairaland.com and about 1,289,195 outbond links were extracted from the posts.
-The web pages were then extracted using Trafilatura.
+| Features       | Data Types  |
+|----------------|-------------|
+| Unnamed: 0     | int64       |
+| text           | string      |
+| id             | string      |
+| link           | string      |
+| token_count    | int64       |
+| section        | string      |
+| domain         | string      |
+| score          | float64     |
+| int_score      | int64       |
+| language       | string      |
+| language_probability | float64 |
 
-Data cleaning
-The data was then cleaned using datatrove, same library that was used to clean the recently released and high performing fineweb edu dataset.
-So itv isn't far fetched to say this dataset is of the same quality as the fineweb dataset
-Dataset cleaning procedure:
+## Data Collection
 
-#### Flowchart of how it was  cleaned
+The dataset was collected from **Nairaland.com**, extracting **1,795,908 unique posts** from 19 different sections of the site. Additionally, **1,289,195 outbound links** were extracted from these posts. The content of these web pages was extracted using **Trafilatura**, a popular library for web scraping and content extraction.
 
-An example of a typical w of the dataset looks like
-```
-```
+## Data Cleaning
 
-### Data fields
+The cleaning process was conducted using **Datatrove**, the same library employed in cleaning the **FineWeb edu** dataset, which is known for its high quality. The data cleaning process involved multiple stages of deduplication, filtering, and normalization to ensure the dataset's quality matches that of other high-performing datasets.
 
-## How to load the dataset 
+### Data Cleaning Procedure:
+- **Step 1:** Remove duplicate posts and links.
+- **Step 2:** Filter out non-Nigerian context posts based on domain analysis.
+- **Step 3:** Normalize textual content, removing HTML artifacts and irrelevant metadata.
+- **Step 4:** Language detection and correction based on predicted language probabilities.
 
-## Social Impact of Dataset
-With the release of this dataset we aim to make model training more accessible to the machine learning community at large.
+## Example Entry
 
-While multiple open-weights models with strong performance have been publicly released in the past, more often than not these releases are not accompanied by the corresponding training dataset. This is unfortunate as the dataset specificities and characteristics have been demonstrated to have a very large impact and role in the performances of the models. As the creation of a high quality training dataset is a fundamental requirement to training an LLM capable of excelling at downstream tasks, with 🍷 FineWeb we (a) not only make the dataset creation process more transparent, by sharing our entire processing setup including the codebase used, we also (b) help alleviate the costs of dataset curation, both in time and in compute, for model creators by publicly releasing our dataset with the community.
+Each data point contains the following fields:
+- `Unnamed: 0`: an index column
+- `text`: the main body of the post or web page
+- `id`: unique identifier for each document
+- `link`: the original URL of the source content
+- `token_count`: the number of tokens in the `text` field
+- `section`: the Nairaland section where the post was found
+- `domain`: the domain of the outbound link
+- `score`: a float representing the content's relevance or quality
+- `int_score`: an integer representation of `score`
+- `language`: detected language of the text (e.g., `en`, `yo`, `ha`, `ig`)
+- `language_probability`: the confidence score of the language detection algorithm
 
+## Data Splits
 
-### Discussion of Biases
-Efforts were made to minimize the amount of NSFW and toxic content present in the dataset by employing filtering on the URL level. However, there are still a significant number of documents present in the final dataset that could be considered toxic or contain harmful content. As 🍷 FineWeb was sourced from the web as a whole, any harmful biases typically present in it may be reproduced on our dataset.
+- **Training Split:** 270,137 examples (620MB in size)
 
-We deliberately avoided using machine learning filtering methods that define text quality based on the similarity to a “gold” source such as wikipedia or toxicity classifiers as these methods have been known to disproportionately remove content in specific dialects and overclassify as toxic text related to specific social identities, respectively.
+## How to Load the Dataset
 
+To load the dataset using Hugging Face's `datasets` library:
 
-### Sections of the datasets
+```python
+from datasets import load_dataset
 
-
-### Citation
+dataset = load_dataset("path_to_naijaweb_dataset", split='train')