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README.md

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+# Swahili Text Dataset
+
+## Overview
+
+This dataset contains a comprehensive collection of Swahili text data, derived from the [AfriBERTa Corpus](https://huggingface.co/datasets/castorini/afriberta-corpus). It provides a rich resource for natural language processing tasks focused on the Swahili language.
+
+## Dataset Details
+
+- **Source**: [AfriBERTa Corpus](https://huggingface.co/datasets/castorini/afriberta-corpus) (Swahili subset)
+- **Language**: Swahili
+- **Size**: [Insert total number of samples here]
+- **Format**: Hugging Face Dataset
+
+## Content
+
+The dataset consists of two main columns:
+
+1. `id`: A unique identifier for each text entry
+2. `text`: The Swahili text content
+
+## Usage
+
+You can load this dataset using the Hugging Face `datasets` library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("[Your_HuggingFace_Username]/[Your_Dataset_Name]")
+```
+
+Replace `[Your_HuggingFace_Username]` and `[Your_Dataset_Name]` with the appropriate values for your uploaded dataset.
+
+## Data Fields
+
+- `id`: string
+- `text`: string
+
+## Data Splits
+
+This dataset combines training and test splits from the original AfriBERTa Corpus. The data has been shuffled with a fixed seed (42) to ensure reproducibility.
+
+## Dataset Creation
+
+This dataset was created by:
+
+1. Loading the Swahili subset of the AfriBERTa Corpus
+2. Concatenating the training and test splits
+3. Shuffling the combined dataset
+4. Extracting the 'id' and 'text' fields
+
+## Intended Uses
+
+This dataset can be used for various natural language processing tasks involving the Swahili language, such as:
+
+- Language modeling
+- Text classification
+- Named entity recognition
+- Machine translation (as a source or target language)
+- Sentiment analysis
+- And more...
+
+## Limitations
+
+- The dataset is limited to the content available in the original AfriBERTa Corpus.
+- It may not represent all dialects or variations of the Swahili language.
+- The quality and accuracy of the text content depend on the original data source.
+
+## Citation
+
+If you use this dataset, please cite the original AfriBERTa Corpus:
+
+```
+@inproceedings{ogueji-etal-2021-small,
+    title = "Small Data? No Problem! Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages",
+    author = "Ogueji, Kelechi  and
+      Zhu, Yuxin  and
+      Lin, Jimmy",
+    booktitle = "Proceedings of the 1st Workshop on Multilingual Representation Learning",
+    month = nov,
+    year = "2021",
+    address = "Punta Cana, Dominican Republic",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.mrl-1.11",
+    pages = "116--126",
+}
+```
+
+## Licensing Information
+
+This dataset is derived from the AfriBERTa Corpus. For usage terms and conditions, please refer to the [original dataset's license](https://huggingface.co/datasets/castorini/afriberta-corpus).
+
+## Contact
+
+If you have questions or comments about this specific version of the dataset, please open an issue in this repository or contact [ronleon76@gmail.com].
+
+---
+
+Dataset created and curated by [AdeptSchneider].
+Last updated: [09/10/2024]