Update README.md

README.md

@@ -28,157 +28,168 @@ pretty_name: Pt-Corpus
 size_categories:
 - 1M<n<10M
 ---
-# Pt-Corpus
 
-Pt-Corpus is a concatenation of several portions of Brazilian Portuguese datasets found in the [Hub](https://huggingface.co/datasets?task_categories=task_categories:text-generation&language=language:pt&sort=trending). This dataset was used in the following study: [TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese](https://arxiv.org/abs/2401.16640).
+# Portuguese-Corpus
+
+## Table of Contents
+
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [Dataset](https://huggingface.co/datasets/nicholasKluge/Pt-Corpus)
+- **Repository:** [GitHub](https://github.com/Nkluge-correa/TeenyTinyLlama)
+- **Paper:** [TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese](https://arxiv.org/abs/2401.16640).
+- **Leaderboard:** None
+- **Point of Contact:** [nicholas@airespucrs.org](nicholas@airespucrs.org)
+
+### Dataset Summary
+
+Portuguese-Corpus is a concatenation of several portions of Brazilian Portuguese datasets found in the [Hub](https://huggingface.co/datasets?task_categories=task_categories:text-generation&language=language:pt&sort=trending).
 
 In a tokenized format, the dataset (uncompressed) weighs 50 GB and has approximately 4.1B tokens. This version does not have instructional content.
 
-The following datasets (_only training splits are a part of the corpus_) and respective licenses form Pt-Corpus:
+### Supported Tasks and Leaderboards
 
-- [Wikipedia](https://huggingface.co/datasets/graelo/wikipedia) (License: [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/))
+This dataset can be utilized for taks involving language modeling.
 
-**Citation Information**
+### Languages
 
-```latex
-@ONLINE{wikidump,
-    author = "Wikimedia Foundation",
-    title  = "Wikimedia Downloads",
-    url    = "https://dumps.wikimedia.org"
-}
-```
+Portuguese.
 
-- [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX) (License: [ODC-By](https://opendatacommons.org/licenses/by/1-0/), [cc0-1.0](https://huggingface.co/datasets/oscar-corpus/OSCAR-2301#licensing-information))
+## Dataset Structure
 
-**Citation Information**
+### Data Instances
 
-```latex
-@misc{nguyen2023culturax,
-      title={CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages}, 
-      author={Thuat Nguyen and Chien Van Nguyen and Viet Dac Lai and Hieu Man and Nghia Trung Ngo and Franck Dernoncourt and Ryan A. Rossi and Thien Huu Nguyen},
-      year={2023},
-      eprint={2309.09400},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
+The dataset consists of the following features:
+
+- **text:** a string of text in Portuguese.
+- **metadata:** the source where that string originated.
+
+### Data Fields
+
+```python
+{
+  "text": "A inteligência artificial (de sigla: IA; do inglês: artificial intelligence, de sigla: AI) é um campo de estudo multidisciplinar que abrange varias áreas do conhecimento.",
+  "metadata": "source: https://huggingface.co/datasets/graelo/wikipedia"
 }
 ```
 
-- [OSCAR](https://huggingface.co/datasets/eduagarcia/OSCAR-2301-pt_dedup) (License: [cc0-1.0](https://huggingface.co/datasets/oscar-corpus/OSCAR-2301#licensing-information))
+### Data Splits
 
-**Citation Information**
+Available splits are `train`.
 
-```latex
-@inproceedings{ortiz-suarez-etal-2020-monolingual,
-    title = "A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages",
-    author = "Ortiz Su{'a}rez, Pedro Javier  and
-      Romary, Laurent  and
-      Sagot, Benoit",
-    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
-    month = jul,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.acl-main.156",
-    pages = "1703--1714",
-    abstract = "We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures.",
-}
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("nicholasKluge/Pt-Corpus", split='train')
+
+# If you don't want to download the entire dataset, set streaming to `True`
+dataset = load_dataset("nicholasKluge/Pt-Corpus", split='train', streaming=True)
 
-@inproceedings{OrtizSuarezSagotRomary2019,
-  author    = {Pedro Javier {Ortiz Su{'a}rez} and Benoit Sagot and Laurent Romary},
-  title     = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures},
-  series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019},
-  editor    = {Piotr Bański and Adrien Barbaresi and Hanno Biber and Evelyn Breiteneder and Simon Clematide and Marc Kupietz and Harald L{"u}ngen and Caroline Iliadi},
-  publisher = {Leibniz-Institut f{"u}r Deutsche Sprache},
-  address   = {Mannheim},
-  doi       = {10.14618/ids-pub-9021},
-  url       = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215},
-  pages     = {9 -- 16},
-  year      = {2019},
-  language  = {en}
-}
 ```
 
-- [CCc100](https://huggingface.co/datasets/eduagarcia/cc100-pt) (License: [Common Crawl terms of use](https://commoncrawl.org/terms-of-use/))
+## Dataset Creation
 
-**Citation Information**
+### Curation Rationale
 
-```latex
-@inproceedings{conneau-etal-2020-unsupervised,
-    title = "Unsupervised Cross-lingual Representation Learning at Scale",
-    author = "Conneau, Alexis  and
-      Khandelwal, Kartikay  and
-      Goyal, Naman  and
-      Chaudhary, Vishrav  and
-      Wenzek, Guillaume  and
-      Guzm{\'a}n, Francisco  and
-      Grave, Edouard  and
-      Ott, Myle  and
-      Zettlemoyer, Luke  and
-      Stoyanov, Veselin",
-    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
-    month = jul,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.acl-main.747",
-    doi = "10.18653/v1/2020.acl-main.747",
-    pages = "8440--8451",
-}
+This dataset was developed are part of the [TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese](https://arxiv.org/abs/2401.16640) paper. In this study, we document the development of open-foundation models tailored for use in low-resource settings, their limitations, and their benefits.
 
-@inproceedings{wenzek-etal-2020-ccnet,
-    title = "{CCN}et: Extracting High Quality Monolingual Datasets from Web Crawl Data",
-    author = "Wenzek, Guillaume  and
-      Lachaux, Marie-Anne  and
-      Conneau, Alexis  and
-      Chaudhary, Vishrav  and
-      Guzm{\'a}n, Francisco  and
-      Joulin, Armand  and
-      Grave, Edouard",
-    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
-    month = may,
-    year = "2020",
-    address = "Marseille, France",
-    publisher = "European Language Resources Association",
-    url = "https://www.aclweb.org/anthology/2020.lrec-1.494",
-    pages = "4003--4012",
-    language = "English",
-    ISBN = "979-10-95546-34-4",
-}
-```
+### Source Data
 
-- [Roots Wikiquote](https://huggingface.co/datasets/bigscience-data/roots_pt_wikiquote) (License: [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/))
+#### Initial Data Collection and Normalization
 
-- [Roots Ted Talks](https://huggingface.co/datasets/bigscience-data/roots_pt_ted_talks_iwslt) (License: [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en))
+We utilized some of the filters used in Rae et al. ([2021](https://arxiv.org/abs/2112.11446)), besides using a [fine-tuned BERTimbau](https://huggingface.co/nicholasKluge/ToxicityModelPT) to exclude samples classified above a pre-defined toxicity threshold.
 
-**Citation Information**
+#### Who are the source language producers?
 
-```latex
-@article{laurenccon2022bigscience,
-  title={The bigscience roots corpus: A 1.6 tb composite multilingual dataset},
-  author={Lauren{\c{c}}on, Hugo and Saulnier, Lucile and Wang, Thomas and Akiki, Christopher and Villanova del Moral, Albert and Le Scao, Teven and Von Werra, Leandro and Mou, Chenghao and Gonz{\'a}lez Ponferrada, Eduardo and Nguyen, Huu and others},
-  journal={Advances in Neural Information Processing Systems},
-  volume={35},
-  pages={31809--31826},
-  year={2022}
-}
-```
+All text samples are native to Portuguese or translated from other languages to Portuguese (slight contamination of other languages should also be expected).
 
-## How to use
+### Annotations
 
-To use this dataset, use the following code snippet:
+#### Annotation process
 
-```python
-from datasets import load_dataset
+Portuguese-Corpus is a concatenation of several portions of Brazilian Portuguese datasets found in the [Hub](https://huggingface.co/datasets?task_categories=task_categories:text-generation&language=language:pt&sort=trending). We utilized some of the filters used in Rae et al. ([2021](https://arxiv.org/abs/2112.11446)), besides using a [fine-tuned BERTimbau](https://huggingface.co/nicholasKluge/ToxicityModelPT) to exclude samples classified above a pre-defined toxicity threshold.
 
-dataset = load_dataset("nicholasKluge/Pt-Corpus", split='train')
+#### Who are the annotators?
 
-# If you don't want to download the entire dataset, set streaming to `True`
-dataset = load_dataset("nicholasKluge/Pt-Corpus", split='train', streaming=True)
+[Nicholas Kluge Corrêa](mailto:nicholas@airespucrs.org).
 
-```
+### Personal and Sensitive Information
+
+This dataset, sourced from web scraping, may potentially contain personal and sensitive information, alongside offensive, toxic, and disturbing language.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+The presence of personal and sensitive information within the dataset raises concerns about privacy and data protection, potentially leading to breaches of individuals' confidentiality and security. Furthermore, the inclusion of offensive, toxic, and disturbing language in the dataset poses risks of perpetuating harmful behaviors and attitudes, contributing to the normalization of hate speech and online toxicity. Therefore, careful handling and ethical considerations are essential to mitigate these potential social impacts and promote responsible dataset use.
+
+### Discussion of Biases
+
+The inclusion of offensive, toxic, and disturbing language in the dataset poses risks of perpetuating harmful behaviors and attitudes, contributing to the normalization of hate speech and online toxicity.
 
-## Disclaimer
+### Other Known Limitations
+
+A significant portion of the data within the dataset has been translated using translation engines, potentially resulting in corrupted samples of both language and code. While useful for quickly converting text between languages, translation engines often struggle with accurately preserving the syntax, semantics, and context of programming languages. As a result, the translated code may contain errors, syntax inconsistencies, or even introduce vulnerabilities, rendering it unreliable or unusable for its intended purpose.
+
+## Additional Information
+
+### Dataset Curators
+
+[Nicholas Kluge Corrêa](mailto:nicholas@airespucrs.org).
+
+### Licensing Information
+
+The following datasets (_only training splits are a part of the corpus_) and respective licenses form the Portuguese-Corpus:
+
+- [Wikipedia](https://huggingface.co/datasets/graelo/wikipedia) (License: [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/))
 
-The dataset might contain offensive content, as some parts are a subset of public Common Crawl data. This means that the dataset contains sentences that, if viewed directly, can be insulting, threatening, or might otherwise cause anxiety.
+- [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX) (License: [ODC-By](https://opendatacommons.org/licenses/by/1-0/), [cc0-1.0](https://huggingface.co/datasets/oscar-corpus/OSCAR-2301#licensing-information))
+
+- [OSCAR](https://huggingface.co/datasets/eduagarcia/OSCAR-2301-pt_dedup) (License: [cc0-1.0](https://huggingface.co/datasets/oscar-corpus/OSCAR-2301#licensing-information))
+
+- [CCc100](https://huggingface.co/datasets/eduagarcia/cc100-pt) (License: [Common Crawl terms of use](https://commoncrawl.org/terms-of-use/))
+
+- [Roots Wikiquote](https://huggingface.co/datasets/bigscience-data/roots_pt_wikiquote) (License: [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/))
+
+- [Roots Ted Talks](https://huggingface.co/datasets/bigscience-data/roots_pt_ted_talks_iwslt) (License: [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en))
+
+### Citation Information
+
+```latex
+
+@misc{correa24ttllama,
+  title = {TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese},
+  author = {Corr{\^e}a, Nicholas Kluge and Falk, Sophia and Fatimah, Shiza and Sen, Aniket and De Oliveira, Nythamar},
+  journal={arXiv preprint arXiv:2401.16640},
+  year={2024}
+}
+
+```
 
+### Contributions
 
+If you would like to contribute, contact me at [nicholas@airespucrs.org](mailto:nicholas@airespucrs.org)!