Datasets:
dataset_info:
- config_name: usenet
features:
- name: title
dtype: string
- name: author
dtype: string
- name: id
dtype: int64
- name: progressive_number
dtype: int64
- name: timestamp
dtype: timestamp[s]
- name: newsgroup
dtype: string
- name: original_url
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 75784729153
num_examples: 89499446
download_size: 34215328650
dataset_size: 75784729153
- config_name: forums
features:
- name: title
dtype: string
- name: author
dtype: string
- name: post_id
dtype: int32
- name: progressive_number
dtype: int32
- name: timestamp
dtype: string
- name: forum
dtype: string
- name: text
dtype: string
- name: image_list
sequence: int32
- name: image_file
dtype: int32
splits:
- name: train
num_bytes: 263411751276
num_examples: 468698334
download_size: 103952670799
dataset_size: 263411751276
- config_name: OJS
features:
- name: journal
dtype: string
- name: url
dtype: string
- name: metadata
list:
- name: Alternative
dtype: string
- name: Coverage
dtype: string
- name: DOI
dtype: string
- name: Description
dtype: string
- name: Format
dtype: string
- name: ISSN
dtype: string
- name: Identifier
dtype: string
- name: Issue
dtype: string
- name: Language
dtype: string
- name: NBN
dtype: string
- name: PersonalName
dtype: string
- name: Rights
dtype: string
- name: Source
dtype: string
- name: Sponsor
dtype: string
- name: Subject
dtype: string
- name: Title
dtype: string
- name: Type
dtype: string
- name: URI
dtype: string
- name: Volume
dtype: string
- name: abbrev
dtype: string
- name: abstract
dtype: string
- name: articleType
dtype: string
- name: author
dtype: string
- name: authors
dtype: string
- name: available
dtype: string
- name: created
dtype: string
- name: date
dtype: string
- name: dateSubmitted
dtype: string
- name: doi
dtype: string
- name: firstpage
dtype: string
- name: institution
dtype: string
- name: issn
dtype: string
- name: issue
dtype: string
- name: issued
dtype: string
- name: keywords
dtype: string
- name: language
dtype: string
- name: lastpage
dtype: string
- name: modified
dtype: string
- name: nbn
dtype: string
- name: pageNumber
dtype: string
- name: readable
dtype: string
- name: reference
dtype: string
- name: spatial
dtype: string
- name: temporal
dtype: string
- name: title
dtype: string
- name: url
dtype: string
- name: volume
dtype: string
- name: text
dtype: string
- name: platform
dtype: string
splits:
- name: train
num_bytes: 12343533858
num_examples: 232223
download_size: 2948236259
dataset_size: 12343533858
- config_name: blogs
features:
- name: title
dtype: string
- name: name
dtype: string
- name: author
dtype: string
- name: date
dtype: string
- name: url
dtype: string
- name: text
dtype: string
- name: category
dtype: string
- name: license_guess
dtype: string
- name: fasttext_langid
sequence: string
- name: fasttext_langprob
dtype: float64
splits:
- name: train
num_bytes: 7100477248
num_examples: 1724658
download_size: 3765734285
dataset_size: 7100477248
- config_name: books
features:
- name: title
dtype: string
- name: author
dtype: string
- name: url
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1567112840
num_examples: 6167
download_size: 970007483
dataset_size: 1567112840
- config_name: reddit
features:
- name: subreddit
dtype: string
- name: author
dtype: string
- name: id
dtype: string
- name: parent_id
dtype: string
- name: created_utc
dtype: string
- name: score
dtype: string
- name: ups
dtype: string
- name: downs
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 368299927
num_examples: 4192526
download_size: 137818471
dataset_size: 368299927
- config_name: websites
features:
- name: url
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 2046525253
num_examples: 163554
download_size: 966736330
dataset_size: 2046525253
- config_name: wikimedia_it
features:
- name: title
dtype: string
- name: text
dtype: string
- name: wiki
dtype: string
splits:
- name: train
num_bytes: 16019187125
num_examples: 5408451
download_size: 7883736990
dataset_size: 16019187125
- config_name: wikimedia_others
features:
- name: title
dtype: string
- name: text
dtype: string
- name: wiki
dtype: string
splits:
- name: train
num_bytes: 1837994874
num_examples: 1043329
download_size: 814755723
dataset_size: 1837994874
configs:
- config_name: OJS
data_files:
- split: train
path: OJS/train-*
- config_name: blogs
data_files:
- split: train
path: blogs/train-*
- config_name: books
data_files:
- split: train
path: books/train-*
- config_name: forums
data_files:
- split: train
path: forums/train-*
- config_name: reddit
data_files:
- split: train
path: reddit/train-*
- config_name: usenet
data_files:
- split: train
path: usenet/train-*
- config_name: websites
data_files:
- split: train
path: websites/train-*
- config_name: wikimedia_it
data_files:
- split: train
path: wikimedia_it/train-*
- config_name: wikimedia_others
data_files:
- split: train
path: wikimedia_others/train-*
task_categories:
- text-classification
- text-generation
language:
- it
size_categories:
- 100B<n<1T
WARNING: THIS "README" IS JUST A STUB, IT WILL BE IMPROVED DURING THE NEXT FEW DAYS AND FILLED WITH MANY OTHER INFORMATIONS AND DETAILED STATISTICS
Testimole -- A multi-billion tokens Italian text corpus
The goal of this work is to create a huge linguistic resource for the Italian language that can be used for several NLP applications, including but not limited to Large Language Models. The dataset is the result of a massive web scraping effort going on from February 2024 to May 2024, so the resources have a cut-off date within this time span.
This is probably one of the biggest linguistic resources in Italian at the present day, as
To create the dataset, I developed several scripts using Python3 and libraries such as BeautifulSoup and Selenium; the scripts were mostly written and executed manually, making it an extremely time-consuming project. The texts span different topics and periods, containing several divergent opinions and beliefs, thus following the main ideas of the "Perspective Data Manifesto" [1]. It is important to note that these data alone are not enough to train an Italian large language model from scratch, mainly not due to the size of the data but because, even if they span over different topics, they are far from covering the broad range of subjects, information, culture, and techniques required to train a state-of-the-art model. Also, as will be better pointed out later, while it is safe to use these data under Fair Use for research purposes, users must investigate potential copyright infringement for other possible purposes. The Tiktoken BPE tokenizer with the cl100k_base model [2] was used for tokenization. This dataset is composed of several sub-datasets, each with different types of data and goals.
Conversational (~ 85 Billions tokens):
UsenetArchiveIT
This is the project that started the entire work: the goal was to collect the largest possible amount of Usenet posts published in the hierachies it.* and italia.* [3], as they were listed on "www.eternal-september.org" and gathered mainly from Google Groups archive.
This split contains 19.395.579.455 tokens. Texts were not checked for language, but it is a safe assumption that most of the text contained is in Italian as the selected Usenet hierarchies target only Italian users.
Detailed statistics, already computed, will follow very soon. For now, here are general stats about this part of the dataset:
"chars": 59389804791,
"tokens": 19395579455,
"sentences": 519535427,
"post": 89499446,
"thread": 14521548,
83GB of JSONL file before the conversion to HuggingFace dataset
Forum
The second part of the project is the one that produced the largest amount of data. 62.415.825.978 A list of Italian message boards based on different platforms (phpBB, vBulletin, Simple Machines, Invision, Snitz, XenForo...) was created using both manual and semi-automatic web searches. Then, for each forum, a generic script (forum_scraper.py) using Python3 and BeautifulSoup was adapted to fit the characteristics of the forum (such as correct div classes for the different fields and multiple page mechanisms). Then, the script ran over the entire range of available pages and output a JSONL file with one post per line. Detailed statistics, already computed, will follow very soon. For now, here are general stats about this part of the dataset:
{
"chars": 199436329709,
"tokens": 62415825978,
"sentences": 1673025712,
"posts": 468391746,
"threads": 25280745,
"hasImage": 46071
}
303GB of JSONL files before the conversion to HuggingFace dataset.
Regarding multimodality, in short: this feature is not very well implemented. More details will follow, but do not expect too much regarding this point.
General notes on conversational datasets:
The data contained in the "usenet" and "forums" splits were generated by Italian users of the Internet between 1995 and 2024. For this reason, they may contain biases, problematic stances with respect to ethics, grammatically wrong sentences and non-factually true information. On the other hand, the kind of data can be considered safer than a random crawl of the Internet, in particular regarding the "forum" subset because in many forums there is a strict system of moderation that prohibit posts to go beyond a certain treshold of acceptance (different from forum to forum) with regards to language and thematics. Because the name of the forum/newsgroup is always present in the dataset, it is possible for the users of this dataset to filter the sources of data according to their needs.
It is also important to note, for people less accustomed to internet conversations, that data coming from forums are not just generic conversations but are often a real goldmine of detailed and extremely specific information about several topics written by people who are often passionate and very knowledgeable about what they are discussing. This is especially true for forums that discuss technical and scientific topics.
This collection of conversational data is useful not only for general language modelling but also for many NLP tasks that could take advantages from a very large amount of conversational data, such as sentiment analysis, hate/misoginy speech detection, parsing and so on; on the other hand, the diacronic nature of data permits interesting analysis on diachronic phenomena such as anaylysis of how the Italian language used in the Internet changed over the year and the most discussed topics for each historical period, just to mention a couple of examples.
The post should not contain personal information as in all the forums internal rules was asked to the user not to share personal information as they would have been publicly available on the web.
General
OJS
This split of the dataset contains articles published as Open Access using the platform OJS. It comprised mainly academic journals from Italian universities, so it can be considered as a very high-quality dataset. All the articles are published with Creative Commons licenses, and the license used for the single article can be retrieved from the metadata.
Blogs
This resource was gathered by scraping data from blogs written in Italian. The project started with a collection of blogs regarding left-wing activism, in order to help another person for his research project, that it is still work in progress. The list of these blog was obtained on a blog aggregator. The blogs that fall under this category are labelled with the category "pol/ant" (Poltics/Antagonism). Because from a quick analysis it seems that data coming from the "forum" category are mainly biased toward right political stances (data about this statement will follow in the next weeks), it could be useful to integrate these data in a general language-modelling task in the optic of the "Perspectivist Data Manifesto" [1]. The other two categories are "let/litblog", containing blogs about literature (the list was obtained from another aggregator) and "inf/linux", a very small category containing blog posts from Italian Linux User Groups. The rest of the data is not categorized. Here a breakdown of number of tokens per category:
This sub-project started with the goal of collecting only blogs released under Public Domain or Creative Commons license. However, due do the automatic nature of the list creation process, I noticed that some blog having an "All right reserved" license were scraped too. Some of these license permits the reuse of the information with the only obligation of mentioning the URL, and the URL is always present in the rows of the dataset. I created a simple script that tried to guess from the home page of the blog, but the results are not optimal and a better pipeline should be implemented. This means that the direct use of this resource is fine under Fair-Use for research purposes but the possibility of usage should be checked by whom wants to use this dataset for other purposes, especially for commercial purposes.
This resource can be considered as a "medium-high" quality dataset, because it mostly contain blogs post, often from good sources with very informative content. It is not possible to guarantee a total absence of undesired content inside the resource, but this, depending from the use case, probably constitutes a minority.
As for the Conversational data split, also this split is diachronically annotated so it could be used for interesting diachronic analysis.
Finally, the blog split contains also an annotation for the language used, as identified by the FastText library.
Wikimedia
This split doesn't need many explanation as it is simply a dump of wikimedia resources in Italian (Wikipedia, Wikibooks, Wikinews, Wikiquote, Wikisource, Wikiversity, Wikivoyage and Wiktionary). It can be very important to include this resource in the training data of a language model because it contains information, presented in a mostly neutral language, about many possible subjects and topics that are not covered by the rest of the dataset.
I decided to create also a category called "wikimedia_others" containing data from Wikimedia projects of other regional languages related with Italian and spoken in Italy, as well as Latin for its historical importance for Italian language and culture. Languages code included in this split are: eml (emilian e rumagno) ,fur (furlan) ,la (latin) ,lij (ligure) ,lld (ladin) ,lmo (lombarda) ,nap (napolitan) ,scn (sicilianu) ,sc (sardu) and vec (veneto). Using this data, depending from the goal of the project, could produce very interesting results.
Books
This collection contains mainly the books coming from LiberLiber's project "Manuzio" [2]. The books were downloaded from the website in many formats and converted to text. Liber Liber is a project akin to Project Gutenberg as it contains many books with expired copyright and thus in Public Domain. Many of these books are considered cornerstones of Italian culture.
The collection contains also a smaller amount of data coming from other sources, such as the Creative Commons licensed school books of "Matematicamente" [3] and Oilproject-Weschool [4] as well as some other CC and PD license book found online.
Websites
I created a very generic script that is able to extract all the text of a website as well as the text contained in Office, PDF and TeX documents. Now, the websites section is mainly composed of three very high-quality and freely licensed websites: ArchivioAntimafia [5], that contains many official documents about Mafia persecution in Italy, Peacelink [6], an historical Italian website about peace activism and HomoLaicus [7] a big collection of texts about various topics (mainly history and politics) released under a CC license. Also other smaller and randomly selected websites are included in this collection. This section has to be considered experimental for two reasons: (1) It containly only a very small subset of the entire high-quality Italian web landscape and it could be increased and improved "ad libitum" (2) It is the only section that can have some bigger issue with deduplication, that we will discuss in the appropriate section.
Despite these two point, users are encouraged to use this section as it is composed of medium-high and high quality contents.
It contains a small subsets (4192672 messages) of conversations in some Italian subreddits.
Italatex Still work in progress. A collection of materials written in LaTeX.
DEDUPLICATION
The presence of duplicate text can be, depending from the use cases, a big problem for several machine learning tasks. I tried to avoid as much as possible the presence of duplicate text in the dataset but still there are some potential issues to be took into consideration. We will distinguish two kind of duplications: (A): Full document duplication, for example, if the same forum post is present more than one time (B): Strings duplication: if some strings (often garbage) recurr several times in the data.
Usenet: Safe regarding A-types of duplications; Could contain B-types duplications, for example: - Users signatures; - Headers such as "reply to message posted by X at Y";
Forums: Safe regarding A-types of duplications. The most problematic forums under this respect were deduplicated using an ad-hoc created script. It shares the same potential problems of Usenet with regard to B-type duplications;
OJS: it should be safe regarding both A-type and B-type duplications;
Blogs: Safe regarding A-types of duplications and mostly safe regarding B-type duplications. However, I noticed that some blogs were scraped along with some html garbage at the beginning or end of the text blob, that should be identified and removed
Wikimedia: it should be mostly safe, with the exception of the recurrence of some Wikipedia-specific lexicon such as "this page is a stub", "this page needs references" and so on;
Books: it should be safe regarding A-types of duplication, but there is a very simple to identify B-type duplication, that is, the header of Liber Liber books with a short presentation of the community-driven project;
Websites: In this case A-type duplication could be in theory present if some pages share the same content, but it should be rare (with the exception of Archivio Antimafia, where files to download are often available in PDF and Word Processing format, so they were downloaded twice). B-type duplication here could be an issue as it is very present in the form of 1) header of the website 2) list of links 3) footer of the website. All the HTML was converted using HTML2TEXT so it should not contain html code.
Detailed statistics Work in progress; this will contain statistics of tokens, chars and sentences lenght for each diachronic resource (usenet newsgroup, post, blog) for each month for each year
Conclusions Work in progress References (partial)
* [1] https://pdai.info/
* [2] https://github.com/openai/tiktoken
* [3] https://xmau.com/usenet/