Update README.md

README.md

@@ -310,3 +310,311 @@ configs:
   - split: train
     path: wikimedia_others/train-*
 ---
+WARNING: THIS "README" IS JUST A STUB, IT WILL BE IMPROVED DURING THE
+NEXT FEW DAYS, GRAMMATICALLY CHECKED AND FILLED WITH MANY OTHER
+INFORMATIONS AND DETAILED STATISTICS
+
+**Testimole** -- A multi-billion 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](http://www.eternal-september.org/)" \[4\]
+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:
+
+{
+
+\"char\_count\": 59389804791,
+
+\"token\_count\": 19395579455,
+
+\"sent\_count\": 519535427,
+
+\"post\_count\": 89499446,
+
+\"thread\_count\": 14521548,
+
+\"author\_count\": 3626546,
+
+}
+
+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:
+
+{
+
+\"char\_count\": 199436329709,
+
+\"token\_count\": 62415825978,
+
+\"text\_bytes\": 201359756617,
+
+\"sent\_count\": 1673025712,
+
+\"post\_count\": 468391746,
+
+\"thread\_count\": 25280745,
+
+\"author\_count\": 37426524,
+
+\"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.
+
+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.
+
+Reddit
+
+It contains a small subsets (4192672 messages) of conversations in some
+Italian subreddits.
+
+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.
+
+References
+
+\* \[1\] <https://pdai.info/>
+
+\* \[2\] https://github.com/openai/tiktoken
+
+\* \[3\] <https://xmau.com/usenet/>