Update README.md

README.md

@@ -51,4 +51,118 @@ configs:
   data_files:
   - split: train
     path: ary_Arab/train-*
+task_categories:
+- text-generation
+language:
+- ary
 ---
+# Gherbal’ing Multilingual Fineweb2 🍵
+Following up on their previous release, the fineweb team has been hard at work on their upcoming multilingual fineweb dataset which contains a massive collection of 50M+ sentences across 100+ languages. The data, sourced from the Common Crawl corpus, has been classified into these languages using GlotLID, a model able to recognize more than
+2000 languages. The performance of GlotLID is quite impressive, considering the complexity
+of the task. It is able to correctly identify the language of a sentence with a decent degree of
+accuracy. However, especially for low-resource languages, it still makes some mistakes, and
+some languages are more difficult for it to identify than others.
+We were approached by the fineweb team to see if we could help them improve the quality of
+the dataset using our Gherbal language detection model, which we recently released and
+made available on our API platform. Our performance on several low-resource languages,
+notably Moroccan, Persian and Swahili, is quite impressive, and the hope is to expand the
+available resources for these underserved communities. We gladly accepted the challenge and
+were given access to the pre-release dataset, with GlotLID classifications, and were tasked
+with identifying the sentences that GlotLID misclassified.
+As a first step, we chose to focus on Moroccan Arabic, a language spoken by millions of
+people in Morocco and people of Moroccan descent in Europe. This report will detail the
+process we went through to achieve our goal, and the results we obtained.
+# The Dataset
+The dataset we were given is a bunch of parquet files containing 50M+ sentences, with the
+following columns:
+* **id:** a unique identifier for the document
+* **text:** the document itself, extracted from a webpage
+* **metadata**: a json column containing metadata about the sentence, including the url of
+the page it was found on, the date of the page, and the previous classification of the page
+by GlotLID.
+The dataset contains several configurations, each one corresponding to a different language.
+We focused our attention on the Arabic arb_Arab_dedup and the Moroccan
+ary_Arab_dedup configurations, which we will refer to as the “Arabic” and “Moroccan”
+datasets respectively.
+# Our Approach
+## Dataset Processing
+To tackle this challenge, we developed a systematic pipeline to process and analyze each
+dataset. First, we performed thorough text cleanup to remove any unwanted artifacts and
+standardize the content. This ensures we’re working with clean, natural language text,
+especially as webpage content can be quite noisy.
+Next, we leveraged the Natural Language Toolkit (NLTK) library to break down the
+documents into individual sentences. While this is not a perfect solution and the noisy content
+can make it difficult to identify sentences particulary for languages not supported by NLTK,
+it is a good enough approximation for our purposes, which is to reduce the variance in a
+webpage and avoid confusing the model with extremely long mixed-language content. This
+step was crucial as it allowed us to analyze the text at a more granular level.
+With our sentences prepared, we then ran each one through our Gherbal language detection model. The model evaluated each sentence and provided a confidence score across the 33
+languages Gherbal supports. We then aggregated these sentence-level results by averaging the
+classification scores. This gave us a comprehensive understanding of the dominant language
+patterns within each document. A more fine-grained analysis at the sentence level would have
+yielded more data with higher quality, but ultimately postponed to a later release given the
+time and resource constraints.
+Finally, we applied a filtering step to focus specifically on content classified as Moroccan
+Arabic in Arabic script (ary_Arab). The resulting dataset is available on Huggingface at
+**sawalni-ai/ml-fw-darija**.
+
+## Dataset Analysis
+We used our Klimat library to analyze the dataset. Klimat is a tool we developed to perform
+statistical analysis on language datasets, and is able to generate a number of interesting
+insights into the data. We will share more about Klimat in a future blog post, but for now we
+will focus on the results we obtained for the fineweb dataset.
+## Website Analysis
+We also performed an analysis on the websites that were used to source the data in
+multilingual fineweb, and classified by Gherbal as Moroccan Arabic. This gave us an
+interesting insight on where Moroccan Arabic is used on the web, which could be useful to
+increase the quantity of high quality data for the language. We broke down the data by
+multiple criteria, including the top level domain, the duration the website was online (based
+on Common Crawl accessing it), and more.
+We restricted the analysis to high confidence samples, and filtered to the top 1000 websites
+by quantity of data.
+# Our Results
+Let’s start by looking at the results for the Moroccan dataset.
+* Original count in ary_Arab: 5.8M
+* Resulting count after filtering: 37352 (0.64% of original)
+* Number of tokens in ary_Arab: 2.8B (estimated using tiktoken for multilinguality)
+* Number of tokens in filtered dataset: 75.3M
+
+## False Positives
+A manual review of the filtered dataset revealed that human preferences were consistent with
+the results of Gherbal, and that the filtered dataset should be a good resource for training and
+evaluating models for Moroccan, despite the small sample size. It is worth noting that
+Algerian and Tunisian Arabic were also misclassified as Moroccan Arabic due to the elevated
+mutual intelligibility between the three. This is a known current limitation of Gherbal which
+only supports Moroccan and Egyptian varieties of Arabic and should be addressed in future
+releases.
+## False Negatives
+Looking at our Gherbal paper (pending publication), specifically at the benchmark results on
+the flores-200 devtest set, we can estimate that the false negative rate from Standard Arabic
+(arb_Arab) to Moroccan Arabic (ary_Arab) is around 10%. Extrapolating this figure, we can
+estimate the false negative rate for the filtered dataset to be around 37352 * 0.1 = 3735
+Moroccan Arabic sentences that were incorrectly filtered out.
+
+## Other dataset configurations
+We also applied the same process to the other dataset configurations, namely the Arabic
+(arb_Arab) and the latin-script Arabic (arb_Latn) configurations. While the results are not yet
+complete, we can already draw some interesting observations:
+- Arabic (arb_Arab)
+  * Original count in arb_Arab: 24.2M
+  * Resulting count after filtering: 0
+While some samples (<100 in total) were classified as Moroccan Arabic, a manual review
+revealed that these were all incorrect classifications by Gherbal and that the filtered dataset is
+indeed empty. This might change as we process the rest of the dataset, or as we improve
+Gherbal’s performance on Arabic and its related languages. The resulting dataset will be
+made available as an additional configuration on the same dataset here when the processing is
+complete.
+- Arabic in Latin script (arb_Latn)
+  * Original count in arb_Latn: 600K
+  * Resulting count after filtering: 15K (2.5% of original)
+This dataset is classified as arb_Latn by GlotLID, which presents extreme variance in the data
+as Arabic can be transliterated in so many different ways. As Gherbal is able to correctly
+identify ary_Latn (Moroccan Arabic in Latin script) with a high degree of accuracy, we are
+able to recover a significant amount of data that was previously quite unusable among all the
+noise. We also observe that this dataset contains the most variations in the actual language as
+classified by Gherbal, which confirms that the arb_Latn label from GlotLID is not a good
+proxy for high quality Arabic data in latin script. The resulting dataset will be made available
+as an additional configuration on the same dataset here when the analysis is complete.