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- name: language_names
dtype: string
- name: language_code
dtype: string
splits:
- name: train
num_bytes: 2181278
num_examples: 1000
download_size: 1577122
dataset_size: 2181278
- config_name: zsm_Latn
features:
- name: id
dtype: string
- name: text
dtype: string
- name: educational_value_labels
sequence: string
- name: annotator_ids
sequence: string
- name: problematic_content_label_present
dtype: bool
- name: problematic_content_label_agreement
dtype: float64
- name: language_names
dtype: string
- name: language_code
dtype: string
splits:
- name: train
num_bytes: 3428921
num_examples: 1000
download_size: 1944801
dataset_size: 3428921
configs:
- config_name: aeb_Arab
data_files:
- split: train
path: aeb_Arab/train-*
- config_name: apc_Arab
data_files:
- split: train
path: apc_Arab/train-*
- config_name: arb_Arab
data_files:
- split: train
path: arb_Arab/train-*
- config_name: ars_Arab
data_files:
- split: train
path: ars_Arab/train-*
- config_name: ary_Arab
data_files:
- split: train
path: ary_Arab/train-*
- config_name: arz_Arab
data_files:
- split: train
path: arz_Arab/train-*
- config_name: asm_Beng
data_files:
- split: train
path: asm_Beng/train-*
- config_name: asm_Latn
data_files:
- split: train
path: asm_Latn/train-*
- config_name: ast_Latn
data_files:
- split: train
path: ast_Latn/train-*
- config_name: bak_Cyrl
data_files:
- split: train
path: bak_Cyrl/train-*
- config_name: bar_Latn
data_files:
- split: train
path: bar_Latn/train-*
- config_name: ben_Beng
data_files:
- split: train
path: ben_Beng/train-*
- config_name: bho_Deva
data_files:
- split: train
path: bho_Deva/train-*
- config_name: bre_Latn
data_files:
- split: train
path: bre_Latn/train-*
- config_name: bul_Cyrl
data_files:
- split: train
path: bul_Cyrl/train-*
- config_name: cat_Latn
data_files:
- split: train
path: cat_Latn/train-*
- config_name: ces_Latn
data_files:
- split: train
path: ces_Latn/train-*
- config_name: cmn_Hani
data_files:
- split: train
path: cmn_Hani/train-*
- config_name: crh_Latn
data_files:
- split: train
path: crh_Latn/train-*
- config_name: dan_Latn
data_files:
- split: train
path: dan_Latn/train-*
- config_name: default
data_files:
- split: train
path: data/train-*
- config_name: deu_Latn
data_files:
- split: train
path: deu_Latn/train-*
- config_name: ekk_Latn
data_files:
- split: train
path: ekk_Latn/train-*
- config_name: eus_Latn
data_files:
- split: train
path: eus_Latn/train-*
- config_name: fao_Latn
data_files:
- split: train
path: fao_Latn/train-*
- config_name: fas_Arab
data_files:
- split: train
path: fas_Arab/train-*
- config_name: fil_Latn
data_files:
- split: train
path: fil_Latn/train-*
- config_name: fin_Latn
data_files:
- split: train
path: fin_Latn/train-*
- config_name: fra_Latn
data_files:
- split: train
path: fra_Latn/train-*
- config_name: glg_Latn
data_files:
- split: train
path: glg_Latn/train-*
- config_name: gmh_Latn
data_files:
- split: train
path: gmh_Latn/train-*
- config_name: goh_Latn
data_files:
- split: train
path: goh_Latn/train-*
- config_name: gom_Deva
data_files:
- split: train
path: gom_Deva/train-*
- config_name: gsw_Latn
data_files:
- split: train
path: gsw_Latn/train-*
- config_name: guj_Gujr
data_files:
- split: train
path: guj_Gujr/train-*
- config_name: hin_Deva
data_files:
- split: train
path: hin_Deva/train-*
- config_name: hsb_Latn
data_files:
- split: train
path: hsb_Latn/train-*
- config_name: hun_Latn
data_files:
- split: train
path: hun_Latn/train-*
- config_name: ind_Latn
data_files:
- split: train
path: ind_Latn/train-*
- config_name: ita_Latn
data_files:
- split: train
path: ita_Latn/train-*
- config_name: jpn_Jpan
data_files:
- split: train
path: jpn_Jpan/train-*
- config_name: kas_Deva
data_files:
- split: train
path: kas_Deva/train-*
- config_name: kin_Latn
data_files:
- split: train
path: kin_Latn/train-*
- config_name: kor_Hang
data_files:
- split: train
path: kor_Hang/train-*
- config_name: lat_Latn
data_files:
- split: train
path: lat_Latn/train-*
- config_name: lez_Cyrl
data_files:
- split: train
path: lez_Cyrl/train-*
- config_name: lij_Latn
data_files:
- split: train
path: lij_Latn/train-*
- config_name: lit_Latn
data_files:
- split: train
path: lit_Latn/train-*
- config_name: lug_Latn
data_files:
- split: train
path: lug_Latn/train-*
- config_name: lvs_Latn
data_files:
- split: train
path: lvs_Latn/train-*
- config_name: mal_Mlym
data_files:
- split: train
path: mal_Mlym/train-*
- config_name: mar_Deva
data_files:
- split: train
path: mar_Deva/train-*
- config_name: nan_Latn
data_files:
- split: train
path: nan_Latn/train-*
- config_name: nds_Latn
data_files:
- split: train
path: nds_Latn/train-*
- config_name: nld_Latn
data_files:
- split: train
path: nld_Latn/train-*
- config_name: nob_Latn
data_files:
- split: train
path: nob_Latn/train-*
- config_name: npi_Deva
data_files:
- split: train
path: npi_Deva/train-*
- config_name: npi_Latn
data_files:
- split: train
path: npi_Latn/train-*
- config_name: pbt_Arab
data_files:
- split: train
path: pbt_Arab/train-*
- config_name: pcm_Latn
data_files:
- split: train
path: pcm_Latn/train-*
- config_name: pdc_Latn
data_files:
- split: train
path: pdc_Latn/train-*
- config_name: pfl_Latn
data_files:
- split: train
path: pfl_Latn/train-*
- config_name: pol_Latn
data_files:
- split: train
path: pol_Latn/train-*
- config_name: por_Latn
data_files:
- split: train
path: por_Latn/train-*
- config_name: quz_Latn
data_files:
- split: train
path: quz_Latn/train-*
- config_name: ron_Latn
data_files:
- split: train
path: ron_Latn/train-*
- config_name: rus_Cyrl
data_files:
- split: train
path: rus_Cyrl/train-*
- config_name: sco_Latn
data_files:
- split: train
path: sco_Latn/train-*
- config_name: sin_Sinh
data_files:
- split: train
path: sin_Sinh/train-*
- config_name: slk_Latn
data_files:
- split: train
path: slk_Latn/train-*
- config_name: som_Latn
data_files:
- split: train
path: som_Latn/train-*
- config_name: spa_Latn
data_files:
- split: train
path: spa_Latn/train-*
- config_name: srp_Cyrl
data_files:
- split: train
path: srp_Cyrl/train-*
- config_name: srp_Latn
data_files:
- split: train
path: srp_Latn/train-*
- config_name: swe_Latn
data_files:
- split: train
path: swe_Latn/train-*
- config_name: tam_Taml
data_files:
- split: train
path: tam_Taml/train-*
- config_name: tat_Cyrl
data_files:
- split: train
path: tat_Cyrl/train-*
- config_name: tat_Latn
data_files:
- split: train
path: tat_Latn/train-*
- config_name: tel_Telu
data_files:
- split: train
path: tel_Telu/train-*
- config_name: tha_Thai
data_files:
- split: train
path: tha_Thai/train-*
- config_name: tir_Ethi
data_files:
- split: train
path: tir_Ethi/train-*
- config_name: tok_Latn
data_files:
- split: train
path: tok_Latn/train-*
- config_name: tur_Latn
data_files:
- split: train
path: tur_Latn/train-*
- config_name: udm_Cyrl
data_files:
- split: train
path: udm_Cyrl/train-*
- config_name: ukr_Cyrl
data_files:
- split: train
path: ukr_Cyrl/train-*
- config_name: uzn_Cyrl
data_files:
- split: train
path: uzn_Cyrl/train-*
- config_name: uzn_Latn
data_files:
- split: train
path: uzn_Latn/train-*
- config_name: vie_Latn
data_files:
- split: train
path: vie_Latn/train-*
- config_name: vls
data_files:
- split: train
path: vls/train-*
- config_name: vls_Latn
data_files:
- split: train
path: vls_Latn/train-*
- config_name: yor_Latn
data_files:
- split: train
path: yor_Latn/train-*
- config_name: yue_Hani
data_files:
- split: train
path: yue_Hani/train-*
- config_name: zsm_Latn
data_files:
- split: train
path: zsm_Latn/train-*
tags:
- argilla
- data-is-better-together
task_categories:
- text-classification
- text-classification
- text-classification
- text-classification
- text-classification
- text-classification
- text-classification
- text-classification
- text-classification
- text-classification
- text-classification
- text-classification
- text-classification
- text-classification
- text-classification
- text-classification
- text-classification
- text-classification
- text-classification
- text-classification
- text-classification
- text-classification
- text-classification
- text-classification
- text-classification
- text-classification
- text-classification
- text-classification
- text-classification
language:
- lvs
- kor
- kin
- ita
- hin
- gom
- glg
- fra
- fas
- eus
- deu
- dan
- cat
- bho
- bak
- arz
- ary
- arb
- yor
- vie
- ukr
- tur
- tir
- tel
- tam
- swe
- spa
- slk
- rus
- por
- pbt
- nld
- nds
- vls
- srp
- sco
- lat
- ind
- gmh
- ekk
- bul
- bre
- bar
- apc
- aeb
- ron
- mar
- kas
- hin
- crh
- arb
- zsm
- yue
- uzn
- uzn
- udm
- tha
- tat
- som
- sin
- pol
- pcm
- npi
- npi
- nan
- mal
- lug
- lit
- lez
- asm
- asm
- fil
- cmn
- ces
- ben
- ast
- ars
- jpn
- guj
- gsw
- fin
- tok
- srp
- quz
- pfl
- pdc
- nob
- lij
- hun
- hsb
- goh
- fao
pretty_name: FineWeb-c
FineWeb-C: Educational content in many languages, labelled by the community
Multilingual data is better together!
Note: We are not actively working on this project anymore. You can continue to contribute annotations and we'll occasionally refresh the exported data.
What is this?
FineWeb-C is a collaborative, community-driven project that expands upon the FineWeb2 dataset. The goal is to create high-quality educational content annotations across hundreds of languages.
By enhancing web content with these annotations, we aim to improve the development of Large Language Models (LLMs) in all languages, making AI technology more accessible and effective globally.
The annotations in this dataset will help train AI systems to automatically identify high-quality educational content in more languages and in turn help build better Large Language Models for all languages.
What the community is doing:
- For a given language, look at a page of web content from the FineWeb2 dataset in Argilla.
- Rate how educational the content is.
- Flag problematic content i.e. content that is malformed or in the wrong language.
Once a language reaches 1,000 annotations, the dataset will be included in this dataset! Alongside rating the educational quality of the content, different language communities are discussing other ways to improve the quality of data for their language in our Discord discussion channel.
What's been done so far?
So far 466 members of the Hugging Face community have submitted 58,188 annotations.
The following languages have reached the 10 annotation threshold to be included in the dataset.
| Language Code |
Language Name |
Completed Annotations |
Annotators |
| aeb_Arab |
Tunisian Arabic |
529 |
9 |
| apc_Arab |
North Levantine Arabic |
250 |
4 |
| arb_Arab |
Standard Arabic |
1000 |
10 |
| ars_Arab |
Najdi Arabic |
1000 |
1 |
| ary_Arab |
Moroccan Arabic |
1000 |
15 |
| arz_Arab |
Egyptian Arabic |
1000 |
9 |
| asm_Beng |
Assamese |
24 |
1 |
| asm_Latn |
Assamese |
1000 |
5 |
| ast_Latn |
Asturian |
80 |
1 |
| bak_Cyrl |
Bashkir |
442 |
4 |
| bar_Latn |
Bavarian |
1000 |
1 |
| ben_Beng |
Bangla |
15 |
1 |
| bho_Deva |
Bhojpuri |
643 |
1 |
| bre_Latn |
Breton |
46 |
2 |
| bul_Cyrl |
Bulgarian |
36 |
1 |
| cat_Latn |
Catalan |
34 |
5 |
| ces_Latn |
Czech |
459 |
5 |
| cmn_Hani |
Mandarin Chinese |
1000 |
3 |
| crh_Latn |
Crimean Tatar |
188 |
1 |
| dan_Latn |
Danish |
1000 |
18 |
| deu_Latn |
German |
212 |
19 |
| ekk_Latn |
Standard Estonian |
141 |
3 |
| eus_Latn |
Basque |
246 |
16 |
| fao_Latn |
Faroese |
24 |
1 |
| fas_Arab |
Persian |
1000 |
3 |
| fil_Latn |
Filipino |
1000 |
2 |
| fin_Latn |
Finnish |
1000 |
7 |
| fra_Latn |
French |
1000 |
28 |
| glg_Latn |
Galician |
341 |
8 |
| gmh_Latn |
Middle High German |
1000 |
1 |
| goh_Latn |
Old High German |
1000 |
5 |
| gom_Deva |
Goan Konkani |
160 |
1 |
| gsw_Latn |
Swiss German |
1000 |
2 |
| guj_Gujr |
Gujarati |
11 |
1 |
| hin_Deva |
Hindi |
621 |
22 |
| hsb_Latn |
Upper Sorbian |
27 |
1 |
| hun_Latn |
Hungarian |
124 |
3 |
| ind_Latn |
Indonesian |
29 |
3 |
| ita_Latn |
Italian |
1000 |
26 |
| jpn_Jpan |
Japanese |
1000 |
5 |
| kas_Deva |
Kashmiri |
186 |
2 |
| kin_Latn |
Kinyarwanda |
912 |
3 |
| kor_Hang |
Korean |
639 |
12 |
| lat_Latn |
Latin |
149 |
2 |
| lez_Cyrl |
Lezghian |
55 |
1 |
| lij_Latn |
Ligurian |
1000 |
1 |
| lit_Latn |
Lithuanian |
323 |
3 |
| lug_Latn |
Ganda |
132 |
1 |
| lvs_Latn |
Standard Latvian |
80 |
6 |
| mal_Mlym |
Malayalam |
10 |
1 |
| mar_Deva |
Marathi |
56 |
2 |
| nan_Latn |
Min Nan Chinese |
14 |
5 |
| nds_Latn |
Low German |
389 |
5 |
| nld_Latn |
Dutch |
361 |
10 |
| nob_Latn |
Norwegian Bokmål |
24 |
6 |
| npi_Deva |
Nepali (individual language) |
387 |
5 |
| npi_Latn |
Nepali (individual language) |
12 |
2 |
| pbt_Arab |
Southern Pashto |
11 |
1 |
| pcm_Latn |
Nigerian Pidgin |
240 |
3 |
| pdc_Latn |
Pennsylvania German |
622 |
2 |
| pfl_Latn |
Palatine German |
1000 |
1 |
| pol_Latn |
Polish |
50 |
2 |
| por_Latn |
Portuguese |
404 |
18 |
| quz_Latn |
Cusco Quechua |
80 |
1 |
| ron_Latn |
Romanian |
268 |
4 |
| rus_Cyrl |
Russian |
1000 |
4 |
| sco_Latn |
Scots |
588 |
3 |
| sin_Sinh |
Sinhala |
211 |
3 |
| slk_Latn |
Slovak |
221 |
5 |
| som_Latn |
Somali |
11 |
1 |
| spa_Latn |
Spanish |
1000 |
38 |
| srp_Cyrl |
Serbian |
93 |
4 |
| srp_Latn |
Serbian |
11 |
1 |
| swe_Latn |
Swedish |
1000 |
8 |
| tam_Taml |
Tamil |
1000 |
8 |
| tat_Latn |
Tatar |
208 |
5 |
| tel_Telu |
Telugu |
66 |
5 |
| tha_Thai |
Thai |
411 |
2 |
| tir_Ethi |
Tigrinya |
745 |
6 |
| tok_Latn |
Toki Pona |
11 |
1 |
| tur_Latn |
Turkish |
379 |
7 |
| udm_Cyrl |
Udmurt |
55 |
1 |
| ukr_Cyrl |
Ukrainian |
1000 |
5 |
| uzn_Cyrl |
Northern Uzbek |
20 |
2 |
| uzn_Latn |
Northern Uzbek |
44 |
2 |
| vie_Latn |
Vietnamese |
1000 |
11 |
| vls_Latn |
West Flemish |
1000 |
1 |
| yor_Latn |
Yoruba |
443 |
7 |
| yue_Hani |
Cantonese |
1000 |
7 |
| zsm_Latn |
Standard Malay |
1000 |
1 |
You can help contribute to the dataset here.
Note: We are not actively supporting this effort anymore but you can continue to contribute annotations and we'll occasionally refresh the exported data.
Below is an overview of the number of annotations submitted for each language (updated daily).
Why are we doing this?
There are many languages in the world where no high quality LLMs exist. Having high quality data is a central part of building high quality LLMs. FineWeb2 is a crucial step in improving the availability of high quality data for many languages. We plan to go a step further.
FineWeb-Edu for every language?
FineWeb-Edu is a dataset built on the original FineWeb dataset. The dataset was constructed by developing an educational quality classifier using annotations generated by LLama3-70B-Instruct and using this classifier to retain only the most educational web pages.
FineWeb-Edu outperforms FineWeb on popular benchmark. Crucially, using this approach reduces the amount of data needed to train a high quality LLM reducing the barrier to building a high quality LLM for many languages.
We want to make it possible to build FineWeb-Edu datasets for all the worlds languages. To do this we need annotations in order to train an educational quality classifier.
This in turn will allow us to build the next generation of Large Language Models for many languages.
Why not use LLMs to annotate the data?
For high resources languages, using an LLM to generate educational quality annotations can be a good solution. However, for many languages LLMs are not able to generate high quality annotations — or we don't have enough data to validate whether the annotations are correct.
How can I help?
You can help by contributing to the dataset here and join the community discussions in Discord!
Why would I bother to contribute to this dataset?
Your contributions directly shape the future of AI in your language. Here's why this matters:
Break the AI language barrier: Most commercial AI companies focus on profitable languages, leaving many communities behind. Your work helps bring AI capabilities to more languages.
Keep it open: Unlike proprietary datasets locked away by companies, FineWeb2-C is an open dataset. This means anyone can use it to build AI systems that truly serve their community's needs. Through this open approach we also learn about which approaches work best for different languages.
Be part of something bigger: Just as Wikipedia showed how volunteers can build invaluable resources, the Hugging Face community has created numerous open models and datasets. You're joining a movement to democratize AI technology.
Every annotation counts. Whether you can contribute ten minutes or ten hours, your input helps build a more inclusive future for AI technology 🤗
Who contributed to this dataset so far?
These are the top 10 contributors to this release of the dataset. Make sure to give them a follow on the Hub to show your appreciation!
| Hugging Face Username |
Submissions |
| stefan-it |
4,614 |
| tagay1n |
2,094 |
| hannayukhymenko |
1,937 |
| hasnachouikhi |
1,865 |
| Aivis |
1,613 |
| ivykopal |
1,365 |
| gaydmi |
1,112 |
| catastropiyush |
1,059 |
| theblackcat102 |
1,002 |
| SamerAttrah |
1,000 |
Data work is the under appreciated foundation of AI and ML. This dataset is built by the community for the community. Below is a leaderboard that is updated daily and shows all the contributors to this annotation effort.
Language-specific Contributors
Below you can find a list of all the contributors to this release of the dataset for each language ❤️
Detailed Contributor Statistics for each language
Assamese (asm_Beng)
User Statistics Table (Minimum 1 submissions)
Assamese (asm_Latn)
User Statistics Table (Minimum 1 submissions)
Asturian (ast_Latn)
User Statistics Table (Minimum 1 submissions)
Bangla (ben_Beng)
User Statistics Table (Minimum 1 submissions)
Bashkir (bak_Cyrl)
User Statistics Table (Minimum 1 submissions)
Basque (eus_Latn)
User Statistics Table (Minimum 1 submissions)
Bavarian (bar_Latn)
User Statistics Table (Minimum 1 submissions)
Bhojpuri (bho_Deva)
User Statistics Table (Minimum 1 submissions)
Breton (bre_Latn)
User Statistics Table (Minimum 1 submissions)
Bulgarian (bul_Cyrl)
User Statistics Table (Minimum 1 submissions)
Cantonese (yue_Hani)
User Statistics Table (Minimum 1 submissions)
Catalan (cat_Latn)
User Statistics Table (Minimum 1 submissions)
Crimean Tatar (crh_Latn)
User Statistics Table (Minimum 1 submissions)
| Username |
Submissions |
| gaydmi |
188 |
Cusco Quechua (quz_Latn)
User Statistics Table (Minimum 1 submissions)
| Username |
Submissions |
| daqc |
80 |
Czech (ces_Latn)
User Statistics Table (Minimum 1 submissions)
Danish (dan_Latn)
User Statistics Table (Minimum 1 submissions)
Dutch (nld_Latn)
User Statistics Table (Minimum 1 submissions)
Egyptian Arabic (arz_Arab)
User Statistics Table (Minimum 1 submissions)
Faroese (fao_Latn)
User Statistics Table (Minimum 1 submissions)
Filipino (fil_Latn)
User Statistics Table (Minimum 1 submissions)
Finnish (fin_Latn)
User Statistics Table (Minimum 1 submissions)
French (fra_Latn)
User Statistics Table (Minimum 1 submissions)
Galician (glg_Latn)
User Statistics Table (Minimum 1 submissions)
Ganda (lug_Latn)
User Statistics Table (Minimum 1 submissions)
| Username |
Submissions |
| Bronsn |
132 |
German (deu_Latn)
User Statistics Table (Minimum 1 submissions)
Goan Konkani (gom_Deva)
User Statistics Table (Minimum 1 submissions)
Gujarati (guj_Gujr)
User Statistics Table (Minimum 1 submissions)
Hindi (hin_Deva)
User Statistics Table (Minimum 1 submissions)
Hindi (hin_Deva)
User Statistics Table (Minimum 1 submissions)
Hungarian (hun_Latn)
User Statistics Table (Minimum 1 submissions)
Indonesian (ind_Latn)
User Statistics Table (Minimum 1 submissions)
Italian (ita_Latn)
User Statistics Table (Minimum 1 submissions)
Japanese (jpn_Jpan)
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Kashmiri (kas_Deva)
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Kinyarwanda (kin_Latn)
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Korean (kor_Hang)
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Latin (lat_Latn)
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Lezghian (lez_Cyrl)
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Ligurian (lij_Latn)
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Lithuanian (lit_Latn)
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Low German (nds_Latn)
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Malayalam (mal_Mlym)
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Mandarin Chinese (cmn_Hani)
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Marathi (mar_Deva)
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Middle High German (gmh_Latn)
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Min Nan Chinese (nan_Latn)
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Moroccan Arabic (ary_Arab)
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Najdi Arabic (ars_Arab)
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Nepali (individual language) (npi_Latn)
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Nepali (individual language) (npi_Deva)
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Nigerian Pidgin (pcm_Latn)
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North Levantine Arabic (apc_Arab)
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Northern Uzbek (uzn_Latn)
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Northern Uzbek (uzn_Cyrl)
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Norwegian Bokmål (nob_Latn)
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Old High German (goh_Latn)
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Palatine German (pfl_Latn)
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Pennsylvania German (pdc_Latn)
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Persian (fas_Arab)
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Polish (pol_Latn)
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Portuguese (por_Latn)
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Romanian (ron_Latn)
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Russian (rus_Cyrl)
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Scots (sco_Latn)
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Serbian (srp_Cyrl)
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Serbian (srp_Latn)
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Sinhala (sin_Sinh)
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Slovak (slk_Latn)
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Somali (som_Latn)
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Southern Pashto (pbt_Arab)
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| Username |
Submissions |
| ihanif |
11 |
Spanish (spa_Latn)
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Standard Arabic (arb_Arab)
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Standard Arabic (arb_Arab)
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Standard Estonian (ekk_Latn)
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Standard Latvian (lvs_Latn)
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Standard Malay (zsm_Latn)
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Swedish (swe_Latn)
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Swiss German (gsw_Latn)
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Tamil (tam_Taml)
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Tatar (tat_Latn)
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Telugu (tel_Telu)
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Thai (tha_Thai)
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Tigrinya (tir_Ethi)
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Toki Pona (tok_Latn)
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Tunisian Arabic (aeb_Arab)
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Turkish (tur_Latn)
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Udmurt (udm_Cyrl)
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Ukrainian (ukr_Cyrl)
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Upper Sorbian (hsb_Latn)
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| Username |
Submissions |
| Korla |
27 |
Vietnamese (vie_Latn)
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West Flemish (vls_Latn)
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Yoruba (yor_Latn)
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Using this dataset
The dataset has a default config that contains all the language and configs per language.
To download the dataset using the Hugging Face datasets library, you can use the following code:
from datasets import load_dataset
dataset = load_dataset("data-is-better-together/fineweb-c-edu")
To download a specific language, you can use the following code:
dataset = load_dataset("data-is-better-together/fineweb-c-edu", name="cmn_Hani")
You can also download the dataset using Pandas
import pandas as pd
df = pd.read_parquet("hf://datasets/data-is-better-together/fineweb-c-edu/arb_Arab/train-00000-of-00001.parquet")
or polars
import polars as pl
df = pl.read_parquet('hf://datasets/davanstrien/fineweb-c-exported-data-test/arb_Arab/train-00000-of-00001.parquet')
Data Fields
The dataset contains the following columns:
| Column Name |
Type |
Description |
| id |
string |
A unique identifier for each annotation record |
| text |
string |
The text of the web page |
| educational_value_labels |
list[string] |
A list of labels indicating the educational value of the web page rated by the community |
| annotator_ids |
string |
A string ID for the annotator |
| problematic_content_label_present |
boolean |
A flag indicating the presence of at leaste one 'problematic' label being assigned to the text |
| problematic_content_label_agreement |
float |
The agreement of the annotator with the problematic content label |
| language_names |
str |
The name of the language page |
| language_code |
str |
The code of the language |
|
|
|
The main things to note (we'll update this as we get more data)
- Some languages already have multiple annotations per page. So far we haven't done any processing on these rows so people are free to calculate the agreement of the annotators in whatever way they want.
- For languages with many active annotators, we may increase the overlap of annotations over time to further improve the quality of the dataset.
- Some languages contain many
problematic content labels. These often occur when the language detection was not correct. There is a problematic_content_label_present boolean column that indicates if the page contains at least one problematic content label. If you want to remove these rows you can do so by filtering on this column. Alternatively, you can use the problematic_content_label_agreement column to filter on the agreement of the annotators i.e. only remove rows where the annotators agree on the problematic content label. For many of the most active language efforts we're working with the community to improve the quality of the data so we hope the number of problematic content labels will decrease over time.
Licensing Information
The dataset is released under the Open Data Commons Attribution License (ODC-By) v1.0 license. The use of this dataset is also subject to CommonCrawl's Terms of Use.
Citation
Citation information needs to be added
Last Updated
2025-07-08
