FineWeb-HQ datasets
Collection
Collection containing FineWeb-HQ and FineWeb2-HQ quality filtered datasets.
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3 items
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بنى المنزل من الطوب البحري. أما الأبواب والنوافذ صنعت من خشب الساج والتي تعتبر مادة باهظة الثمن استخدمت في بناء منازل الأثرياء. وبنيت الأسقف من حزم الصندل التي استوردت من الهند وشرق إفريقيا التي يبلغ طولها ثلاثة أمتار وقطرها 10 سنتيمترا، وغطيت بالحصير المنسوج وطبقة من الطين مختلطة بالقش تبلغ سماكتها 20 سنتيمترا. ولتوفير عازل من الرطوبة غطيت بطبقة من التراب. استخدم الطوب الأحمر لساحات الدار لأنها أقوى وقادرة على امتصاص الماء ومقاومة للحرارة. فاستخدام الطين في بناء المنزل أدى إلى انهياره عام 1945 بعد سقوط كبير للأمطار آنذاك.
والجدير بالذكر بأن هذا المنزل قد تم تحويله إلى مدرسة للبنات «مدرسة التربية النسوية»، وظلت تعرف بهذا الاسم حتى العام الدراسي 1949/1950 ثم أطلقت عليها دائرة المعارف اسم «المدرسة القبلية» نسبة إلى موقعها في الحي القبلي.
أصبح مبني المدرسة القبلية ضمن قائمة المباني التاريخية التي يشرف عليها المجلس الوطني للثقافة والفنون والآداب الذي قام بعمل صيانة جذرية للمبني شملت طلاء الجدران بالاسمنت وترميم المدخل الرئيسي وإغلاق الليوان بألواح زجاجية. كما تمت تغطية ساحات الدار بألواح عازلة غير قابلة للكسر والتي تسمح بدخول أشعة الشمس.
افتتح المجلس الوطني للثقافة والفنون والآداب المبني من جديد بعد إعادة ترميمه عام 2001 احتفالا بدولة الكويت عاصمة للثقافة العربية. ويعتبر المبنى احد المراكز الثقافية التابعة للمجلس الوطني. فهو مقر قطاع الآثار والمتاحف، والشؤون الهندسية فضلا عن القطاعات الثقافية الأخرى.
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<urn:uuid:ede5da81-714d-4067-9811-93dbaadb241e>
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2013-05-19T15:18:54Z
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CC-MAIN-2013-20
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مفاعل الماء الثقيل المضغوط
مفاعل الماء الثقيل المضغوط (بالإنجليزية: pressurised heavy water reactor) هو نوع من مفاعل نووي لإنتاج الطاقة الكهربائية، يستعمل عادة اليورانيوم الطبيعي (غير مخصب) كوقود نووي ويستخدم الماء الثقيل D2O (أكسيد الديوتيريوم)كمهدئ ومبرد في المفاعل. وطبقا لتصميم هذا المفاعل يبقى الماء الثقيل تحت ضغط عالي من أجل رفع درجة حرارة غليانه بحيث يمكن تسخينه إلى درجات حرارة أعلى بدون أن يغلي، وذلك كما يحدث في مفاعل الماء المضغوط.
وبينما يتكلف الماء الثقيل تكلفة باهظة عن الماء العادي إلا أن له ميزات بالنسبة إلى خصائصة بالنسبة إلى النيوترونات، فهو يمتص النيوترونات قليلا مما يجعله صالحا لتشغيل المفاعل باليورانيوم الطبيعي بدون تخصيب.
محتويات
استخدام الماء الثقيل [عدل]
في المفاعل النووي ينشطر أنوية اليورانيوم بواسطة امتصاصها النيوترونات وينتج عن الانشطار نيوترونات جديدة، تصتدم هي الأخرى بأنوية جديدة منتجة اشطارا، وطاقة، ونيوترونات. وبهذ يستمر التفاعل الذي يسمى تفاعل تسلسلي. ويتطلب ذلك توزيع هندسي للمواد في قلب المفاعل وتحكم في سير التفاعل، بحيث لا تضيع النيوترونات هباءا (إذا كان المفاعل صغير الحجم فهي تخرج منه بدون أن تحدث تفاعلا جديدا مع اليورانيوم ،أو تمتصها مادة من المواد الأخرى الموجودة في المفاعل). وبالتحكم (بواسطة قضبان تمتص النيوترونات) لا يتزايد عددها تصاعديا في المفاعل فيؤدي إلى انفجار خطير.
ويتكون اليورانيوم الطبيعي من مخلوط لعدة نظائر ويغلب فيه النظير يورانيوم-238 ونسبة صغيرة من اليورانيوم-235 تبلغ 7و0 % فقط. ينشطر اليورانيوم-238 بالنيوترونات السريعة ذات طاقة أكبر من 1 مليون إلكترون فولت MeV. ولا يمكن أن يستمر التفاعل مع اليورانيوم-238 حيث يمتص نيوترونات أكثر مما يصدره منها بالانشطار. ومن ناحية أخرى فإن اليورانيوم-235 هو الذي يُجري تفاعل تسلسلي، ولكن نسبته في اليورانيوم الطبيعي قليلة، فلا يمكن لليورانيوم الطبيعي القيام بتفاعل تسلسلي والوصول إلى الحالة الحرجة بمفرده.
وللتحايل على أن يعمل المفاعل يأتي عن طريق تهدئة سرعة النيوترونات بحيث يرتفع احتمال اصتدامها بأنوية اليورانيوم-235 مما يتيح الفرصة لاستمرار التفاعل في المفاعل. ولهذا نحتاج إلى مهدئ لسرعة النيوترونات، الذي يقوم بامتصاص جزءا من طاقة حركة النيوترونات فتنخفض سرعتها إلى سرعة جزيئات المهدئ نفسها (الماء، أو الماء الثقيل). ولهذا نسمي النيوترونات في تلك الحالة "النيوترونات الحرارية " thermal neutron وكذلك "المفاعل الحراري" thermal reactors.
فاستخدام الماء يكون مصحوبا بامتصاصه للنيوترونات وتبقى القليل منها للتفاعل مع كمية اليورانيوم-235 235U القليلة هي الأخرى في المفاعل، مما لا يسمح بالوصول إلى الحالة الحرجة في المفاعل (أن يؤدي كل نيوترون إلى تفاعل مع اليورانيوم-235 مع قيام 1 نيوترون من النيوترونات الناتجة عن الأنشطار بتفاعل ثان -رغما عن امتصاص النيترونات في المواد الأخرى وضياع بعضها بسبب انطلاقها خارج المفاعل - فيقوم هذا النيوترون بتفاعل تالي مع أحد أنوية اليورانيوم-235).
ولهذا يستخدم مفاعل الماء الخفيف يورانيوم مخصب، أي يورانيوم يحتوي على نسبة أعلى من اليورانيوم-235 تصل من 3% إلى 5 %. (ويسمي اليورانيوم المستهلك في عملية التخصيب يورانيوم منضب depleted uranium، حيث تنخفض فيه نسبة اليورانيوم-235 إلى نحو 07و0 %). وتعمل تلك ال 3% إلى 5 % من اليورانيوم-235 الموجودة في اليورانيوم المخصب على استمرار التفاعل في المفاعل والوصول إلى الحالة الحرجة .
مشكلة التخصيب [عدل]
يحتاج تخصيب اليورانيوم إلى بناء مصنع للتخصيب وهو أولا يتكلف مبالغ باهظة وثانيا يحتاج إلى تصريح في إطار المعاهدة الدولية لمنع انتشار السلاح النووي. وذلك لأنه يمكن تكبير المصنع بحيث ينتج ليس فقط يورانيوم مخصب بدرجة 5% وإنما أيضا بنسبة 90% من اليورانيوم-235 والذي تصنع منه القنابل الذرية.
استخدام اليورانيوم الطبيعي [عدل]
ويمكن حل مشكلة التخصيب واليورانيوم المخصب باستخدام اليورانيوم الطبيعي الذي يحتوي على 7و0% يورانيوم-235 مع الماء الثقيل كمهدئ لسرعة النيوترونات، وهو لا يمتص النيوترونات بالدرجة التي يمتصها الماء. ولذلك يصلح الماء الثقيل إيضا للعمل في مفاعل استنسال لإنتاج البلوتونيوم أو الليثيوم وهي مواد تدخل في صناعة الأسلحة الذرية.
مفاعل كاندو CANDU [عدل]
تصميم المفاعل
|1||حزمة الوقود||8||ألة تموين الوقود|
|2||قلب المفاعل||9||الماء الثقيل|
|3||قضبان التحكم||10||أنبوب الضغط|
|4||خزان الماء الثقيل||11||بخار خارج لتشغيل التوربين|
|5||مولد البخار||12||عودة الماء البارد من التوربين|
|6||طلمبة ماء عادي||13||مبني المفاعل لمنع الإشعاع إلى الخارج|
|7||طلمبة الماء الثقيل|
ويماثل مفاعل الماء الثقيل المضغوط تقريبا مفاعل الماء المضغوط المعتاد، إلا أنه يختلف عنه في بعض التفاصيل.
يقوم التفاعل النووي بتوليد حرارة في قلب المفاعل وتعتبر تتك الدورة هي الدورة الابتدائية ذات ضغط مرتفع. ويعمل المبادل الحراري على نقل الحرارة إلى الدورة الثانوية التي تعمل بالماء العادي وإنتاج البخار، وهو الذي يقوم بتدوير التوربين المتصل بمولد كهربائي.
بعد خروج بخار الماء من التوربين يُكثف في مكثف للبخار بواسطة ماء كثير مأخود من نهر مجاور، ويعاد الماء ثانيا إلى المبادل الحراري لتكملة الدورة. ويمكن الاستعاضة عن التبريد بواسطة ماء النهر وذلك باستخدام برج تبريد.
انظر أيضا [عدل]
- مفاعل ماء خفيف
- مفاعل الماء المغلي
- مفاعل الماء المضغوط
- مفاعل استنسال سريع
- مفاعل سريع بتبريد الرصاص
- مفاعل ملح منصهر
- مفاعل بتبريد غازي تقدمي
- قائمة الحوادث النووية المدنية
- سلاح نووي
- تقنية نووية
- مفاعل كاندو
|
<urn:uuid:ee0a4066-ea59-49b7-a972-f0e712c9a823>
|
2013-05-22T11:32:47Z
|
CC-MAIN-2013-20
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← اللغة القبطية: peniwt abba Danihl pi\hgoumenoc.
تحمل سيرة هذا القديس شهادة حيّة عن حيوية الرهبنة القبطية ومجدها في القرن السادس. كان هذا القديس أبًا طاهرًا كاملًا، خدم بلاده أعظم خدمة بأن قدم للكنيسة أساقفة أعلام من خلال برامج الدراسة التي كانت تُقَدم في الأديرة بهدف إعداد الرهبان لحسن تأدية رسالتهم، والذي كان هذا القديس أحد القائمين بالتعليم فيها مع غيره من الشيوخ الرهبان الذين اشتهروا بالعلم والتقوى. ليس ذلك فحسب بل أن رعايته شملت المتبتلات أيضًا فرعاهن أحسن رعاية.
أسره:
وُلد سنة 485 م.، وارتحل إلى برية شيهيت وهو صبي. وقع تحت الأسر ثلاث مرّات في رهبنته المبكرة، ربما بسبب شغفه نحو الدخول إلى البرّية الداخلية.
في المرة الثالثة صرع أسيره بحجر فأرداه قتيلًا وهرب. لكنه ظل تحت عذاب الضمير طوال حياته. طلب من البابا تيموثاوس الثالث (518-536 م.) عقوبة. وإذ حاول أن يقنعه بأن ذلك كان دفاعًا عن النفس، لأنه كان مرّ النفس جدًا، ولكنه لم يقتنع، فرحل إلى روما والقسطنطينية ثم أفسس وأورشليم وإنطاكية يسأل أساقفة هذه البلاد. وأخيرًا إذ لم يسترح سلّم نفسه للقضاء بالإسكندرية، لكن القاضي أخلى سبيله متمنيًا لو قتل سبعة من هؤلاء البرابرة العنفاء.
مع الشريفة أناسطاسية:
لما شاع صيته أتت إليه الشريفة أناسطاسية إحدى الشماسات اللواتي هربن مع القديس أنبا ساويرس الأنطاكي إلى مصر سنة 518 م. وكانت غنية جدًا. جاءت متخفية في زي الرجال وأعلمته بحقيقة أمرها، كما أنبأته بأنها هربت من وجه إمبراطور القسطنطينية الذي أراد الزواج منها. فترهبت ومكثت في مغارة بقربه مدة ثمانية وعشرين عامًا ولم يعلم بأمرها أحد. وكان أحد تلاميذ القمص دانيال يحمل إليها الخبز والماء مرة أسبوعيًا ويتركهما عند باب مغارتها. وكانت كلما ساورتها الهواجس والهموم وصفت جميع ما خالجها من مشاعر بكتابته على حجر وتتركه عند باب المغارة، فيأخذه التلميذ إلى القديس دانيال الذي يكتب لها بدوره الرد ويرسله مع التلميذ. وكان القديس يختار رسوله ممن يجهلون اليونانية التي تكتب بها القديسة أناسطاسية، فتظل اعترافاتها سرًا مكتومًا.
مع أولوجيوس قاطع الأحجار:
في حوالي عام 525 م.، حدث ذات يوم أن ذهب القمص دانيال إلى المدينة ليبيع السلال التي صنعها بيديه، فأبصر إنسانًا اسمه أولوجيوس كان يقطع حجارة كل يوم بقيراط ذهب، فيقتات منه باليسير ويطعم الفقراء بالباقي ولم يكن يدخر شيئًا. فلما أبصر القديس ذلك استحسن سيرته وطلب من الله أن يعطيه مالًا ليزداد في عمل الخير والرحمة، وسمع له الله.
وجد أولوجيوس كنزًا أثناء عمله في الحجارة وأخذه ومضى إلى القسطنطينية، وسعى بماله حتى صار وزيرًا وترك عمل الخير. (ستجد المزيد عن هؤلاء القديسين هنا في موقع الأنبا تكلا في أقسام السير والسنكسار والتاريخ). فلما سمع بخبره القديس دانيال قصد القسطنطينية، وعرف سيرته وما صار إليه من عدم الخير. حاول القديس الالتقاء به لكن الجند منعوه. ثم رأى رؤيا كأن السيد المسيح جالس يحكم بين الناس، وكأنه أمر بتعليق القديس دانيال وطالبه بنفس أولوجيوس. ولما استيقظ من نومه عاد إلى ديره وسأل الله عن أولوجيوس أن يعيده إلى ما كان عليه. فظهر له ملاك الرب ونهاه أن يتعرض لحكم الله في خلقه.
بعد هذا حدثت مؤامرة ضد يوستنيان سنة 532 م.، اشترك فيها أولوجيوس، وإذ أراد الإمبراطور البطش به هرب من القسطنطينية إلى مصر لينجو بنفسه. عاد إلى بلده يقطع الحجارة كما كان أولًا. فاجتمع به الأنبا دانيال وقصَّ عليه ما حل به بسببه، فعزاه وسنده ورده إلى محبة الفقراء.
القديسة أناسيمون:
كشف القديس دانيال عن شخصية القديسة أناسيمون الملكة التي تخفّت في شخصية هبيلة لتعيش محتقرة في إحدى أديرة البنات بمصر. كما كشف عن شخصية قديس عظيم يُسمى مرقس كان يتسوّل أمام دار البطريركية.
اللص التائب:
أراد لص أن يسرق أموال دير للراهبات، فتقمص شخصية أنبا دانيال، ودخل الدير ليلًا. سألته الراهبات أن يصلّي من أجل راهبة عمياء، وإذ لم يكن مسيحيًا قال لهن أن يغسلن وجهها بالماء الذي غسلن به قدميه. وإذ انفتحت عيناها تأثر اللص جدًا، وخرج إلى القديس أنبا دانيال الذي استقبله ببشاشة وأعلمه أنه كان معه بالروح حين دخل دير الراهبات. آمن اللص بالمسيحية وتتلمذ على يدي أنبا دانيال.
تعرضه للاضطهاد:
تعرض القديس للضرب حتى كاد يفارق الحياة، وذلك لأنه شجب طومس لاون وعقيدة مجمع خلقيدونية أمام مندوب الملك يوستنيان. اضطر إلى الهروب إلى مدينة تامبولا (حاليًا مركز شبراخيت) حيث أقام بجوارها ديرًا مكث فيه حتى مات يوستنيان عام 565 م.
مع أندرونيكس وزوجته أثناسيا:
كانا أندرونيكس وزوجته أثناسيا من أثرياء إنطاكية، مات أولادهما جميعًا فجأة فدخلا في حزنٍ شديدٍ. لكنهما عادا إلى حياة التسليم للَّه، وقرّرا الرهبنة.
جاء الزوج إلى القديس أنبا دانيال الذي نصحه أن يستودع زوجته في إحدى أديرة الراهبات ثم يعود إليه. تتلمذ على يدي أنبا دانيال لمدة 12 عامًا. وإذ أراد زيارة الأماكن المقدسة التقى براهبٍ يود الذهاب إلى أورشليم، فتحدثا معًا في الطريق، وتعاهدا أن يعيشا معًا عند عودتهما. وبالفعل سكنا في الدير الثامن عشر بالإسكندرية (اوكتوكا يديكاثون). وبقيا هناك 12 عامًا، وكان القديس دانيال يفتقدهما.
أخيرًا تنيح الراهب واكتشف أندرونيكوس أن الراهب هو زوجته أثناسيا، فأقام في قلايتها.
اهتمامه بالرهبان والراهبات:
عاش القديس دانيال قمص شيهيت أربعين سنة ونصف في الصحاري مداومًا على الصلاة والصوم، مهتمًا بالرهبان والراهبات الخاضعين لرئاسته. وقد نال هذا القديس أحزانًا كثيرةً في سبيل الإيمان، وأظهر الله على يديه آيات كثيرة، وعرف زمان انتقاله من هذا العالم، فجمع الرهبان وأوصاهم وثبتهم وعزاهم ثم تنيّح بسلام في الثامن من شهر بشنس.
* يُكتَب خطأ: الأنبا دانيال الأسفيطي بدلًا من الإسقيطي.
_____
* المرجعReference (الذي استخدمه كتاب "قاموس آباء الكنيسة وقديسيها مع بعض شخصيات كنسية" للقمص تادرس يعقوب ملطي):
القمص تادرس: الكنيسة القبطية الأرثوذكسية والروحانية، 1987م، ص70.
[ http://St-Takla.org ]
أسماء القديسين بالحروف الأبجدية:
أ |
ب |
ت |
ث |
ج |
ح |
خ |
د |
ذ |
ر |
ز |
س |
ش |
ص
ض | ط | ظ | ع | غ | ف | ق | ك | ل | م | ن | ه | و | ي
* صور القديسين والشهداء في قسم الجاليرى
* قسم المدائح و الترتيل المكتوبة وبها مدائح لبعض الآباء القديسين و القديسات
* إرسل كروت تهنئة في أعياد القديسين
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© كنيسة الأنبا تكلا هيمانوت الحبشي القس - الإسكندرية - مصر / URL: http://St-Takla.org / اتصل بنا على:
http://st-takla.org/Saints/Coptic-Orthodox-Saints-Biography/Coptic-Saints-Story_893.html
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2013-05-22T11:32:39Z
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CC-MAIN-2013-20
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End of preview. Expand
in Data Studio
FineWeb2-HQ
Dataset summary
FineWeb2-HQ is a high-quality, model-filtered pretraining dataset derived as a subset of FineWeb2, spanning 20 languages. It enables around 6x faster pretraining compared to the base dataset. FineWeb2-HQ was created by selecting the top 10% quality documents of FineWeb2 in each language, based on scores assigned by a deep learning classifier trained to identify structured and knowledge-rich samples using XLM-RoBERTa embeddings.
Validation was performed by pretraining 1B-parameter LLM models (llama-like architecture) across multiple languages and writing systems (scripts). Evaluations on CMMLU (Chinese) and MMLU (German & French) demonstrate that FineWeb2-HQ matches FineWeb2 performance when trained with 6x fewer tokens, and outperforms it when fully trained. Additionally, improvements were observed across other benchmarks, such as outperforming its English cousins DCLM and FineWeb-Edu.
For more details, see our paper Enhancing Multilingual LLM Pretraining with Model-Based Data Selection.
Key features
- High-quality selection: Top 10% of FineWeb2 documents by quality
- Multilingual coverage: 20 languages, ensuring diverse linguistic representation
- Model-based filtering: Uses an XLM-RoBERTa embedding-based classifier to score documents
- Enhanced benchmark performance: Surpasses FineWeb2 benchmark performance
- Fully open: Emphasis on transparency
Languages and subsets
| Subset name | Language name | Number of documents | Disk size |
|---|---|---|---|
| rus_Cyrl | Russian | 55,220,956 | 1.2T |
| cmn_Hani | Chinese | 54,211,986 | 784G |
| deu_Latn | German | 43,095,728 | 618G |
| spa_Latn | Spanish | 40,057,637 | 515G |
| jpn_Jpan | Japanese | 34,185,427 | 393G |
| fra_Latn | French | 32,248,772 | 483G |
| ita_Latn | Italian | 21,180,304 | 269G |
| por_Latn | Portuguese | 18,135,468 | 222G |
| pol_Latn | Polish | 13,384,885 | 168G |
| nld_Latn | Dutch | 12,920,963 | 160G |
| ind_Latn | Indonesian | 8,911,149 | 125G |
| tur_Latn | Turkish | 8,578,808 | 100G |
| ces_Latn | Czech | 5,995,459 | 104G |
| arb_Arab | Arabic | 5,560,599 | 94G |
| fas_Arab | Persian | 5,107,187 | 69G |
| hun_Latn | Hungarian | 4,527,332 | 79G |
| swe_Latn | Swedish | 4,382,454 | 61G |
| ell_Grek | Greek | 4,346,440 | 84G |
| dan_Latn | Danish | 4,082,751 | 61G |
| vie_Latn | Vietnamese | 4,003,956 | 59G |
The approach as described in the paper is easy to extend to other languages as well, and we might consider adding new languages to an upcoming version of the present dataset.
We also separately release the computed general-purpose embedding vectors for the the full sets of the original FineWeb2 dataset (not just the HQ subsets), in the respective languages, as they can be useful for other applications beyond quality filtering: FineWeb2-embedded.
Dataset structure
Data fields
Each data entry includes the original FineWeb2 data fields with the addition of:
quality_score: quality score obtained by the quality classifierembeddings: array of float arrays containing 768-dimensional XLM-RoBERTa embeddings for every 512 token chunk of the tokenized text
Data instance
{
"id": "<urn:uuid:f26003c7-6084-4791-b3fe-240eedc37e76>",
"text": "Plutonium ist einer der gefährlichsten Stoffe der Welt. Es entsteht als hochgiftiges und radioaktives Nebenprodukt der Energiegewinnung in Atomkraftwerken. Wer nur ein Millionstel Gramm – ein kaum staubkorngroßes Teilchen – der Substanz einatmet, kann daran sterben. In der Natur kommt der Stoff nur in geringsten Mengen vor, wird aber künstlich hergestellt, weil man damit Bomben bauen kann. Je nach Reinheitsgrad reichen für eine Atombombe bereits fünf Kilogramm. Bis zum Beginn der achtziger Jahre des letzten Jahrhunderts hatten die Reaktoren weltweit bereits rund 300.000 Kilogramm erbrütet. Jährlich kommen etwa 20.000 Kilo hinzu. Genau dieser Stoff wird zu Land und zu Wasser um den ganzen Erdball herum transportiert. Legendär sind die Castor-Transporte, bei denen unter strengsten Sicherheitsvorkehrungen und entsprechenden Kosten abgebrannte Brennelemente aus deutschen Kernkraftwerken zur Wiederaufbereitung nach La Hague (Frankreich) oder Sellafield (Großbritannien) gebracht werden. Erst vergangenen Mai hat ein Frachter die größte Menge wiederaufbereiteten Mülls aller Zeiten von Frankreich nach Japan gebracht. Nicht auszudenken, was ein Unfall auf See bedeuten würde.",
"date": "2014-03-16T08:53:38Z",
"dump": "CC-MAIN-2014-10",
"embeddings": [[ ... ]],
"file_path": "s3://commoncrawl/crawl-data/CC-MAIN-2014-10/segments/1394678702159/warc/CC-MAIN-20140313024502-00039-ip-10-183-142-35.ec2.internal.warc.gz",
"language": "deu",
"language_score": 0.9983288645744324,
"language_script": "Latn",
"minhash_cluster_size": 2,
"top_langs": {"deu_Latn_score": 0.9983288645744324},
"url": "http://www.greenpeace.org/austria/de/themen/atom/probleme/atomtransporte/",
"quality_score": 0.06472613662481308
}
Usage
You can load the dataset in Python using datasets:
from datasets import load_dataset
dataset = load_dataset("epfml/FineWeb2-HQ", "deu_Latn")
Licensing information
Like FineWeb2, this dataset is released under Open Data Commons Attribution License (ODC-By) v1.0 license and is subject to CommonCrawl's Terms of Use.
Dataset origin
Being a subset of FineWeb2, this data covers websites over the 2013-2024 time period.
FineWeb2 is sourced from the internet at large, it is very likely that some personable identifiable information (PII) will be present, even if the FineWeb2 processing has already anonymized email addresses and public IP addresses. If you find your own PII and would like it removed, please fill out the FineWeb2 PII removal/opt out form.
CommonCrawl respects robots.txt at crawl time, but if you are a webmaster and find your website in FineWeb2 and would like to have it removed, you may also use the FineWeb2 PII removal/opt out form.
Considerations for Using the Data
Before using this dataset for training models, we recommend performing additional filtering for sensitive content such as PII or harmful content. For the aspects of social impact, discussion of biases, and known limitations, we also refer to the FineWeb2 documentation.
Citation information
If you use this dataset in your research or applications, please use the following citation:
@article{messmer2025multilingdatacomp,
title={Enhancing Multilingual LLM Pretraining with Model-Based Data Selection},
author={Bettina Messmer and Vinko Sabolčec and Martin Jaggi},
journal={arXiv},
year={2025},
url={https://arxiv.org/abs/2502.10361},
}
- Downloads last month
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Size of downloaded dataset files:
6.04 TB
Size of the auto-converted Parquet files:
6.04 TB
Number of rows:
380,138,261