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data/retrieval_battle-a660d6d7-6b46-4e6b-9c85-4651683faa00.jsonl

@@ -24,3 +24,4 @@
 {"tstamp": 1726227813.0441, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "a8c037585f6d4f068c9084f64f149988", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "merhaba, bugün günlerden cuma ve yarın haftasonu.", "0_output": [["merhaba, bugün günlerden cuma ve yarın haftasonu.", "Alparslan: Büyük Selçuklu\n\nRecurring Kaan Yalçin as Kavurd Bey, a Seljuk Melik, Emir of Kirman, eldest son of Çağrı Bey, nephew of Tuğrul Bey and elder brother of Yakuti, Alparslan, Begim Şehver, Süleyman, Hatice Arslan and Gevher Hatun. Father of Melik Merdan.\nBetül Çobanoğlu as Selcan Hatun: wife of Çağrı Bey. Mother of Suleyman, Yakuti, Begim Sehver, Gevher and Hatice Arslan. Raised Alparslan as her own son.\nHakan Şahin as Kündürî. (deceased)\nEsila Umut as Gevher Hatun: sister of Alparslan, Kavurd, Süleyman and Begim Şehver, wife of Erbaskan.\nFerit Kaya as Erbaskan: paternal half brother of Ibrahim Yinal, Husband of Gevher. (deceased)\nBurak Ali Özkan as Shehzade Mesud, son of the Ghaznavid State emperor Sultan Mevdud and Begim Sehver Sultan, grandson of Çağrı Bey, nephew of Alparslan.\nAyşegül Ünsal as Akınay Hatun, mother of Alpagut, widow of Temurleng.\nBurak Şafak as Avar Bey.\nOnur Aycelik as Artuk Bey.\nKutay Sungar as Atsız Bey.\nEmre Bulut as Afşin Bey.\nMerve Nil Güder as Gülce Hatun, daughter of Emir Arslan Yusuf, younger sister of Seferiye Hatun.\nCihan Muğlu (kid) as Serdar Alp.\nBora Cengiz as Alexander. Son of Tekfur Grigor, Brother of Flora.(deceased)\nAnil Özdemirci as Süleyman Şah, based from Suleiman ibn Qutalmish, son of Kutalmiş Bey, Brother of Melik Mansur.\nDoğukan Çevik as Melik Mansur, son of Kutalmiş Bey, Brother of Süleyman Şah.\nKanbolat Görkem Arslan as Kutalmiş Bey, based from Qutalmish, Father of Süleyman Şah and Melik Mansur. (deceased)"]], "0_corpus": "wikipedia", "1_conv_id": "0b726b19d71a4d1a964be8adeb3749dd", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "merhaba, bugün günlerden cuma ve yarın haftasonu.", "1_output": [["merhaba, bugün günlerden cuma ve yarın haftasonu.", "Friday\n\nIslam In Islam, Friday (from sun-down Thursday to sun-down Friday) is the day of communion, of praying together, the holy day of Muslims. Friday observance includes attendance at a Masjid (mosque) for congregation prayer or Salat Al Jumu'ah. It is considered a day of peace and mercy (see Jumu'ah).\nAccording to some Islamic traditions, the day is stated to be the original holy day ordained by God, but that now Jews and Christians recognize the days after. In some Islamic countries, the week begins on Sunday and ends on Saturday, just like the Jewish week and the week in some Christian countries. The week begins on Saturday and ends on Friday in most other Islamic countries, such as Somalia, and Iran. Friday is also the day of rest in the Baháʼí Faith. In some Malaysian states, Friday is the first week-end day, with Saturday the second, to allow Muslims to perform their religious obligations on Friday. Sunday is the first working day of the week for governmental organizations.\nMuslims are recommended not to fast on a Friday by itself (makruh, recommended against, but not haram, religiously forbidden), unless it is accompanied with fasting the day before (Thursday) or day after (Saturday), or it corresponds with days usually considered good for fasting (i.e. Day of Arafah or Ashura), or it falls within one's usual religious fasting habits (i.e. fasting every other day), then it's completely permissible."]], "1_corpus": "wikipedia"}
 {"tstamp": 1726248540.5499, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "aa14f43a4a1544119d587fa7ff56a9cc", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "central idea of poem lines from the deserted village", "0_output": [["central idea of poem lines from the deserted village", "Elegy Written in a Country Churchyard\n\nPoetic parallels In choosing an \"English\" over a Classical setting, Gray provided a model for later poets wishing to describe England and the English countryside during the second half of the 18th century.  Once Gray had set the example, any occasion would do to give a sense of the effects of time in a landscape, as for instance in the passage of the seasons as described in John Scott's Four Elegies, descriptive and moral (1757). Other imitations, though avoiding overt verbal parallels, chose similar backgrounds to signal their parentage. One favourite theme was a meditation among ruins, such as John Langhorne's Written among the ruins of Pontefract Castle (1756), Edward Moore's \"An elegy, written among the ruins of a nobleman's seat in Cornwall\" (1756) and John Cunningham's \"An elegy on a pile of ruins\" (1761). Gray's friend William Mason chose an actual churchyard in south Wales for his Elegy VI (1787), adding a reference to the poet in the text. He also provided a final note explaining that the poem was written \"to make it appear a day scene, and as such to contrast it with the twilight scene of my excellent Friend's Elegy\".\nA kinship between Gray's Elegy and Oliver Goldsmith's The Deserted Village has been recognised, although the latter was more openly political in its treatment of the rural poor and used heroic couplets, where the elegist poets kept to cross-rhymed quatrains. At first it was collected in various editions along with Gray's poem and other topographical works, but from 1873 a number of editions appeared which contained just the Elegy and The Deserted Village, though sometimes with the inclusion of Goldsmith's The Traveller or some other single work as well. At that period an anonymous review in The Academy (12 December 1896) claimed that \"Gray's 'Elegy' and Goldsmith's 'The Deserted Village' shine forth as the two human poems in a century of artifice.\""]], "0_corpus": "wikipedia", "1_conv_id": "3e57275f681f428895625bc7b255bb74", "1_model_name": "text-embedding-3-large", "1_prompt": "central idea of poem lines from the deserted village", "1_output": [["central idea of poem lines from the deserted village", "Elegy Written in a Country Churchyard\n\nPoem The poem begins in a churchyard with a speaker who is describing his surroundings in vivid detail. The speaker emphasises both aural and visual sensations as he examines the area in relation to himself:\nThe curfew tolls the knell of parting day,\nThe lowing herd wind slowly o'er the lea,\nThe ploughman homeward plods his weary way,\nAnd leaves the world to darkness and to me.\nNow fades the glimmering landscape on the sight,\nAnd all the air a solemn stillness holds,\nSave where the beetle wheels his droning flight,\nAnd drowsy tinklings lull the distant folds:\nSave that from yonder ivy-mantled tow'r\nThe moping owl does to the moon complain\nOf such, as wand'ring near her secret bow'r,\nMolest her ancient solitary reign. (lines 1–12)\nAs the poem continues, the speaker begins to focus less on the countryside and more on his immediate surroundings. His descriptions move from sensations to his own thoughts as he begins to emphasise what is not present in the scene; he contrasts an obscure country life with a life that is remembered. This contemplation provokes the speaker's thoughts on the natural process of wastage and unfulfilled potential.\nFull many a gem of purest ray serene\nThe dark unfathom'd caves of ocean bear:\nFull many a flower is born to blush unseen,\nAnd waste its sweetness on the desert air."]], "1_corpus": "wikipedia"}
 {"tstamp": 1726255059.6595, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "9a563ac9388846b891675e80e8b1460d", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "https://arxiv.org/pdf/2004.12832", "0_output": [["https://arxiv.org/pdf/2004.12832", "Title: Deep Generative Models for Ultra-High Granularity Particle Physics Detector Simulation: A Voyage From Emulation to Extrapolation\n\nAbstract: Simulating ultra-high-granularity detector responses in Particle Physics represents a critical yet computationally demanding task. This thesis aims to overcome this challenge for the Pixel Vertex Detector (PXD) at the Belle II experiment, which features over 7.5M pixel channels-the highest spatial resolution detector simulation dataset ever analysed with generative models. This thesis starts off by a comprehensive and taxonomic review on generative models for simulating detector signatures. Then, it presents the Intra-Event Aware Generative Adversarial Network (IEA-GAN), a new geometry-aware generative model that introduces a relational attentive reasoning and Self-Supervised Learning to approximate an \"event\" in the detector. This study underscores the importance of intra-event correlation for downstream physics analyses. Building upon this, the work drifts towards a more generic approach and presents YonedaVAE, a Category Theory-inspired generative model that tackles the open problem of Out-of-Distribution (OOD) simulation. YonedaVAE introduces a learnable Yoneda embedding to capture the entirety of an event based on its sensor relationships, formulating a Category theoretical language for intra-event relational reasoning. This is complemented by introducing a Self-Supervised learnable prior for VAEs and an Adaptive Top-q sampling mechanism, enabling the model to sample point clouds with variable intra-category cardinality in a zero-shot manner. Variable Intra-event cardinality has not been approached before and is vital for simulating irregular detector geometries. Trained on an early experiment data, YonedaVAE can reach a reasonable OOD simulation precision of a later experiment with almost double luminosity. This study introduces, for the first time, the results of using deep generative models for ultra-high granularity detector simulation in Particle Physics."]], "0_corpus": "arxiv", "1_conv_id": "03513db2e8d248dfb044d972e8176a2e", "1_model_name": "text-embedding-3-large", "1_prompt": "https://arxiv.org/pdf/2004.12832", "1_output": [["https://arxiv.org/pdf/2004.12832", "Title: Title Redacted\n\nAbstract: Abstract redacted by arXiv administrators."]], "1_corpus": "arxiv"}
+{"tstamp": 1726258725.1016, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "9fcb9af5effe4e8fa755d1219dc54bda", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "nutritional facts for an apple", "0_output": [["nutritional facts for an apple", "Apple\n\nIn other areas, such as the Mediterranean, some individuals have adverse reactions to apples because of their similarity to peaches. This form of apple allergy also includes OAS, but often has more severe symptoms, such as vomiting, abdominal pain and urticaria, and can be life-threatening. Individuals with this form of allergy can also develop reactions to other fruits and nuts. Cooking does not break down the protein causing this particular reaction, so affected individuals cannot eat raw or cooked apples. Freshly harvested, over-ripe fruits tend to have the highest levels of the protein that causes this reaction.\nBreeding efforts have yet to produce a hypoallergenic fruit suitable for either of the two forms of apple allergy.\nUses\nNutrition\nA raw apple is 86% water and 14% carbohydrates, with negligible content of fat and protein (table). A reference serving of a raw apple with skin weighing 100 grams provides 52 calories and a moderate content of dietary fiber. Otherwise, there is low content of micronutrients, with the Daily Values of all falling below 10%.\nCulinary\nAll parts of the fruit, including the skin, except for the seeds, are suitable for human consumption. The core, from stem to bottom, containing the seeds, is usually not eaten and is discarded.\nApples can be consumed in various ways: juice, raw in salads, baked in pies, cooked into sauces and spreads like apple butter, and other baked dishes.\nApples are sometimes used as an ingredient in savory foods, such as sausage and stuffing.\nSeveral techniques are used to preserve apples and apple products. Apples can be canned, dried or frozen. Canned or frozen apples are eventually baked into pies or other cooked dishes. Apple juice or cider is also bottled. Apple juice is often concentrated and frozen."]], "0_corpus": "wikipedia", "1_conv_id": "2eb540b916b44f86ae031c15d08fb748", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "nutritional facts for an apple", "1_output": [["nutritional facts for an apple", "An apple a day keeps the doctor away\n\nOrigin A variant of the proverb, \"Eat an apple on going to bed, and you'll keep the doctor from earning his bread\" was recorded as a Pembrokeshire saying in 1866. The modern phrasing, \"An apple a day keeps the doctor away\", began usage at the end of the 19th century, with early print examples found as early as 1887.\nScientific background\nA 2013 study using computer modelling compared eating apples with taking a common daily cholesterol-lowering drug to estimate risk of cardiovascular diseases. The computer model estimated that eating an apple a day was generally comparable for people over age 50 years to using a statin drug to reduce low-density lipoprotein cholesterol, concluding that eating an apple a day \"is able to match modern medicine and is likely to have fewer side effects,\" while having similar annual cost.\nA 2015 study found apple eaters \"were more likely, in the crude analysis, to keep the doctor (and prescription medications) away.\"  When they adjusted for \"sociodemographic and health-related characteristics, however, the association was no longer statistically significant\". The study also found that people who ate an apple a day used fewer prescription medications.\nNutritional content of an apple\nA medium-size (100 gram) raw apple is 86% water and 14% carbohydrates with negligible content of fat and protein, and supplies 52 calories of food energy. It contains a moderate amount of dietary fiber, but otherwise has a low level of micronutrients."]], "1_corpus": "wikipedia"}

data/retrieval_individual-a660d6d7-6b46-4e6b-9c85-4651683faa00.jsonl

@@ -90,3 +90,5 @@
 {"tstamp": 1726255116.5874, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1726255116.4089, "finish": 1726255116.5874, "ip": "", "conv_id": "d1bd637a2b4f4a52bdae67b018039865", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Like Sparrows on a Clothes Line: The Self-Organization of Random Number Sequences\n\nAbstract: We study sequences of random numbers {Z[1],Z[2],Z[3],...,Z[n]} -- which can be considered random walks with reflecting barriers -- and define their \"types\" according to whether Z[i] > Z[i+1], (a down-movement), or Z[i] < Z[i+1] (up-movement). This paper examines the means, xi, to which the Zi converge, when a large number of sequences of the same type is considered. It is shown that these means organize themselves in such a way that, between two turning points of the sequence, they are equidistant from one another. We also show that m steps in one direction tend to offset one step in the other direction, as m -> infinity. Key words:random number sequence, self-organization, random walk, reflecting barriers."]], "corpus": "arxiv"}
 {"tstamp": 1726255754.4414, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1726255754.2168, "finish": 1726255754.4414, "ip": "", "conv_id": "999321227dba443b9ea3f4a6d7fe56b2", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "text to video generation paper", "output": [["text to video generation paper", "Title: Video Generation from Text Employing Latent Path Construction for Temporal Modeling\n\nAbstract: Video generation is one of the most challenging tasks in Machine Learning and Computer Vision fields of study. In this paper, we tackle the text to video generation problem, which is a conditional form of video generation. Humans can listen/read natural language sentences, and can imagine or visualize what is being described; therefore, we believe that video generation from natural language sentences will have an important impact on Artificial Intelligence. Video generation is relatively a new field of study in Computer Vision, which is far from being solved. The majority of recent works deal with synthetic datasets or real datasets with very limited types of objects, scenes, and emotions. To the best of our knowledge, this is the very first work on the text (free-form sentences) to video generation on more realistic video datasets like Actor and Action Dataset (A2D) or UCF101. We tackle the complicated problem of video generation by regressing the latent representations of the first and last frames and employing a context-aware interpolation method to build the latent representations of in-between frames. We propose a stacking ``upPooling'' block to sequentially generate RGB frames out of each latent representation and progressively increase the resolution. Moreover, our proposed Discriminator encodes videos based on single and multiple frames. We provide quantitative and qualitative results to support our arguments and show the superiority of our method over well-known baselines like Recurrent Neural Network (RNN) and Deconvolution (as known as Convolutional Transpose) based video generation methods."]], "corpus": "arxiv"}
 {"tstamp": 1726255754.4414, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1726255754.2168, "finish": 1726255754.4414, "ip": "", "conv_id": "bc46423a73e745c8a7714604506671fe", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "text to video generation paper", "output": [["text to video generation paper", "Title: Make-A-Video: Text-to-Video Generation without Text-Video Data\n\nAbstract: We propose Make-A-Video -- an approach for directly translating the tremendous recent progress in Text-to-Image (T2I) generation to Text-to-Video (T2V). Our intuition is simple: learn what the world looks like and how it is described from paired text-image data, and learn how the world moves from unsupervised video footage. Make-A-Video has three advantages: (1) it accelerates training of the T2V model (it does not need to learn visual and multimodal representations from scratch), (2) it does not require paired text-video data, and (3) the generated videos inherit the vastness (diversity in aesthetic, fantastical depictions, etc.) of today's image generation models. We design a simple yet effective way to build on T2I models with novel and effective spatial-temporal modules. First, we decompose the full temporal U-Net and attention tensors and approximate them in space and time. Second, we design a spatial temporal pipeline to generate high resolution and frame rate videos with a video decoder, interpolation model and two super resolution models that can enable various applications besides T2V. In all aspects, spatial and temporal resolution, faithfulness to text, and quality, Make-A-Video sets the new state-of-the-art in text-to-video generation, as determined by both qualitative and quantitative measures."]], "corpus": "arxiv"}
+{"tstamp": 1726258629.7804, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1726258627.4162, "finish": 1726258629.7804, "ip": "", "conv_id": "9fcb9af5effe4e8fa755d1219dc54bda", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "nutritional facts for an apple", "output": [["nutritional facts for an apple", "Apple\n\nIn other areas, such as the Mediterranean, some individuals have adverse reactions to apples because of their similarity to peaches. This form of apple allergy also includes OAS, but often has more severe symptoms, such as vomiting, abdominal pain and urticaria, and can be life-threatening. Individuals with this form of allergy can also develop reactions to other fruits and nuts. Cooking does not break down the protein causing this particular reaction, so affected individuals cannot eat raw or cooked apples. Freshly harvested, over-ripe fruits tend to have the highest levels of the protein that causes this reaction.\nBreeding efforts have yet to produce a hypoallergenic fruit suitable for either of the two forms of apple allergy.\nUses\nNutrition\nA raw apple is 86% water and 14% carbohydrates, with negligible content of fat and protein (table). A reference serving of a raw apple with skin weighing 100 grams provides 52 calories and a moderate content of dietary fiber. Otherwise, there is low content of micronutrients, with the Daily Values of all falling below 10%.\nCulinary\nAll parts of the fruit, including the skin, except for the seeds, are suitable for human consumption. The core, from stem to bottom, containing the seeds, is usually not eaten and is discarded.\nApples can be consumed in various ways: juice, raw in salads, baked in pies, cooked into sauces and spreads like apple butter, and other baked dishes.\nApples are sometimes used as an ingredient in savory foods, such as sausage and stuffing.\nSeveral techniques are used to preserve apples and apple products. Apples can be canned, dried or frozen. Canned or frozen apples are eventually baked into pies or other cooked dishes. Apple juice or cider is also bottled. Apple juice is often concentrated and frozen."]], "corpus": "wikipedia"}
+{"tstamp": 1726258629.7804, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1726258627.4162, "finish": 1726258629.7804, "ip": "", "conv_id": "2eb540b916b44f86ae031c15d08fb748", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "nutritional facts for an apple", "output": [["nutritional facts for an apple", "An apple a day keeps the doctor away\n\nOrigin A variant of the proverb, \"Eat an apple on going to bed, and you'll keep the doctor from earning his bread\" was recorded as a Pembrokeshire saying in 1866. The modern phrasing, \"An apple a day keeps the doctor away\", began usage at the end of the 19th century, with early print examples found as early as 1887.\nScientific background\nA 2013 study using computer modelling compared eating apples with taking a common daily cholesterol-lowering drug to estimate risk of cardiovascular diseases. The computer model estimated that eating an apple a day was generally comparable for people over age 50 years to using a statin drug to reduce low-density lipoprotein cholesterol, concluding that eating an apple a day \"is able to match modern medicine and is likely to have fewer side effects,\" while having similar annual cost.\nA 2015 study found apple eaters \"were more likely, in the crude analysis, to keep the doctor (and prescription medications) away.\"  When they adjusted for \"sociodemographic and health-related characteristics, however, the association was no longer statistically significant\". The study also found that people who ate an apple a day used fewer prescription medications.\nNutritional content of an apple\nA medium-size (100 gram) raw apple is 86% water and 14% carbohydrates with negligible content of fat and protein, and supplies 52 calories of food energy. It contains a moderate amount of dietary fiber, but otherwise has a low level of micronutrients."]], "corpus": "wikipedia"}