Scheduled Commit

data/retrieval_battle-76065722-116b-41cc-961b-aebfed552f79.jsonl

@@ -159,3 +159,4 @@
 {"tstamp": 1728039163.547, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "165840269bb8478bbab3978d5a528965", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "tell me about Lee Kuan Yew", "0_output": [["tell me about Lee Kuan Yew", "Lee Kuan Yew\n\nLee Kuan Yew   (born Harry Lee Kuan Yew; 16 September 1923 – 23 March 2015), often referred to by his initials LKY, was a Singaporean statesman and lawyer who served as the first Prime Minister of Singapore from 1959 to 1990, and Secretary-General of the People's Action Party from 1954 to 1992. He was the Member of Parliament (MP) for Tanjong Pagar from 1955 until his death in 2015. Lee is widely recognised as the founding father of the modern Singaporean state, and for his leadership in turning and transforming it into a highly developed country during his tenure."]], "0_corpus": "wikipedia", "1_conv_id": "12226c465e894aba96fd800851c60470", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "tell me about Lee Kuan Yew", "1_output": [["tell me about Lee Kuan Yew", "Lee Kuan Yew\n\nLee Kuan Yew   (born Harry Lee Kuan Yew; 16 September 1923 – 23 March 2015), often referred to by his initials LKY, was a Singaporean statesman and lawyer who served as the first Prime Minister of Singapore from 1959 to 1990, and Secretary-General of the People's Action Party from 1954 to 1992. He was the Member of Parliament (MP) for Tanjong Pagar from 1955 until his death in 2015. Lee is widely recognised as the founding father of the modern Singaporean state, and for his leadership in turning and transforming it into a highly developed country during his tenure."]], "1_corpus": "wikipedia"}
 {"tstamp": 1728055790.4076, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "67848ffd4c32469a92af622b5a51ebac", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "best ways to search relevant article ", "0_output": [["best ways to search relevant article ", "Title: Finding and Recommending Scholarly Articles\n\nAbstract: The rate at which scholarly literature is being produced has been increasing at approximately 3.5 percent per year for decades. This means that during a typical 40 year career the amount of new literature produced each year increases by a factor of four. The methods scholars use to discover relevant literature must change. Just like everybody else involved in information discovery, scholars are confronted with information overload. Two decades ago, this discovery process essentially consisted of paging through abstract books, talking to colleagues and librarians, and browsing journals. A time-consuming process, which could even be longer if material had to be shipped from elsewhere. Now much of this discovery process is mediated by online scholarly information systems. All these systems are relatively new, and all are still changing. They all share a common goal: to provide their users with access to the literature relevant to their specific needs. To achieve this each system responds to actions by the user by displaying articles which the system judges relevant to the user's current needs. Recently search systems which use particularly sophisticated methodologies to recommend a few specific papers to the user have been called \"recommender systems\". These methods are in line with the current use of the term \"recommender system\" in computer science. We do not adopt this definition, rather we view systems like these as components in a larger whole, which is presented by the scholarly information systems themselves. In what follows we view the recommender system as an aspect of the entire information system; one which combines the massive memory capacities of the machine with the cognitive abilities of the human user to achieve a human-machine synergy."]], "0_corpus": "arxiv", "1_conv_id": "2d28bd14957d40a582c0c76cb91c65c5", "1_model_name": "voyage-multilingual-2", "1_prompt": "best ways to search relevant article ", "1_output": [["best ways to search relevant article ", "Title: Towards a Semantic Search Engine for Scientific Articles\n\nAbstract: Because of the data deluge in scientific publication, finding relevant information is getting harder and harder for researchers and readers. Building an enhanced scientific search engine by taking semantic relations into account poses a great challenge. As a starting point, semantic relations between keywords from scientific articles could be extracted in order to classify articles. This might help later in the process of browsing and searching for content in a meaningful scientific way. Indeed, by connecting keywords, the context of the article can be extracted. This paper aims to provide ideas to build such a smart search engine and describes the initial contributions towards achieving such an ambitious goal."]], "1_corpus": "arxiv"}
 {"tstamp": 1728055888.5455, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "aeaf9946eb9b40cb8df74422d910e9fe", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "Семантический поиск похожих научных статей на русском языке", "0_output": [["Семантический поиск похожих научных статей на русском языке", "Title: Evaluation experiments on related terms search in Wikipedia: Information Content and Adapted HITS (In Russian)\n\nAbstract: The classification of metrics and algorithms search for related terms via WordNet, Roget's Thesaurus, and Wikipedia was extended to include adapted HITS algorithm. Evaluation experiments on Information Content and adapted HITS algorithm are described. The test collection of Russian word pairs with human-assigned similarity judgments is proposed. ----- Klassifikacija metrik i algoritmov poiska semanticheski blizkih slov v tezaurusah WordNet, Rozhe i jenciklopedii Vikipedija rasshirena adaptirovannym HITS algoritmom. S pomow'ju jeksperimentov v Vikipedii oceneny metrika Information Content i adaptirovannyj algoritm HITS. Predlozhen resurs dlja ocenki semanticheskoj blizosti russkih slov."]], "0_corpus": "arxiv", "1_conv_id": "bd5ae926e2b747b58befb8cf04831a18", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "Семантический поиск похожих научных статей на русском языке", "1_output": [["Семантический поиск похожих научных статей на русском языке", "Title: Texts in, meaning out: neural language models in semantic similarity task for Russian\n\nAbstract: Distributed vector representations for natural language vocabulary get a lot of attention in contemporary computational linguistics. This paper summarizes the experience of applying neural network language models to the task of calculating semantic similarity for Russian. The experiments were performed in the course of Russian Semantic Similarity Evaluation track, where our models took from the 2nd to the 5th position, depending on the task. We introduce the tools and corpora used, comment on the nature of the shared task and describe the achieved results. It was found out that Continuous Skip-gram and Continuous Bag-of-words models, previously successfully applied to English material, can be used for semantic modeling of Russian as well. Moreover, we show that texts in Russian National Corpus (RNC) provide an excellent training material for such models, outperforming other, much larger corpora. It is especially true for semantic relatedness tasks (although stacking models trained on larger corpora on top of RNC models improves performance even more). High-quality semantic vectors learned in such a way can be used in a variety of linguistic tasks and promise an exciting field for further study."]], "1_corpus": "arxiv"}
+{"tstamp": 1728056690.1517, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "a18383d181684287829141784aefd5a6", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "What is the population of BERLIN ?", "0_output": [["What is the population of BERLIN ?", "Berlin\n\nBerlin is the capital and largest city of Germany, both by area and by population. Its more than 3.85 million inhabitants make it the European Union's most populous city, as measured by population within city limits having gained this status after the United Kingdom's, and thus London's, departure from the European Union. Simultaneously, the city is one of the states of Germany, and is the third smallest state in the country in terms of area. Berlin is surrounded by the state of Brandenburg, and Brandenburg's capital Potsdam is nearby. The urban area of Berlin has a population of over 4.5 million and is therefore the most populous urban area in Germany. The Berlin-Brandenburg capital region has around 6.2 million inhabitants and is Germany's second-largest metropolitan region after the Rhine-Ruhr region, and the sixth-biggest metropolitan region by GDP in the European Union.\nBerlin was built along the banks of the Spree river, which flows into the Havel in the western borough of Spandau. The city incorporates lakes in the western and southeastern boroughs, the largest of which is Müggelsee. About one-third of the city's area is composed of forests, parks and gardens, rivers, canals, and lakes."]], "0_corpus": "wikipedia", "1_conv_id": "3d6513ac1b5d46bdbab2e79d2a15b3c4", "1_model_name": "text-embedding-3-large", "1_prompt": "What is the population of BERLIN ?", "1_output": [["What is the population of BERLIN ?", "Demographics of Berlin\n\nThe industrialisation had brought about a rapid expansion of the suburbs, many of them developed explicitly for workers of specific factories, e.g. Siemensstadt and Borsigwalde. The Greater Berlin Act of 1920 (Groß-Berlin-Gesetz) boosted the population by incorporating many hitherto autonomous towns and cities, e.g. Spandau and Köpenick at the margins of the modern metropolis, but also Charlottenburg, nowadays almost in the heart of the city. The city approximately reached its modern extent, growing from  to . This expansion made Berlin the most populous city proper of Continental Europe in the interwar period (though not the largest agglomeration) and the third-largest in the world behind London and New York.\nThe four-million mark was surpassed in the 1920s, and in 1942, the officially registered population reached its maximum of 4.48 million, although because of the war conditions, this was an overestimation. More likely estimates based on food rationing data show lower numbers of 3.95 million people in February 1942 and only 3.11 million people in February 1944 (incl. 177,000 foreigners) when aerial attacks approached its most intense phase.\nIn the context of the more general huge population movements in immediately post-war Germany, a significant part of Berlin's pre-war population permanently resettled to other parts of Germany or abroad. A 1946 census counted 436,600 Berliners in the western occupation zones and 306,823 in the Soviet zone. In 1950, this number had risen to 518,218 in what had now become the Federal Republic. Since the end of World War II, the city population has been fluctuating between 3 and 3.5 million, with a low of less than 3.1 million from the mid-1970s to mid-1980s. Between 1950 and 1961, so between the establishment of the Soviet-backed German Democratic Republic and the construction of the Berlin Wall, most of the losses were incurred by East Berlin, while West Berlin showed modest growth of 2.3%."]], "1_corpus": "wikipedia"}

data/retrieval_individual-76065722-116b-41cc-961b-aebfed552f79.jsonl

@@ -725,3 +725,7 @@
 {"tstamp": 1728055684.2151, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1728055681.7577, "finish": 1728055684.2151, "ip": "", "conv_id": "2d28bd14957d40a582c0c76cb91c65c5", "model_name": "voyage-multilingual-2", "prompt": "best ways to search relevant article ", "output": [["best ways to search relevant article ", "Title: Towards a Semantic Search Engine for Scientific Articles\n\nAbstract: Because of the data deluge in scientific publication, finding relevant information is getting harder and harder for researchers and readers. Building an enhanced scientific search engine by taking semantic relations into account poses a great challenge. As a starting point, semantic relations between keywords from scientific articles could be extracted in order to classify articles. This might help later in the process of browsing and searching for content in a meaningful scientific way. Indeed, by connecting keywords, the context of the article can be extracted. This paper aims to provide ideas to build such a smart search engine and describes the initial contributions towards achieving such an ambitious goal."]], "corpus": "arxiv"}
 {"tstamp": 1728055854.0939, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1728055851.9241, "finish": 1728055854.0939, "ip": "", "conv_id": "aeaf9946eb9b40cb8df74422d910e9fe", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "Семантический поиск похожих научных статей на русском языке", "output": [["Семантический поиск похожих научных статей на русском языке", "Title: Evaluation experiments on related terms search in Wikipedia: Information Content and Adapted HITS (In Russian)\n\nAbstract: The classification of metrics and algorithms search for related terms via WordNet, Roget's Thesaurus, and Wikipedia was extended to include adapted HITS algorithm. Evaluation experiments on Information Content and adapted HITS algorithm are described. The test collection of Russian word pairs with human-assigned similarity judgments is proposed. ----- Klassifikacija metrik i algoritmov poiska semanticheski blizkih slov v tezaurusah WordNet, Rozhe i jenciklopedii Vikipedija rasshirena adaptirovannym HITS algoritmom. S pomow'ju jeksperimentov v Vikipedii oceneny metrika Information Content i adaptirovannyj algoritm HITS. Predlozhen resurs dlja ocenki semanticheskoj blizosti russkih slov."]], "corpus": "arxiv"}
 {"tstamp": 1728055854.0939, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1728055851.9241, "finish": 1728055854.0939, "ip": "", "conv_id": "bd5ae926e2b747b58befb8cf04831a18", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "Семантический поиск похожих научных статей на русском языке", "output": [["Семантический поиск похожих научных статей на русском языке", "Title: Texts in, meaning out: neural language models in semantic similarity task for Russian\n\nAbstract: Distributed vector representations for natural language vocabulary get a lot of attention in contemporary computational linguistics. This paper summarizes the experience of applying neural network language models to the task of calculating semantic similarity for Russian. The experiments were performed in the course of Russian Semantic Similarity Evaluation track, where our models took from the 2nd to the 5th position, depending on the task. We introduce the tools and corpora used, comment on the nature of the shared task and describe the achieved results. It was found out that Continuous Skip-gram and Continuous Bag-of-words models, previously successfully applied to English material, can be used for semantic modeling of Russian as well. Moreover, we show that texts in Russian National Corpus (RNC) provide an excellent training material for such models, outperforming other, much larger corpora. It is especially true for semantic relatedness tasks (although stacking models trained on larger corpora on top of RNC models improves performance even more). High-quality semantic vectors learned in such a way can be used in a variety of linguistic tasks and promise an exciting field for further study."]], "corpus": "arxiv"}
+{"tstamp": 1728056675.98, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1728056675.0264, "finish": 1728056675.98, "ip": "", "conv_id": "a18383d181684287829141784aefd5a6", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "What is the population of BERLIN ?", "output": [["What is the population of BERLIN ?", "Berlin\n\nBerlin is the capital and largest city of Germany, both by area and by population. Its more than 3.85 million inhabitants make it the European Union's most populous city, as measured by population within city limits having gained this status after the United Kingdom's, and thus London's, departure from the European Union. Simultaneously, the city is one of the states of Germany, and is the third smallest state in the country in terms of area. Berlin is surrounded by the state of Brandenburg, and Brandenburg's capital Potsdam is nearby. The urban area of Berlin has a population of over 4.5 million and is therefore the most populous urban area in Germany. The Berlin-Brandenburg capital region has around 6.2 million inhabitants and is Germany's second-largest metropolitan region after the Rhine-Ruhr region, and the sixth-biggest metropolitan region by GDP in the European Union.\nBerlin was built along the banks of the Spree river, which flows into the Havel in the western borough of Spandau. The city incorporates lakes in the western and southeastern boroughs, the largest of which is Müggelsee. About one-third of the city's area is composed of forests, parks and gardens, rivers, canals, and lakes."]], "corpus": "wikipedia"}
+{"tstamp": 1728056675.98, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1728056675.0264, "finish": 1728056675.98, "ip": "", "conv_id": "3d6513ac1b5d46bdbab2e79d2a15b3c4", "model_name": "text-embedding-3-large", "prompt": "What is the population of BERLIN ?", "output": [["What is the population of BERLIN ?", "Demographics of Berlin\n\nThe industrialisation had brought about a rapid expansion of the suburbs, many of them developed explicitly for workers of specific factories, e.g. Siemensstadt and Borsigwalde. The Greater Berlin Act of 1920 (Groß-Berlin-Gesetz) boosted the population by incorporating many hitherto autonomous towns and cities, e.g. Spandau and Köpenick at the margins of the modern metropolis, but also Charlottenburg, nowadays almost in the heart of the city. The city approximately reached its modern extent, growing from  to . This expansion made Berlin the most populous city proper of Continental Europe in the interwar period (though not the largest agglomeration) and the third-largest in the world behind London and New York.\nThe four-million mark was surpassed in the 1920s, and in 1942, the officially registered population reached its maximum of 4.48 million, although because of the war conditions, this was an overestimation. More likely estimates based on food rationing data show lower numbers of 3.95 million people in February 1942 and only 3.11 million people in February 1944 (incl. 177,000 foreigners) when aerial attacks approached its most intense phase.\nIn the context of the more general huge population movements in immediately post-war Germany, a significant part of Berlin's pre-war population permanently resettled to other parts of Germany or abroad. A 1946 census counted 436,600 Berliners in the western occupation zones and 306,823 in the Soviet zone. In 1950, this number had risen to 518,218 in what had now become the Federal Republic. Since the end of World War II, the city population has been fluctuating between 3 and 3.5 million, with a low of less than 3.1 million from the mid-1970s to mid-1980s. Between 1950 and 1961, so between the establishment of the Soviet-backed German Democratic Republic and the construction of the Berlin Wall, most of the losses were incurred by East Berlin, while West Berlin showed modest growth of 2.3%."]], "corpus": "wikipedia"}
+{"tstamp": 1728056731.3956, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1728056729.1211, "finish": 1728056731.3956, "ip": "", "conv_id": "6183cbd3b6ea4368aab02dd40b380207", "model_name": "text-embedding-004", "prompt": "What is the population of Berlin ?", "output": [["What is the population of Berlin ?", "Demographics of Berlin\n\nIn December 2019, the city-state of Berlin had a population of 3,769,495 registered inhabitants in an area of . The city's population density was 4,227 inhabitants per km2. Berlin is Germany's largest city and the most populous city proper in the European Union.\nIn December 2015, there were 621,075 registered residents of foreign nationality, originating from approximately 190 different countries.\nHistory\nThe city responded to the 1685 revocation of the Edict of Nantes in France with the Edict of Potsdam, which guaranteed religious freedom and tax-free status to French Huguenot refugees for ten years. Thanks to its role as the capital of rising Prussia, the population grew steadily: it surpassed the 100,000 mark in 1747 and the one-million mark in 1877."]], "corpus": "wikipedia"}
+{"tstamp": 1728056731.3956, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1728056729.1211, "finish": 1728056731.3956, "ip": "", "conv_id": "c44b630bba0b4511aff479ced395b5d3", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "What is the population of Berlin ?", "output": [["What is the population of Berlin ?", "Berlin population statistics\n\nBerlin is the most populous city in the European Union, as calculated by city-proper population (not metropolitan area).\nDemographics\nPopulation by borough\nHistorical development of Berlin's population\nThe spike in population in 1920 is a result of the Greater Berlin Act.\nPopulation by nationality\nOn 31 December 2010 the largest groups by foreign nationality were citizens from Turkey (104,556), Poland (40,988), Serbia (19,230), Italy (15,842), Russia (15,332), United States (12,733), France (13,262), Vietnam (13,199), Croatia (10,104), Bosnia and Herzegovina (10,198), UK (10,191), Greece (9,301), Austria (9,246), Ukraine (8,324), Lebanon (7,078), Spain (7,670), Bulgaria (9,988), the People's Republic of China (5,632), Thailand (5,037). There is also a large Arabic community, mostly from Lebanon, Palestine and Iraq. Additionally, Berlin has one of the largest Vietnamese communities outside Vietnam, with about 83,000 people of Vietnamese origin."]], "corpus": "wikipedia"}