Scheduled Commit

data/clustering_individual-1948e9c4-e613-4487-b12e-0e988013b994.jsonl

@@ -72,3 +72,7 @@
 {"tstamp": 1722871742.7525, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722871742.7051, "finish": 1722871742.7525, "ip": "", "conv_id": "d12c68d607354123aefe6e889a682284", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["Hello how are you?"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722871752.9011, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722871752.8246, "finish": 1722871752.9011, "ip": "", "conv_id": "2688c50e544c4bd08bf56e9934347477", "model_name": "GritLM/GritLM-7B", "prompt": ["Hello how are you?", "I'm good"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722871752.9011, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722871752.8246, "finish": 1722871752.9011, "ip": "", "conv_id": "d12c68d607354123aefe6e889a682284", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["Hello how are you?", "I'm good"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722872624.5957, "task_type": "clustering", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722872623.7834, "finish": 1722872624.5957, "ip": "", "conv_id": "3e5b12e39e2c445e95ac90b7481a42e3", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": ["hydroelectric", "solar", "Atari", "PlayStation", "Nintendo", "Hindi", "French", "Russian", "English", "Spanish", "Arabic", "taekwondo", "judo", "sailboat", "catamaran", "canoe"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722872624.5957, "task_type": "clustering", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722872623.7834, "finish": 1722872624.5957, "ip": "", "conv_id": "af7855e6a6014714a7bec015907dcdbf", "model_name": "text-embedding-3-large", "prompt": ["hydroelectric", "solar", "Atari", "PlayStation", "Nintendo", "Hindi", "French", "Russian", "English", "Spanish", "Arabic", "taekwondo", "judo", "sailboat", "catamaran", "canoe"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722872664.7256, "task_type": "clustering", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722872664.4143, "finish": 1722872664.7256, "ip": "", "conv_id": "c6a1685ef9cd484389083bf118806489", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": ["Egyptian", "Celtic", "Roman", "Hindu", "Chinese", "Norse", "Facebook", "Pinterest", "triangle", "hexagon", "pentagon", "circle", "haiku", "limerick", "Renaissance", "Abstract Expressionism"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722872664.7256, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722872664.4143, "finish": 1722872664.7256, "ip": "", "conv_id": "9c1c36a0dd564be9a44f0f9d5bd8ebcf", "model_name": "text-embedding-004", "prompt": ["Egyptian", "Celtic", "Roman", "Hindu", "Chinese", "Norse", "Facebook", "Pinterest", "triangle", "hexagon", "pentagon", "circle", "haiku", "limerick", "Renaissance", "Abstract Expressionism"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/retrieval_battle-1948e9c4-e613-4487-b12e-0e988013b994.jsonl

@@ -150,3 +150,8 @@
 {"tstamp": 1722871990.1213, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "78284d34a4ef47e29eb0067f2420da39", "0_model_name": "text-embedding-3-large", "0_prompt": "What is KiwiTech ", "0_output": [["What is KiwiTech ", "Kiwi.com\n\nKiwi.com (previously known as skypicker.com) is a Czech online travel agency founded by Oliver Dlouhý and Jozef Képesi in 2012. Kiwi.com provides a fare aggregator, metasearch engine and booking for airline tickets and ground transportation. Its ticket search features Kiwi.com’s \"virtual interlining\" concept – itineraries combined from over 750 carriers, including many that do not usually cooperate in online bookings."]], "0_corpus": "wikipedia", "1_conv_id": "375faf9b60ee45219b9143ad1f7e78c4", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "What is KiwiTech ", "1_output": [["What is KiwiTech ", "Kiwi.com\n\nHistory The online portal skypicker.com was created in 2011. The company was founded in Brno by Oliver Dlouhý and Jozef Képesi. In 2016 the company acquired the domain name Kiwi.com for $800,000 and rebranded itself as Kiwi.com. Jiří Hlavenka was one of its first investors. In 2017, Kiwi.com acquired a stake in Jaroslav Kokolus. In 2018, Kiwi.com was included by Financial Times into the list of European firms with the top compound annual revenue growth in 2015-2018 and was titled ‘7th fastest growing company’ in EMEA according to Deloitte.\nAs of June 2019, Kiwi.com’s main shareholder is General Atlantic. Dlouhý and Képesi remain as major shareholders and continue to run the company. Other shareholders are Touzimsky Airlines and Luboš Charčenko.\nIn December 2022, Airasia partnered with Kiwi.com to transform the carrier into the ASEAN app for travel and lifestyle.\nIn 2023, Kiwi.com partnered with Bratislava Airport to assist travelers in finding destinations starting from the Slovakian hub.\nServices\nIn 2018, Kiwi.com launched NOMAD, a special multi-city travel search tool, and Tequila, a dedicated B2B platform.\nAlong with its headquarters in Brno, Kiwi.com has offices in Prague (Czech Republic), Barcelona (Spain), Bratislava (Slovakia), Košice (Slovakia), London (United Kingdom) and Miami (USA).\nThe company is presently one of the five biggest online air ticket sellers in Europe, with an annual turnover of approximately 1.1 billion euros in 2018. The company booked around 12,000 traveler itineraries daily and employed 2000 workers during 2019.\nIn 2022, Kiwi.com sold 50 billion CZK worth of tickets."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722872047.7203, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "fc8e1acde1f8497aae43dae2f85e1587", "0_model_name": "text-embedding-3-large", "0_prompt": "I am looking for a paper which defined transformer architecture ", "0_output": [["I am looking for a paper which defined transformer architecture ", "Title: An Introduction to Transformers\n\nAbstract: The transformer is a neural network component that can be used to learn useful representations of sequences or sets of data-points. The transformer has driven recent advances in natural language processing, computer vision, and spatio-temporal modelling. There are many introductions to transformers, but most do not contain precise mathematical descriptions of the architecture and the intuitions behind the design choices are often also missing. Moreover, as research takes a winding path, the explanations for the components of the transformer can be idiosyncratic. In this note we aim for a mathematically precise, intuitive, and clean description of the transformer architecture. We will not discuss training as this is rather standard. We assume that the reader is familiar with fundamental topics in machine learning including multi-layer perceptrons, linear transformations, softmax functions and basic probability."]], "0_corpus": "arxiv", "1_conv_id": "b59f4dc6299b49cb9815c6bfe87599cb", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": "I am looking for a paper which defined transformer architecture ", "1_output": [["I am looking for a paper which defined transformer architecture ", "Title: An Introduction to Transformers\n\nAbstract: The transformer is a neural network component that can be used to learn useful representations of sequences or sets of data-points. The transformer has driven recent advances in natural language processing, computer vision, and spatio-temporal modelling. There are many introductions to transformers, but most do not contain precise mathematical descriptions of the architecture and the intuitions behind the design choices are often also missing. Moreover, as research takes a winding path, the explanations for the components of the transformer can be idiosyncratic. In this note we aim for a mathematically precise, intuitive, and clean description of the transformer architecture. We will not discuss training as this is rather standard. We assume that the reader is familiar with fundamental topics in machine learning including multi-layer perceptrons, linear transformations, softmax functions and basic probability."]], "1_corpus": "arxiv"}
 {"tstamp": 1722872217.9369, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "875162e88675454b899e5007c3af71b0", "0_model_name": "voyage-multilingual-2", "0_prompt": "legal medical bill", "0_output": [["legal medical bill", "Title: Natural language processing of MIMIC-III clinical notes for identifying diagnosis and procedures with neural networks\n\nAbstract: Coding diagnosis and procedures in medical records is a crucial process in the healthcare industry, which includes the creation of accurate billings, receiving reimbursements from payers, and creating standardized patient care records. In the United States, Billing and Insurance related activities cost around $471 billion in 2012 which constitutes about 25% of all the U.S hospital spending. In this paper, we report the performance of a natural language processing model that can map clinical notes to medical codes, and predict final diagnosis from unstructured entries of history of present illness, symptoms at the time of admission, etc. Previous studies have demonstrated that deep learning models perform better at such mapping when compared to conventional machine learning models. Therefore, we employed state-of-the-art deep learning method, ULMFiT on the largest emergency department clinical notes dataset MIMIC III which has 1.2M clinical notes to select for the top-10 and top-50 diagnosis and procedure codes. Our models were able to predict the top-10 diagnoses and procedures with 80.3% and 80.5% accuracy, whereas the top-50 ICD-9 codes of diagnosis and procedures are predicted with 70.7% and 63.9% accuracy. Prediction of diagnosis and procedures from unstructured clinical notes benefit human coders to save time, eliminate errors and minimize costs. With promising scores from our present model, the next step would be to deploy this on a small-scale real-world scenario and compare it with human coders as the gold standard. We believe that further research of this approach can create highly accurate predictions that can ease the workflow in a clinical setting."]], "0_corpus": "arxiv", "1_conv_id": "b07a1531d4e2469a9b60f75a7627217c", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "legal medical bill", "1_output": [["legal medical bill", "Title: Learning Bill Similarity with Annotated and Augmented Corpora of Bills\n\nAbstract: Bill writing is a critical element of representative democracy. However, it is often overlooked that most legislative bills are derived, or even directly copied, from other bills. Despite the significance of bill-to-bill linkages for understanding the legislative process, existing approaches fail to address semantic similarities across bills, let alone reordering or paraphrasing which are prevalent in legal document writing. In this paper, we overcome these limitations by proposing a 5-class classification task that closely reflects the nature of the bill generation process. In doing so, we construct a human-labeled dataset of 4,721 bill-to-bill relationships at the subsection-level and release this annotated dataset to the research community. To augment the dataset, we generate synthetic data with varying degrees of similarity, mimicking the complex bill writing process. We use BERT variants and apply multi-stage training, sequentially fine-tuning our models with synthetic and human-labeled datasets. We find that the predictive performance significantly improves when training with both human-labeled and synthetic data. Finally, we apply our trained model to infer section- and bill-level similarities. Our analysis shows that the proposed methodology successfully captures the similarities across legal documents at various levels of aggregation."]], "1_corpus": "arxiv"}
+{"tstamp": 1722872405.1291, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "2b52dbe8d63e4dd7923e455cf7513676", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "are touch sensitive lamps safe?", "0_output": [["are touch sensitive lamps safe?", "Stackoverflow Stackexchange\n\nQ: How to cache resources in Asp.net core? Can you please point me to an example. I  want to cache some objects that will be frequently used in most of the pages on the website? I am not sure what will be the recommended way of doing it in MVC 6.\n\nA: I think currently there no such like OutputCache attribute available that avaiable in ASP.net MVC 5. \nMostly attribute is just shortcut and it will indirectly use Cache provider ASP.net. \nSame thing available in ASP.net 5 vnext. \nhttps://github.com/aspnet/Caching\nHere different Cache mechanism available and you can use Memory Cache and create your own attribute. \nHope this help you.\n"]], "0_corpus": "stackexchange", "1_conv_id": "8bd21496f289479fa641ea8ca863aae0", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "are touch sensitive lamps safe?", "1_output": [["are touch sensitive lamps safe?", "Mathematica Stackexchange\n\nQ: Creating a warning before executing a function if not cancelled? How could I create a warning dialog that executes a function if not cancelled after a delay ? Also I would like the time remaining before execution to be displayed and the dialog to be non blocking.\n\nA: Using the function below we can do:\nExecuteIfNotCanceled[\"A print will happen in\",Print@\"A print happened indeed!\"&]\n\nThis function is interesting as it uses many different aspects of Mathematica.\nExecuteIfNotCanceled[message_,function_,delay:_Integer:5]:=\n    DynamicModule[{n=delay,decrementTask,closeScheduledTask,dialog,tasks},\n\n        tasks = {decrementTask,closeScheduledTask};\n\n        decrementTask=CreateScheduledTask[n--];         \n\n        closeScheduledTask=\n            CreateScheduledTask[\n                NotebookClose@dialog;\n                RemoveScheduledTask /@ tasks;\n                function[];\n                ,\n                {delay}\n            ];\n\n        StartScheduledTask /@ tasks;\n\n        dialog=\n            CreateDialog[\n                Column[\n                    {\n                        Dynamic[message~~\" in \"~~ToString@n~~\" seconds.\",TrackedSymbols:>{n}]\n                        ,\n                        CancelButton@DialogReturn[RemoveScheduledTask /@ tasks]\n                    }\n                    , \n                    Alignment -> Right\n                ]\n                ,\n                Modal->False\n            ];\n    ];\n\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1722872428.3276, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "9b97097a3a094d84a7e0092e2e3243fc", "0_model_name": "text-embedding-004", "0_prompt": "what is a data scientist?", "0_output": [["what is a data scientist?", "Data science\n\nData science is \"a concept to unify statistics, data analysis, informatics, and their related methods\" to \"understand and analyze actual phenomena\" with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. However, data science is different from computer science and information science. Turing Award winner Jim Gray imagined data science as a \"fourth paradigm\" of science (empirical, theoretical, computational, and now data-driven) and asserted that \"everything about science is changing because of the impact of information technology\" and the data deluge.\nA data scientist is a professional who creates programming code and combines it with statistical knowledge to create insights from data.\nFoundations\nData science is an interdisciplinary field focused on extracting knowledge from typically large data sets and applying the knowledge and insights from that data to solve problems in a wide range of application domains. The field encompasses preparing data for analysis, formulating data science problems, analyzing data, developing data-driven solutions, and presenting findings to inform high-level decisions in a broad range of application domains. As such, it incorporates skills from computer science, statistics, information science, mathematics, data visualization, information visualization, data sonification, data integration, graphic design, complex systems, communication and business. Statistician Nathan Yau, drawing on Ben Fry, also links data science to human–computer interaction: users should be able to intuitively control and explore data. In 2015, the American Statistical Association identified database management, statistics and machine learning, and distributed and parallel systems as the three emerging foundational professional communities."]], "0_corpus": "wikipedia", "1_conv_id": "a46e93be3b52442d82f277674f2e88c1", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "what is a data scientist?", "1_output": [["what is a data scientist?", "Data science\n\nData science is \"a concept to unify statistics, data analysis, informatics, and their related methods\" to \"understand and analyze actual phenomena\" with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. However, data science is different from computer science and information science. Turing Award winner Jim Gray imagined data science as a \"fourth paradigm\" of science (empirical, theoretical, computational, and now data-driven) and asserted that \"everything about science is changing because of the impact of information technology\" and the data deluge.\nA data scientist is a professional who creates programming code and combines it with statistical knowledge to create insights from data.\nFoundations\nData science is an interdisciplinary field focused on extracting knowledge from typically large data sets and applying the knowledge and insights from that data to solve problems in a wide range of application domains. The field encompasses preparing data for analysis, formulating data science problems, analyzing data, developing data-driven solutions, and presenting findings to inform high-level decisions in a broad range of application domains. As such, it incorporates skills from computer science, statistics, information science, mathematics, data visualization, information visualization, data sonification, data integration, graphic design, complex systems, communication and business. Statistician Nathan Yau, drawing on Ben Fry, also links data science to human–computer interaction: users should be able to intuitively control and explore data. In 2015, the American Statistical Association identified database management, statistics and machine learning, and distributed and parallel systems as the three emerging foundational professional communities."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722872490.5481, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "2b6d53a7fd2c4e99896f3f8911857159", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "Research introducing a sequence tagging approach to grammatical error correction achieving high F-scores on CoNLL-2014 and BEA-2019.", "0_output": [["Research introducing a sequence tagging approach to grammatical error correction achieving high F-scores on CoNLL-2014 and BEA-2019.", "Title: GECToR -- Grammatical Error Correction: Tag, Not Rewrite\n\nAbstract: In this paper, we present a simple and efficient GEC sequence tagger using a Transformer encoder. Our system is pre-trained on synthetic data and then fine-tuned in two stages: first on errorful corpora, and second on a combination of errorful and error-free parallel corpora. We design custom token-level transformations to map input tokens to target corrections. Our best single-model/ensemble GEC tagger achieves an $F_{0.5}$ of 65.3/66.5 on CoNLL-2014 (test) and $F_{0.5}$ of 72.4/73.6 on BEA-2019 (test). Its inference speed is up to 10 times as fast as a Transformer-based seq2seq GEC system. The code and trained models are publicly available."]], "0_corpus": "arxiv", "1_conv_id": "d37145f50e4d406bacc9f67c34d054d3", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "Research introducing a sequence tagging approach to grammatical error correction achieving high F-scores on CoNLL-2014 and BEA-2019.", "1_output": [["Research introducing a sequence tagging approach to grammatical error correction achieving high F-scores on CoNLL-2014 and BEA-2019.", "Title: The free abelian topological group and the free locally convex space on the unit interval\n\nAbstract: We give a complete description of the topological spaces $X$ such that the free abelian topological group $A(X)$ embeds into the free abelian topological group $A(I)$ of the closed unit interval. In particular, the free abelian topological group $A(X)$ of any finite-dimensional compact metrizable space $X$ embeds into $A(I)$. The situation turns out to be somewhat different for free locally convex spaces. Some results for the spaces of continuous functions with the pointwise topology are also obtained. Proofs are based on the classical Kolmogorov's Superposition Theorem."]], "1_corpus": "arxiv"}
+{"tstamp": 1722872492.5035, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "d0349da3ba4e47228621853474fe4569", "0_model_name": "voyage-multilingual-2", "0_prompt": "skills of a software engineer", "0_output": [["skills of a software engineer", "Competence (human resources)\n\nInitiative and creativity  Plans work and carries out tasks without detailed instructions; makes constructive suggestions; prepares for problems or opportunities in advance; undertakes additional responsibilities; responds to situations as they arise with minimal supervision; creates novel solutions to problems; evaluates new technology as potential solutions to existing problems.\nInnovation  Able to challenge conventional practices; adapts established methods for new uses; pursues ongoing system improvement; creates novel solutions to problems; evaluates new technology as potential solutions to existing problems.\nJudgement  Makes sound decisions; bases decisions on fact rather than emotion; analyzes problems skillfully; uses logic to reach solutions.\nLeadership  Able to become a role model for the team and lead from the front. Reliable and have the capacity to motivate subordinates.  Solves problems and takes important decisions.\nOrganization  Able to manage multiple projects; able to determine project urgency in a practical way; uses goals to guide actions; creates detailed action plans; organizes and schedules people and tasks effectively.\nProblem solving  Anticipates problems; sees how a problem and its solution will affect other units; gathers information before making decisions; weighs alternatives against objectives and arrives at reasonable decisions; adapts well to changing priorities, deadlines and directions; works to eliminate all processes which do not add value; is willing to take action, even under pressure, criticism or tight deadlines; takes informed risks; recognizes and accurately evaluates the signs of a problem; analyzes current procedures for possible improvements; notifies supervisor of problems in a timely manner."]], "0_corpus": "wikipedia", "1_conv_id": "21a095e8e61943ca9612889e97a035cb", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "skills of a software engineer", "1_output": [["skills of a software engineer", "Programmer\n\nA programmer, computer programmer or coder is an author of computer source code  someone with skill in computer programming.\nThe professional titles software developer and software engineer are used for jobs that require a programmer.\nGenerally, a programmer writes code in a computer language and with an intent to build software that achieves some goal.\nIdentification\nSometimes a programmer or job position is identified by the language used or target platform. For example, assembly programmer, web developer.\nJob title\nThe job titles that include programming tasks have differing connotations across the computer industry and to different individuals. The following are notable descriptions.\nA software developer primarily implements software based on specifications and fixes bugs. Other duties may include reviewing code changes and testing.\nTo achieve the required skills for the job, they might obtain a computer science or associate degree, attend a programming boot camp or be self-taught.\nA software engineer usually is responsible for the same tasks as a developer\nplus broader responsibilities of software engineering including architecting and designing new features and applications, targeting new platforms, managing the software development lifecycle (design, implementation, testing, and deployment), leading a team of programmers, communicating with customers, managers and other engineers, considering system stability and quality, and exploring software development methodologies."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722872501.7743, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "17a47f312ba84ac4bd0a8d19a7ca1112", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "Baseline and analysis of using BERT for improving coreference resolution tasks  ", "0_output": [["Baseline and analysis of using BERT for improving coreference resolution tasks  ", "Title: BERT for Coreference Resolution: Baselines and Analysis\n\nAbstract: We apply BERT to coreference resolution, achieving strong improvements on the OntoNotes (+3.9 F1) and GAP (+11.5 F1) benchmarks. A qualitative analysis of model predictions indicates that, compared to ELMo and BERT-base, BERT-large is particularly better at distinguishing between related but distinct entities (e.g., President and CEO). However, there is still room for improvement in modeling document-level context, conversations, and mention paraphrasing. Our code and models are publicly available."]], "0_corpus": "arxiv", "1_conv_id": "e478befe332d4662b114b57a5ed9e744", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "Baseline and analysis of using BERT for improving coreference resolution tasks  ", "1_output": [["Baseline and analysis of using BERT for improving coreference resolution tasks  ", "Title: Absorbing processes in Richardson diffusion: analytical results\n\nAbstract: We consider the recently addressed problem of a passive particle (a predator), being the center of a ``sphere of interception'' of radius $R$ and able to absorb other passive particles (the preys) entering into the sphere. Assuming that all the particles are advected by a turbulent flow and that, in particular, the Richardson equation properly describes the relative dispersion, we calculate an analytical expression for the flux into the sphere as a function of time, assuming an initial constant density of preys outside the sphere. In the same framework, we show that the distribution of times of first passage into the sphere has a $t^{-5/2}$ power law tail, seen in contrast to the $t^{-3/2}$ appearing in standard 3D diffusion. We also discuss the correction due to the integral length scale on the results in the stationary case."]], "1_corpus": "arxiv"}

data/retrieval_individual-1948e9c4-e613-4487-b12e-0e988013b994.jsonl

@@ -481,3 +481,15 @@
 {"tstamp": 1722871979.4978, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722871976.8959, "finish": 1722871979.4978, "ip": "", "conv_id": "375faf9b60ee45219b9143ad1f7e78c4", "model_name": "GritLM/GritLM-7B", "prompt": "What is KiwiTech ", "output": [["What is KiwiTech ", "Kiwi.com\n\nHistory The online portal skypicker.com was created in 2011. The company was founded in Brno by Oliver Dlouhý and Jozef Képesi. In 2016 the company acquired the domain name Kiwi.com for $800,000 and rebranded itself as Kiwi.com. Jiří Hlavenka was one of its first investors. In 2017, Kiwi.com acquired a stake in Jaroslav Kokolus. In 2018, Kiwi.com was included by Financial Times into the list of European firms with the top compound annual revenue growth in 2015-2018 and was titled ‘7th fastest growing company’ in EMEA according to Deloitte.\nAs of June 2019, Kiwi.com’s main shareholder is General Atlantic. Dlouhý and Képesi remain as major shareholders and continue to run the company. Other shareholders are Touzimsky Airlines and Luboš Charčenko.\nIn December 2022, Airasia partnered with Kiwi.com to transform the carrier into the ASEAN app for travel and lifestyle.\nIn 2023, Kiwi.com partnered with Bratislava Airport to assist travelers in finding destinations starting from the Slovakian hub.\nServices\nIn 2018, Kiwi.com launched NOMAD, a special multi-city travel search tool, and Tequila, a dedicated B2B platform.\nAlong with its headquarters in Brno, Kiwi.com has offices in Prague (Czech Republic), Barcelona (Spain), Bratislava (Slovakia), Košice (Slovakia), London (United Kingdom) and Miami (USA).\nThe company is presently one of the five biggest online air ticket sellers in Europe, with an annual turnover of approximately 1.1 billion euros in 2018. The company booked around 12,000 traveler itineraries daily and employed 2000 workers during 2019.\nIn 2022, Kiwi.com sold 50 billion CZK worth of tickets."]], "corpus": "wikipedia"}
 {"tstamp": 1722872035.7013, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722872033.4967, "finish": 1722872035.7013, "ip": "", "conv_id": "fc8e1acde1f8497aae43dae2f85e1587", "model_name": "text-embedding-3-large", "prompt": "I am looking for a paper which defined transformer architecture ", "output": [["I am looking for a paper which defined transformer architecture ", "Title: An Introduction to Transformers\n\nAbstract: The transformer is a neural network component that can be used to learn useful representations of sequences or sets of data-points. The transformer has driven recent advances in natural language processing, computer vision, and spatio-temporal modelling. There are many introductions to transformers, but most do not contain precise mathematical descriptions of the architecture and the intuitions behind the design choices are often also missing. Moreover, as research takes a winding path, the explanations for the components of the transformer can be idiosyncratic. In this note we aim for a mathematically precise, intuitive, and clean description of the transformer architecture. We will not discuss training as this is rather standard. We assume that the reader is familiar with fundamental topics in machine learning including multi-layer perceptrons, linear transformations, softmax functions and basic probability."]], "corpus": "arxiv"}
 {"tstamp": 1722872035.7013, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722872033.4967, "finish": 1722872035.7013, "ip": "", "conv_id": "b59f4dc6299b49cb9815c6bfe87599cb", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "I am looking for a paper which defined transformer architecture ", "output": [["I am looking for a paper which defined transformer architecture ", "Title: An Introduction to Transformers\n\nAbstract: The transformer is a neural network component that can be used to learn useful representations of sequences or sets of data-points. The transformer has driven recent advances in natural language processing, computer vision, and spatio-temporal modelling. There are many introductions to transformers, but most do not contain precise mathematical descriptions of the architecture and the intuitions behind the design choices are often also missing. Moreover, as research takes a winding path, the explanations for the components of the transformer can be idiosyncratic. In this note we aim for a mathematically precise, intuitive, and clean description of the transformer architecture. We will not discuss training as this is rather standard. We assume that the reader is familiar with fundamental topics in machine learning including multi-layer perceptrons, linear transformations, softmax functions and basic probability."]], "corpus": "arxiv"}
+{"tstamp": 1722872391.9927, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722872389.8402, "finish": 1722872391.9927, "ip": "", "conv_id": "2b52dbe8d63e4dd7923e455cf7513676", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "are touch sensitive lamps safe?", "output": [["are touch sensitive lamps safe?", "Stackoverflow Stackexchange\n\nQ: How to cache resources in Asp.net core? Can you please point me to an example. I  want to cache some objects that will be frequently used in most of the pages on the website? I am not sure what will be the recommended way of doing it in MVC 6.\n\nA: I think currently there no such like OutputCache attribute available that avaiable in ASP.net MVC 5. \nMostly attribute is just shortcut and it will indirectly use Cache provider ASP.net. \nSame thing available in ASP.net 5 vnext. \nhttps://github.com/aspnet/Caching\nHere different Cache mechanism available and you can use Memory Cache and create your own attribute. \nHope this help you.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1722872391.9927, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722872389.8402, "finish": 1722872391.9927, "ip": "", "conv_id": "8bd21496f289479fa641ea8ca863aae0", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "are touch sensitive lamps safe?", "output": [["are touch sensitive lamps safe?", "Mathematica Stackexchange\n\nQ: Creating a warning before executing a function if not cancelled? How could I create a warning dialog that executes a function if not cancelled after a delay ? Also I would like the time remaining before execution to be displayed and the dialog to be non blocking.\n\nA: Using the function below we can do:\nExecuteIfNotCanceled[\"A print will happen in\",Print@\"A print happened indeed!\"&]\n\nThis function is interesting as it uses many different aspects of Mathematica.\nExecuteIfNotCanceled[message_,function_,delay:_Integer:5]:=\n    DynamicModule[{n=delay,decrementTask,closeScheduledTask,dialog,tasks},\n\n        tasks = {decrementTask,closeScheduledTask};\n\n        decrementTask=CreateScheduledTask[n--];         \n\n        closeScheduledTask=\n            CreateScheduledTask[\n                NotebookClose@dialog;\n                RemoveScheduledTask /@ tasks;\n                function[];\n                ,\n                {delay}\n            ];\n\n        StartScheduledTask /@ tasks;\n\n        dialog=\n            CreateDialog[\n                Column[\n                    {\n                        Dynamic[message~~\" in \"~~ToString@n~~\" seconds.\",TrackedSymbols:>{n}]\n                        ,\n                        CancelButton@DialogReturn[RemoveScheduledTask /@ tasks]\n                    }\n                    , \n                    Alignment -> Right\n                ]\n                ,\n                Modal->False\n            ];\n    ];\n\n"]], "corpus": "stackexchange"}
+{"tstamp": 1722872414.3013, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722872411.993, "finish": 1722872414.3013, "ip": "", "conv_id": "9b97097a3a094d84a7e0092e2e3243fc", "model_name": "text-embedding-004", "prompt": "what is a data scientist?", "output": [["what is a data scientist?", "Data science\n\nData science is \"a concept to unify statistics, data analysis, informatics, and their related methods\" to \"understand and analyze actual phenomena\" with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. However, data science is different from computer science and information science. Turing Award winner Jim Gray imagined data science as a \"fourth paradigm\" of science (empirical, theoretical, computational, and now data-driven) and asserted that \"everything about science is changing because of the impact of information technology\" and the data deluge.\nA data scientist is a professional who creates programming code and combines it with statistical knowledge to create insights from data.\nFoundations\nData science is an interdisciplinary field focused on extracting knowledge from typically large data sets and applying the knowledge and insights from that data to solve problems in a wide range of application domains. The field encompasses preparing data for analysis, formulating data science problems, analyzing data, developing data-driven solutions, and presenting findings to inform high-level decisions in a broad range of application domains. As such, it incorporates skills from computer science, statistics, information science, mathematics, data visualization, information visualization, data sonification, data integration, graphic design, complex systems, communication and business. Statistician Nathan Yau, drawing on Ben Fry, also links data science to human–computer interaction: users should be able to intuitively control and explore data. In 2015, the American Statistical Association identified database management, statistics and machine learning, and distributed and parallel systems as the three emerging foundational professional communities."]], "corpus": "wikipedia"}
+{"tstamp": 1722872414.3013, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722872411.993, "finish": 1722872414.3013, "ip": "", "conv_id": "a46e93be3b52442d82f277674f2e88c1", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "what is a data scientist?", "output": [["what is a data scientist?", "Data science\n\nData science is \"a concept to unify statistics, data analysis, informatics, and their related methods\" to \"understand and analyze actual phenomena\" with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. However, data science is different from computer science and information science. Turing Award winner Jim Gray imagined data science as a \"fourth paradigm\" of science (empirical, theoretical, computational, and now data-driven) and asserted that \"everything about science is changing because of the impact of information technology\" and the data deluge.\nA data scientist is a professional who creates programming code and combines it with statistical knowledge to create insights from data.\nFoundations\nData science is an interdisciplinary field focused on extracting knowledge from typically large data sets and applying the knowledge and insights from that data to solve problems in a wide range of application domains. The field encompasses preparing data for analysis, formulating data science problems, analyzing data, developing data-driven solutions, and presenting findings to inform high-level decisions in a broad range of application domains. As such, it incorporates skills from computer science, statistics, information science, mathematics, data visualization, information visualization, data sonification, data integration, graphic design, complex systems, communication and business. Statistician Nathan Yau, drawing on Ben Fry, also links data science to human–computer interaction: users should be able to intuitively control and explore data. In 2015, the American Statistical Association identified database management, statistics and machine learning, and distributed and parallel systems as the three emerging foundational professional communities."]], "corpus": "wikipedia"}
+{"tstamp": 1722872463.5522, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722872461.0473, "finish": 1722872463.5522, "ip": "", "conv_id": "d0349da3ba4e47228621853474fe4569", "model_name": "voyage-multilingual-2", "prompt": "skills of a software engineer", "output": [["skills of a software engineer", "Competence (human resources)\n\nInitiative and creativity  Plans work and carries out tasks without detailed instructions; makes constructive suggestions; prepares for problems or opportunities in advance; undertakes additional responsibilities; responds to situations as they arise with minimal supervision; creates novel solutions to problems; evaluates new technology as potential solutions to existing problems.\nInnovation  Able to challenge conventional practices; adapts established methods for new uses; pursues ongoing system improvement; creates novel solutions to problems; evaluates new technology as potential solutions to existing problems.\nJudgement  Makes sound decisions; bases decisions on fact rather than emotion; analyzes problems skillfully; uses logic to reach solutions.\nLeadership  Able to become a role model for the team and lead from the front. Reliable and have the capacity to motivate subordinates.  Solves problems and takes important decisions.\nOrganization  Able to manage multiple projects; able to determine project urgency in a practical way; uses goals to guide actions; creates detailed action plans; organizes and schedules people and tasks effectively.\nProblem solving  Anticipates problems; sees how a problem and its solution will affect other units; gathers information before making decisions; weighs alternatives against objectives and arrives at reasonable decisions; adapts well to changing priorities, deadlines and directions; works to eliminate all processes which do not add value; is willing to take action, even under pressure, criticism or tight deadlines; takes informed risks; recognizes and accurately evaluates the signs of a problem; analyzes current procedures for possible improvements; notifies supervisor of problems in a timely manner."]], "corpus": "wikipedia"}
+{"tstamp": 1722872463.5522, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722872461.0473, "finish": 1722872463.5522, "ip": "", "conv_id": "21a095e8e61943ca9612889e97a035cb", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "skills of a software engineer", "output": [["skills of a software engineer", "Programmer\n\nA programmer, computer programmer or coder is an author of computer source code  someone with skill in computer programming.\nThe professional titles software developer and software engineer are used for jobs that require a programmer.\nGenerally, a programmer writes code in a computer language and with an intent to build software that achieves some goal.\nIdentification\nSometimes a programmer or job position is identified by the language used or target platform. For example, assembly programmer, web developer.\nJob title\nThe job titles that include programming tasks have differing connotations across the computer industry and to different individuals. The following are notable descriptions.\nA software developer primarily implements software based on specifications and fixes bugs. Other duties may include reviewing code changes and testing.\nTo achieve the required skills for the job, they might obtain a computer science or associate degree, attend a programming boot camp or be self-taught.\nA software engineer usually is responsible for the same tasks as a developer\nplus broader responsibilities of software engineering including architecting and designing new features and applications, targeting new platforms, managing the software development lifecycle (design, implementation, testing, and deployment), leading a team of programmers, communicating with customers, managers and other engineers, considering system stability and quality, and exploring software development methodologies."]], "corpus": "wikipedia"}
+{"tstamp": 1722872483.2599, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722872483.0808, "finish": 1722872483.2599, "ip": "", "conv_id": "2b6d53a7fd2c4e99896f3f8911857159", "model_name": "GritLM/GritLM-7B", "prompt": "Research introducing a sequence tagging approach to grammatical error correction achieving high F-scores on CoNLL-2014 and BEA-2019.", "output": [["Research introducing a sequence tagging approach to grammatical error correction achieving high F-scores on CoNLL-2014 and BEA-2019.", "Title: GECToR -- Grammatical Error Correction: Tag, Not Rewrite\n\nAbstract: In this paper, we present a simple and efficient GEC sequence tagger using a Transformer encoder. Our system is pre-trained on synthetic data and then fine-tuned in two stages: first on errorful corpora, and second on a combination of errorful and error-free parallel corpora. We design custom token-level transformations to map input tokens to target corrections. Our best single-model/ensemble GEC tagger achieves an $F_{0.5}$ of 65.3/66.5 on CoNLL-2014 (test) and $F_{0.5}$ of 72.4/73.6 on BEA-2019 (test). Its inference speed is up to 10 times as fast as a Transformer-based seq2seq GEC system. The code and trained models are publicly available."]], "corpus": "arxiv"}
+{"tstamp": 1722872483.2599, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722872483.0808, "finish": 1722872483.2599, "ip": "", "conv_id": "d37145f50e4d406bacc9f67c34d054d3", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Research introducing a sequence tagging approach to grammatical error correction achieving high F-scores on CoNLL-2014 and BEA-2019.", "output": [["Research introducing a sequence tagging approach to grammatical error correction achieving high F-scores on CoNLL-2014 and BEA-2019.", "Title: The free abelian topological group and the free locally convex space on the unit interval\n\nAbstract: We give a complete description of the topological spaces $X$ such that the free abelian topological group $A(X)$ embeds into the free abelian topological group $A(I)$ of the closed unit interval. In particular, the free abelian topological group $A(X)$ of any finite-dimensional compact metrizable space $X$ embeds into $A(I)$. The situation turns out to be somewhat different for free locally convex spaces. Some results for the spaces of continuous functions with the pointwise topology are also obtained. Proofs are based on the classical Kolmogorov's Superposition Theorem."]], "corpus": "arxiv"}
+{"tstamp": 1722872498.9575, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722872498.7698, "finish": 1722872498.9575, "ip": "", "conv_id": "17a47f312ba84ac4bd0a8d19a7ca1112", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "Baseline and analysis of using BERT for improving coreference resolution tasks  ", "output": [["Baseline and analysis of using BERT for improving coreference resolution tasks  ", "Title: BERT for Coreference Resolution: Baselines and Analysis\n\nAbstract: We apply BERT to coreference resolution, achieving strong improvements on the OntoNotes (+3.9 F1) and GAP (+11.5 F1) benchmarks. A qualitative analysis of model predictions indicates that, compared to ELMo and BERT-base, BERT-large is particularly better at distinguishing between related but distinct entities (e.g., President and CEO). However, there is still room for improvement in modeling document-level context, conversations, and mention paraphrasing. Our code and models are publicly available."]], "corpus": "arxiv"}
+{"tstamp": 1722872498.9575, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722872498.7698, "finish": 1722872498.9575, "ip": "", "conv_id": "e478befe332d4662b114b57a5ed9e744", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Baseline and analysis of using BERT for improving coreference resolution tasks  ", "output": [["Baseline and analysis of using BERT for improving coreference resolution tasks  ", "Title: Absorbing processes in Richardson diffusion: analytical results\n\nAbstract: We consider the recently addressed problem of a passive particle (a predator), being the center of a ``sphere of interception'' of radius $R$ and able to absorb other passive particles (the preys) entering into the sphere. Assuming that all the particles are advected by a turbulent flow and that, in particular, the Richardson equation properly describes the relative dispersion, we calculate an analytical expression for the flux into the sphere as a function of time, assuming an initial constant density of preys outside the sphere. In the same framework, we show that the distribution of times of first passage into the sphere has a $t^{-5/2}$ power law tail, seen in contrast to the $t^{-3/2}$ appearing in standard 3D diffusion. We also discuss the correction due to the integral length scale on the results in the stationary case."]], "corpus": "arxiv"}
+{"tstamp": 1722872509.6622, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722872507.3344, "finish": 1722872509.6622, "ip": "", "conv_id": "7b8f24017afe4c52af18874926431446", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "Research improving BERT and T5 on NLU tasks with a model using 2D positional encodings and span prediction", "output": [["Research improving BERT and T5 on NLU tasks with a model using 2D positional encodings and span prediction", "Title: Rethinking Positional Encoding in Language Pre-training\n\nAbstract: In this work, we investigate the positional encoding methods used in language pre-training (e.g., BERT) and identify several problems in the existing formulations. First, we show that in the absolute positional encoding, the addition operation applied on positional embeddings and word embeddings brings mixed correlations between the two heterogeneous information resources. It may bring unnecessary randomness in the attention and further limit the expressiveness of the model. Second, we question whether treating the position of the symbol \\texttt{[CLS]} the same as other words is a reasonable design, considering its special role (the representation of the entire sentence) in the downstream tasks. Motivated from above analysis, we propose a new positional encoding method called \\textbf{T}ransformer with \\textbf{U}ntied \\textbf{P}ositional \\textbf{E}ncoding (TUPE). In the self-attention module, TUPE computes the word contextual correlation and positional correlation separately with different parameterizations and then adds them together. This design removes the mixed and noisy correlations over heterogeneous embeddings and offers more expressiveness by using different projection matrices. Furthermore, TUPE unties the \\texttt{[CLS]} symbol from other positions, making it easier to capture information from all positions. Extensive experiments and ablation studies on GLUE benchmark demonstrate the effectiveness of the proposed method. Codes and models are released at https://github.com/guolinke/TUPE."]], "corpus": "arxiv"}
+{"tstamp": 1722872509.6622, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722872507.3344, "finish": 1722872509.6622, "ip": "", "conv_id": "39ca84e0cf394d6cb6b07c48a671741f", "model_name": "text-embedding-004", "prompt": "Research improving BERT and T5 on NLU tasks with a model using 2D positional encodings and span prediction", "output": [["Research improving BERT and T5 on NLU tasks with a model using 2D positional encodings and span prediction", "Title: Span Selection Pre-training for Question Answering\n\nAbstract: BERT (Bidirectional Encoder Representations from Transformers) and related pre-trained Transformers have provided large gains across many language understanding tasks, achieving a new state-of-the-art (SOTA). BERT is pre-trained on two auxiliary tasks: Masked Language Model and Next Sentence Prediction. In this paper we introduce a new pre-training task inspired by reading comprehension to better align the pre-training from memorization to understanding. Span Selection Pre-Training (SSPT) poses cloze-like training instances, but rather than draw the answer from the model's parameters, it is selected from a relevant passage. We find significant and consistent improvements over both BERT-BASE and BERT-LARGE on multiple reading comprehension (MRC) datasets. Specifically, our proposed model has strong empirical evidence as it obtains SOTA results on Natural Questions, a new benchmark MRC dataset, outperforming BERT-LARGE by 3 F1 points on short answer prediction. We also show significant impact in HotpotQA, improving answer prediction F1 by 4 points and supporting fact prediction F1 by 1 point and outperforming the previous best system. Moreover, we show that our pre-training approach is particularly effective when training data is limited, improving the learning curve by a large amount."]], "corpus": "arxiv"}