Scheduled Commit

data/retrieval_battle-407c4836-37e2-4f9f-8e9b-06706cc4440c.jsonl

@@ -9,3 +9,4 @@
 {"tstamp": 1722444028.9194, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "fa4418617a5a491b8a7ede90483569ba", "0_model_name": "BM25", "0_prompt": "Fine-tuning BERT for optimal text classification performance on multiple datasets", "0_output": [["Fine-tuning BERT for optimal text classification performance on multiple datasets", "Title: Imbalanced Multi-label Classification for Business-related Text with Moderately Large Label Spaces\n\nAbstract: In this study, we compared the performance of four different methods for multi label text classification using a specific imbalanced business dataset. The four methods we evaluated were fine tuned BERT, Binary Relevance, Classifier Chains, and Label Powerset. The results show that fine tuned BERT outperforms the other three methods by a significant margin, achieving high values of accuracy, F1 Score, Precision, and Recall. Binary Relevance also performs well on this dataset, while Classifier Chains and Label Powerset demonstrate relatively poor performance. These findings highlight the effectiveness of fine tuned BERT for multi label text classification tasks, and suggest that it may be a useful tool for businesses seeking to analyze complex and multifaceted texts."]], "0_corpus": "arxiv", "1_conv_id": "e87880dd7cd94e34b26d6ec08910f019", "1_model_name": "text-embedding-004", "1_prompt": "Fine-tuning BERT for optimal text classification performance on multiple datasets", "1_output": [["Fine-tuning BERT for optimal text classification performance on multiple datasets", "Title: How to Fine-Tune BERT for Text Classification?\n\nAbstract: Language model pre-training has proven to be useful in learning universal language representations. As a state-of-the-art language model pre-training model, BERT (Bidirectional Encoder Representations from Transformers) has achieved amazing results in many language understanding tasks. In this paper, we conduct exhaustive experiments to investigate different fine-tuning methods of BERT on text classification task and provide a general solution for BERT fine-tuning. Finally, the proposed solution obtains new state-of-the-art results on eight widely-studied text classification datasets."]], "1_corpus": "arxiv"}
 {"tstamp": 1722444509.5213, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "17817cacbabd4dcf9fa57b72945df25a", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "What is the antipode of heaven?", "0_output": [["What is the antipode of heaven?", "Heaven in Christianity\n\nIn Christianity, heaven is traditionally the location of the throne of God and the angels of God, and in most forms of Christianity it is the abode of the righteous dead in the afterlife. In some Christian denominations it is understood as a temporary stage before the resurrection of the dead and the saints' return to the New Earth.\nIn the Book of Acts, the resurrected Jesus ascends to heaven where, as the Nicene Creed states, he now sits at the right hand of God and will return to earth in the Second Coming. According to Catholic, Eastern Orthodox, and Oriental Orthodox teaching, Mary, mother of Jesus, is said to have been assumed into heaven without the corruption of her earthly body; she is venerated as Queen of Heaven.\nIn the Christian Bible, concepts about Christian eschatology, the future \"kingdom of heaven\", and the resurrection of the dead are found, particularly in the book of Revelation and in 1 Corinthians 15.\nDescription\nThe Bible does not provide much information on what Heaven is supposed to be like. As a result, Christian theologians are usually not very specific when describing heaven.\nThe Book of Revelation states that the New Jerusalem will be transported from Heaven to Earth, rather than people from Earth going to Heaven. The description of the gates of New Jerusalem in  inspired the idea of the Pearly gates, which is the informal name for the gateway to heaven according to some Christian denominations."]], "0_corpus": "wikipedia", "1_conv_id": "2d381decf1d1476fbbd0c1c804ed3088", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "What is the antipode of heaven?", "1_output": [["What is the antipode of heaven?", "Heaven\n\nHeaven, or the heavens, is a common religious cosmological or transcendent supernatural place where beings such as deities, angels, souls, saints, or venerated ancestors are said to originate, be enthroned, or reside. According to the beliefs of some religions, heavenly beings can descend to Earth or incarnate and earthly beings can ascend to Heaven in the afterlife or, in exceptional cases, enter Heaven without dying.\nHeaven is often described as a \"highest place\", the holiest place, a Paradise, in contrast to hell or the Underworld or the \"low places\" and universally or conditionally accessible by earthly beings according to various standards of divinity, goodness, piety, faith, or other virtues or right beliefs or simply divine will. Some believe in the possibility of a heaven on Earth in a world to come."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722445209.7995, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "c4c4c875854a41f8a4f23c4dd4df5074", "0_model_name": "embed-english-v3.0", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "0_corpus": "wikipedia", "1_conv_id": "25390507c1a14ef1b32bb1574a1c3f26", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "1_corpus": "wikipedia"}
+{"tstamp": 1722445580.5576, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "33d8228235394657b5ee7c61845ad49f", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "Who won the latest euros?", "0_output": [["Who won the latest euros?", "UEFA European Championship\n\nThe UEFA European Football Championship, less formally the European Championship and informally the Euro or Euros, is the primary association football tournament organised by the Union of European Football Associations (UEFA). The competition is contested by UEFA members' senior men's national teams, determining the continental champion of Europe. It is the second-most watched football tournament in the world after the FIFA World Cup; the Euro 2016 final was watched by a global audience of around 600 million. The competition has been held every four years since 1960, except for 2020, when it was postponed until 2021 due to the COVID-19 pandemic in Europe, but kept the name Euro 2020. Scheduled to be in the even-numbered year between FIFA World Cup tournaments, it was originally called the European Nations' Cup before changing to its current name in 1968. Since 1996, the individual events have been branded as \"UEFA Euro [year]\".\nBefore entering the tournament, all teams other than the host nations (which qualify automatically) compete in a qualifying process. Until 2016, the championship winners could compete in the following year's FIFA Confederations Cup, but were not obliged to do so. From the 2020 edition onwards, the winner competes in the CONMEBOL–UEFA Cup of Champions.\nThe seventeen European Championship tournaments have been won by ten national teams: Spain have won four titles, Germany have won three titles, Italy and France have won two titles, and the Soviet Union, Czechoslovakia, the Netherlands, Denmark, Greece and Portugal have won one title each. To date, Spain is the only team to have won consecutive titles, doing so in 2008 and 2012.\nThe most recent championship, held in Germany in 2024, was won by Spain, who lifted a record fourth European title after beating England 2–1 in the final at Olympiastadion in Berlin.\nHistory"]], "0_corpus": "wikipedia", "1_conv_id": "f03d04a064ae4d97aa465675ae705ebf", "1_model_name": "GritLM/GritLM-7B", "1_prompt": "Who won the latest euros?", "1_output": [["Who won the latest euros?", "UEFA Euro 2016\n\nThe 2016 UEFA European Football Championship, commonly referred to as UEFA Euro 2016 (stylised as UEFA EURO 2016) or simply Euro 2016, was the 15th UEFA European Championship, the quadrennial international men's football championship of Europe organised by UEFA. It was held in France from 10 June to 10 July 2016. Spain were the two-time defending champions, having won the 2008 and 2012 tournaments, but were eliminated in the round of 16 2-0 by Italy. Portugal won the tournament for the first time, following a 1–0 victory after extra time over the host team, France, in the final played at the Stade de France."]], "1_corpus": "wikipedia"}

data/retrieval_individual-407c4836-37e2-4f9f-8e9b-06706cc4440c.jsonl

@@ -24,3 +24,5 @@
 {"tstamp": 1722445253.6548, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722445253.4763, "finish": 1722445253.6548, "ip": "", "conv_id": "e1ca08ee16e14522ab72653bcd36b8b7", "model_name": "GritLM/GritLM-7B", "prompt": "remote desktop connection denied because the user account is not authorized?", "output": [["remote desktop connection denied because the user account is not authorized?", "Superuser Stackexchange\n\nQ: How to grant remote desktop right to a user in Windows Server 2008? I create a user and add it to group Remote Desktop Users but I cannot still remote using mstsc. The error message said the user/group doesn't have the right to remote.\nMy question is how can I grant this right to the group?\nThanks!\n\nA: Check in the group policy editor (gpedit.msc) under Computer Config > Windows Settings > Security Settings > Local Policies > User Rights Assignment. Find the entry for \"Allow log on through remote desktop services\" and \"deny log on through remote desktop services\", and see if the groups in question are in either of those categories. Deny permissions will usually override allow permissions.\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722445343.7295, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722445342.5281, "finish": 1722445343.7295, "ip": "", "conv_id": "96be454fd12343d0af9d7fa31a927266", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "A study on improving traffic forecasting accuracy by combining future traffic volume with historical data.", "output": [["A study on improving traffic forecasting accuracy by combining future traffic volume with historical data.", "Title: Networkwide Traffic State Forecasting Using Exogenous Information: A Multi-Dimensional Graph Attention-Based Approach\n\nAbstract: Traffic state forecasting is crucial for traffic management and control strategies, as well as user- and system-level decision making in the transportation network. While traffic forecasting has been approached with a variety of techniques over the last couple of decades, most approaches simply rely on endogenous traffic variables for state prediction, despite the evidence that exogenous factors can significantly impact traffic conditions. This paper proposes a multi-dimensional spatio-temporal graph attention-based traffic prediction approach (M-STGAT), which predicts traffic based on past observations of speed, along with lane closure events, temperature, and visibility across the transportation network. The approach is based on a graph attention network architecture, which also learns based on the structure of the transportation network on which these variables are observed. Numerical experiments are performed using traffic speed and lane closure data from the California Department of Transportation (Caltrans) Performance Measurement System (PeMS). The corresponding weather data were downloaded from the National Oceanic and Atmospheric Administration (NOOA) Automated Surface Observing Systems (ASOS). For comparison, the numerical experiments implement three alternative models which do not allow for the multi-dimensional input. The M-STGAT is shown to outperform the three alternative models, when performing tests using our primary data set for prediction with a 30-, 45-, and 60-minute prediction horizon, in terms of three error measures: Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). However, the model's transferability can vary for different transfer data sets and this aspect may require further investigation."]], "corpus": "arxiv"}
 {"tstamp": 1722445343.7295, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722445342.5281, "finish": 1722445343.7295, "ip": "", "conv_id": "4ab9f7d1179f49ff8c1b741dd7ab39ef", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "A study on improving traffic forecasting accuracy by combining future traffic volume with historical data.", "output": [["A study on improving traffic forecasting accuracy by combining future traffic volume with historical data.", "Title: Informed along the road: roadway capacity driven graph convolution network for network-wide traffic prediction\n\nAbstract: While deep learning has shown success in predicting traffic states, most methods treat it as a general prediction task without considering transportation aspects. Recently, graph neural networks have proven effective for this task, but few incorporate external factors that impact roadway capacity and traffic flow. This study introduces the Roadway Capacity Driven Graph Convolution Network (RCDGCN) model, which incorporates static and dynamic roadway capacity attributes in spatio-temporal settings to predict network-wide traffic states. The model was evaluated on two real-world datasets with different transportation factors: the ICM-495 highway network and an urban network in Manhattan, New York City. Results show RCDGCN outperformed baseline methods in forecasting accuracy. Analyses, including ablation experiments, weight analysis, and case studies, investigated the effect of capacity-related factors. The study demonstrates the potential of using RCDGCN for transportation system management."]], "corpus": "arxiv"}
+{"tstamp": 1722445564.3919, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722445564.0756, "finish": 1722445564.3919, "ip": "", "conv_id": "33d8228235394657b5ee7c61845ad49f", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "Who won the latest euros?", "output": [["Who won the latest euros?", "UEFA European Championship\n\nThe UEFA European Football Championship, less formally the European Championship and informally the Euro or Euros, is the primary association football tournament organised by the Union of European Football Associations (UEFA). The competition is contested by UEFA members' senior men's national teams, determining the continental champion of Europe. It is the second-most watched football tournament in the world after the FIFA World Cup; the Euro 2016 final was watched by a global audience of around 600 million. The competition has been held every four years since 1960, except for 2020, when it was postponed until 2021 due to the COVID-19 pandemic in Europe, but kept the name Euro 2020. Scheduled to be in the even-numbered year between FIFA World Cup tournaments, it was originally called the European Nations' Cup before changing to its current name in 1968. Since 1996, the individual events have been branded as \"UEFA Euro [year]\".\nBefore entering the tournament, all teams other than the host nations (which qualify automatically) compete in a qualifying process. Until 2016, the championship winners could compete in the following year's FIFA Confederations Cup, but were not obliged to do so. From the 2020 edition onwards, the winner competes in the CONMEBOL–UEFA Cup of Champions.\nThe seventeen European Championship tournaments have been won by ten national teams: Spain have won four titles, Germany have won three titles, Italy and France have won two titles, and the Soviet Union, Czechoslovakia, the Netherlands, Denmark, Greece and Portugal have won one title each. To date, Spain is the only team to have won consecutive titles, doing so in 2008 and 2012.\nThe most recent championship, held in Germany in 2024, was won by Spain, who lifted a record fourth European title after beating England 2–1 in the final at Olympiastadion in Berlin.\nHistory"]], "corpus": "wikipedia"}
+{"tstamp": 1722445564.3919, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722445564.0756, "finish": 1722445564.3919, "ip": "", "conv_id": "f03d04a064ae4d97aa465675ae705ebf", "model_name": "GritLM/GritLM-7B", "prompt": "Who won the latest euros?", "output": [["Who won the latest euros?", "UEFA Euro 2016\n\nThe 2016 UEFA European Football Championship, commonly referred to as UEFA Euro 2016 (stylised as UEFA EURO 2016) or simply Euro 2016, was the 15th UEFA European Championship, the quadrennial international men's football championship of Europe organised by UEFA. It was held in France from 10 June to 10 July 2016. Spain were the two-time defending champions, having won the 2008 and 2012 tournaments, but were eliminated in the round of 16 2-0 by Italy. Portugal won the tournament for the first time, following a 1–0 victory after extra time over the host team, France, in the final played at the Stade de France."]], "corpus": "wikipedia"}