Scheduled Commit

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

@@ -6,3 +6,4 @@
 {"tstamp": 1722442236.114, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "fc59625548c945ab810d99da39f6cdbf", "0_model_name": "text-embedding-3-large", "0_prompt": "Hvilke organisationer har Mette Frederiksen forbindelse til?", "0_output": [["Hvilke organisationer har Mette Frederiksen forbindelse til?", "Mette Frederiksen\n\nMember of Folketing Frederiksen worked as a youth consultant for LO, The Danish Confederation of Trade Unions. She was elected as a member of parliament for Copenhagen County in the 2001 general election which saw the Social Democrats losing the first place and placing second for the first time since 1920. After her election, Frederiksen was named as her party's spokesperson for culture, media and gender equality. In 2002, she received the Nina Bang award for \"showing political courage, enthusiasm andimpact with social feeling\".  Frederiksen received the Ting Prize in 2012.  She co-authored the books Epostler (2003) and From Fight to Culture (2004).\nAfter the 2005 general election loss, Frederiksen became her party's spokesperson for social affairs. Following the election, she also served as the vice-chairperson of the parliamentary group of the Social Democrats. In the 2007 general election that saw the Social Democrats losing two seats, Frederiksen obtained 27,077 votes, placing her seventh in the ranking of the ten Danish politicians with the most votes.\nAfter the 2011 general election that led to a Social Democrats government, Frederiksen served under Prime Minister Helle Thorning-Schmidt as Minister for Employment from 2011 to 2014 and Minister of Justice from 2014 until she succeeded her as party leader. As Minister of Employment, Hendriksen sought reforms of early retirement pensions, flex jobs, and the employment system. The controversial cash assistance reform meant lower cash benefits for young unemployed and provided cohabiting mutual support, among other things.\nLeader of the Social Democrats\nUnder Frederiksen's leadership after the 2015 general election in which the Social Democrats returned to power and gained three seats in the Folketing, the party has moved back to the left on economic issues while taking a conservative stance on immigration.\nPrime Minister of Denmark"]], "0_corpus": "wikipedia", "1_conv_id": "18c745a4015840a4bfb1b883155ee770", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "Hvilke organisationer har Mette Frederiksen forbindelse til?", "1_output": [["Hvilke organisationer har Mette Frederiksen forbindelse til?", "Mette Frederiksen\n\nMember of Folketing Frederiksen worked as a youth consultant for LO, The Danish Confederation of Trade Unions. She was elected as a member of parliament for Copenhagen County in the 2001 general election which saw the Social Democrats losing the first place and placing second for the first time since 1920. After her election, Frederiksen was named as her party's spokesperson for culture, media and gender equality. In 2002, she received the Nina Bang award for \"showing political courage, enthusiasm andimpact with social feeling\".  Frederiksen received the Ting Prize in 2012.  She co-authored the books Epostler (2003) and From Fight to Culture (2004).\nAfter the 2005 general election loss, Frederiksen became her party's spokesperson for social affairs. Following the election, she also served as the vice-chairperson of the parliamentary group of the Social Democrats. In the 2007 general election that saw the Social Democrats losing two seats, Frederiksen obtained 27,077 votes, placing her seventh in the ranking of the ten Danish politicians with the most votes.\nAfter the 2011 general election that led to a Social Democrats government, Frederiksen served under Prime Minister Helle Thorning-Schmidt as Minister for Employment from 2011 to 2014 and Minister of Justice from 2014 until she succeeded her as party leader. As Minister of Employment, Hendriksen sought reforms of early retirement pensions, flex jobs, and the employment system. The controversial cash assistance reform meant lower cash benefits for young unemployed and provided cohabiting mutual support, among other things.\nLeader of the Social Democrats\nUnder Frederiksen's leadership after the 2015 general election in which the Social Democrats returned to power and gained three seats in the Folketing, the party has moved back to the left on economic issues while taking a conservative stance on immigration.\nPrime Minister of Denmark"]], "1_corpus": "wikipedia"}
 {"tstamp": 1722442304.08, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "c11b9d02ecdb4ac29d202f5ce92f0f64", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "What is Venstre?", "0_output": [["What is Venstre?", "Venstre (Denmark)\n\n(V; , ), full name  (), is a conservative-liberal, agrarian political party in Denmark. Founded as part of a peasants' movement against the landed aristocracy, today it espouses an economically liberal, pro-free-market ideology."]], "0_corpus": "wikipedia", "1_conv_id": "3a3a219015e944a08e96fcd2a795e1b8", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "What is Venstre?", "1_output": [["What is Venstre?", "Venstre (Denmark)\n\n(V; , ), full name  (), is a conservative-liberal, agrarian political party in Denmark. Founded as part of a peasants' movement against the landed aristocracy, today it espouses an economically liberal, pro-free-market ideology."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722443951.9871, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "f294c32c8edf4cdb82f34b8988340f89", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "Which country won the most gold medals in the olympic history", "0_output": [["Which country won the most gold medals in the olympic history", "List of Olympic teams by medals won\n\nThe United States has placed first overall in the Summer Olympic Games eighteen times, followed by the Soviet Union with six overall victories (not including the 1992 first-place finish by the Unified Team). In the Winter Olympic Games, Norway has placed first overall nine times and the Soviet Union (prior to its dissolution in 1991) had placed first seven times.\nSummer Olympics\nList by Games\nList by team\nWinter Olympics\nList by Games"]], "0_corpus": "wikipedia", "1_conv_id": "beb89db290a247ab8d2637d64c723268", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "Which country won the most gold medals in the olympic history", "1_output": [["Which country won the most gold medals in the olympic history", "2004 Summer Olympics medal table\n\nThe 2004 Summer Olympics, officially known as the Games of the XXVIII Olympiad, were a summer multi-sport event held in Athens, the capital city of Greece, from 13 to 29 August 2004. A total of 10,625 athletes from 201 countries represented by National Olympic Committees participated in these games, competing in 301 events in 28 sports. Kiribati and Timor Leste competed for the first time in these Olympic Games.\nAthletes from 74 countries won at least one medal. The United States won the most gold medals (36), the most silver medals (40) and the most medals overall (101). China finished second on the International Olympic Committee medal table (though third in terms of total medals), the country's best performance until the 2008 Beijing Olympics, where they were hosts. Russia finished third (second in total medals), and also won the most bronze medals (38). Host nation Greece finished fifteenth, with six gold, six silver, and four bronze medals, in its best total medal haul since 1896."]], "1_corpus": "wikipedia"}
+{"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"}

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

@@ -14,3 +14,5 @@
 {"tstamp": 1722442292.1294, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722442291.7027, "finish": 1722442292.1294, "ip": "", "conv_id": "3a3a219015e944a08e96fcd2a795e1b8", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "What is Venstre?", "output": [["What is Venstre?", "Venstre (Denmark)\n\n(V; , ), full name  (), is a conservative-liberal, agrarian political party in Denmark. Founded as part of a peasants' movement against the landed aristocracy, today it espouses an economically liberal, pro-free-market ideology."]], "corpus": "wikipedia"}
 {"tstamp": 1722443940.8847, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722443938.5083, "finish": 1722443940.8847, "ip": "", "conv_id": "f294c32c8edf4cdb82f34b8988340f89", "model_name": "GritLM/GritLM-7B", "prompt": "Which country won the most gold medals in the olympic history", "output": [["Which country won the most gold medals in the olympic history", "List of Olympic teams by medals won\n\nThe United States has placed first overall in the Summer Olympic Games eighteen times, followed by the Soviet Union with six overall victories (not including the 1992 first-place finish by the Unified Team). In the Winter Olympic Games, Norway has placed first overall nine times and the Soviet Union (prior to its dissolution in 1991) had placed first seven times.\nSummer Olympics\nList by Games\nList by team\nWinter Olympics\nList by Games"]], "corpus": "wikipedia"}
 {"tstamp": 1722443940.8847, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722443938.5083, "finish": 1722443940.8847, "ip": "", "conv_id": "beb89db290a247ab8d2637d64c723268", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "Which country won the most gold medals in the olympic history", "output": [["Which country won the most gold medals in the olympic history", "2004 Summer Olympics medal table\n\nThe 2004 Summer Olympics, officially known as the Games of the XXVIII Olympiad, were a summer multi-sport event held in Athens, the capital city of Greece, from 13 to 29 August 2004. A total of 10,625 athletes from 201 countries represented by National Olympic Committees participated in these games, competing in 301 events in 28 sports. Kiribati and Timor Leste competed for the first time in these Olympic Games.\nAthletes from 74 countries won at least one medal. The United States won the most gold medals (36), the most silver medals (40) and the most medals overall (101). China finished second on the International Olympic Committee medal table (though third in terms of total medals), the country's best performance until the 2008 Beijing Olympics, where they were hosts. Russia finished third (second in total medals), and also won the most bronze medals (38). Host nation Greece finished fifteenth, with six gold, six silver, and four bronze medals, in its best total medal haul since 1896."]], "corpus": "wikipedia"}
+{"tstamp": 1722443999.2944, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1722443998.2791, "finish": 1722443999.2944, "ip": "", "conv_id": "fa4418617a5a491b8a7ede90483569ba", "model_name": "BM25", "prompt": "Fine-tuning BERT for optimal text classification performance on multiple datasets", "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."]], "corpus": "arxiv"}
+{"tstamp": 1722443999.2944, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722443998.2791, "finish": 1722443999.2944, "ip": "", "conv_id": "e87880dd7cd94e34b26d6ec08910f019", "model_name": "text-embedding-004", "prompt": "Fine-tuning BERT for optimal text classification performance on multiple datasets", "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."]], "corpus": "arxiv"}