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data/retrieval_individual-2cea24b1-39e9-480a-ba22-c617ea05c1fe.jsonl

@@ -212,3 +212,5 @@
 {"tstamp": 1724185665.1779, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1724185662.8014, "finish": 1724185665.1779, "ip": "", "conv_id": "a3946db7668d49a9bb6eb51e46fd07de", "model_name": "BM25", "prompt": "my fair lady", "output": [["my fair lady", "My Fair Lady (film)\n\nJames Berardinelli wrote in a 2018 retrospective review, \"Few genres of films are as magical as musicals, and few musicals are as intelligent and lively as My Fair Lady. It's a classic not because a group of stuffy film experts have labeled it as such, but because it has been, and always will be, a pure joy to experience.\"\nDave Whitaker of DavesMovieDatabase, a film aggregator site that combines other lists with box-office, ratings, and awards, listed My Fair Lady in 2020 as the 100th greatest movie of all time, as the 9th greatest Musical of all time, and as the 30th most awarded movie of all time.\nRetrospective analysis of My Fair Lady has been more mixed, with disagreement between reviewers about whether the movie critiques or affirms misogynistic and classist tropes.\nToday\nAs of 2023, My Fair Lady holds a 95% approval rating on Rotten Tomatoes based on 91 reviews, with an average rating of 8.6/10. The consensus states: \"George Cukor's elegant, colorful adaptation of the beloved stage play is elevated to new heights thanks to winning performances by Audrey Hepburn and Rex Harrison.\" On Metacritic, the film holds a weighted average score of 95 out of 100 based on 15 critics, indicating \"universal acclaim\".\nAwards and nominations"]], "corpus": "wikipedia"}
 {"tstamp": 1724187229.4814, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1724187228.5139, "finish": 1724187229.4814, "ip": "", "conv_id": "60d0a8c3992d4d0587d5130da30358c2", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "who sang the song i wanna be sedated", "output": [["who sang the song i wanna be sedated", "I Wanna Be Sedated\n\n\"I Wanna Be Sedated\" is a song by American punk rock band Ramones, originally released on the band's fourth studio album, Road to Ruin (1978), in September 1978. The B-side of the UK single \"She's the One\" was released on September 21, 1978. The song was later released as a single in the Netherlands in 1979, and in the U.S. in 1980 by RSO Records from the Times Square soundtrack album. It has since remained one of the band's best known songs."]], "corpus": "wikipedia"}
 {"tstamp": 1724187229.4814, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1724187228.5139, "finish": 1724187229.4814, "ip": "", "conv_id": "ef75907373cc4770aa1226b4b006d434", "model_name": "text-embedding-3-large", "prompt": "who sang the song i wanna be sedated", "output": [["who sang the song i wanna be sedated", "I Wanna Be Sedated\n\n\"I Wanna Be Sedated\" is a song by American punk rock band Ramones, originally released on the band's fourth studio album, Road to Ruin (1978), in September 1978. The B-side of the UK single \"She's the One\" was released on September 21, 1978. The song was later released as a single in the Netherlands in 1979, and in the U.S. in 1980 by RSO Records from the Times Square soundtrack album. It has since remained one of the band's best known songs."]], "corpus": "wikipedia"}
+{"tstamp": 1724187302.8781, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1724187300.3261, "finish": 1724187302.8781, "ip": "", "conv_id": "eb78d335ae7b444ba7efc4eeb895a293", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "output": [["I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "Title: MTEB: Massive Text Embedding Benchmark\n\nAbstract: Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation. To solve this problem, we introduce the Massive Text Embedding Benchmark (MTEB). MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages. Through the benchmarking of 33 models on MTEB, we establish the most comprehensive benchmark of text embeddings to date. We find that no particular text embedding method dominates across all tasks. This suggests that the field has yet to converge on a universal text embedding method and scale it up sufficiently to provide state-of-the-art results on all embedding tasks. MTEB comes with open-source code and a public leaderboard at https://github.com/embeddings-benchmark/mteb."]], "corpus": "arxiv"}
+{"tstamp": 1724187302.8781, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1724187300.3261, "finish": 1724187302.8781, "ip": "", "conv_id": "e67a54895bd2417da9741be69bfe06bb", "model_name": "text-embedding-3-large", "prompt": "I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "output": [["I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "Title: MTEB: Massive Text Embedding Benchmark\n\nAbstract: Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation. To solve this problem, we introduce the Massive Text Embedding Benchmark (MTEB). MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages. Through the benchmarking of 33 models on MTEB, we establish the most comprehensive benchmark of text embeddings to date. We find that no particular text embedding method dominates across all tasks. This suggests that the field has yet to converge on a universal text embedding method and scale it up sufficiently to provide state-of-the-art results on all embedding tasks. MTEB comes with open-source code and a public leaderboard at https://github.com/embeddings-benchmark/mteb."]], "corpus": "arxiv"}

data/retrieval_side_by_side-2cea24b1-39e9-480a-ba22-c617ea05c1fe.jsonl

@@ -6,3 +6,4 @@
 {"tstamp": 1724159687.6062, "task_type": "retrieval", "type": "tievote", "models": ["BAAI/bge-large-en-v1.5", "Salesforce/SFR-Embedding-2_R"], "ip": "", "0_conv_id": "a0e57bf133a646bd860e9352e09583a6", "0_model_name": "BAAI/bge-large-en-v1.5", "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 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)."]], "0_corpus": "wikipedia", "1_conv_id": "75d174584ef940d79d00443f34495fa0", "1_model_name": "Salesforce/SFR-Embedding-2_R", "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": 1724176423.1867, "task_type": "retrieval", "type": "leftvote", "models": ["GritLM/GritLM-7B", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "bc8fc3f940864b2a98d592bdf0823d0e", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "is there horse riding in the commonwealth games", "0_output": [["is there horse riding in the commonwealth games", "2022 Commonwealth Games\n\nThe charter also establishes the list of optional sports and disciplines that can be chosen by the organisation of each edition: archery (recurve), basketball (3x3 or 5x5), beach volleyball, cycling (mountain bike and track), rhythmic gymnastics, and shooting (clay target, full bore, small bore and pistol). This same rule also establishes that the following events for athletes with disabilities are optional: wheelchair basketball (3x3), para track-cycling, para-table tennis and para-triathlon. Including compulsory and optional sports (disciplines), there shall be no more than 4 team sports on the programme of a Commonwealth Games. If basketball (3x3) is selected from the pool of optional sports, basketball (wheelchair para 3x3) becomes an obligatory event (or vice versa), in which case the sport of basketball shall be considered as one team sport. In cases when cricket is selected from the pool of optional sports, wheelchair basketball also becomes a part of the programme and can be an exception to the 4 team sports limit. Respecting local demands, an extra sport or some extra events can be included in this list, but they have to be approved by the Commonwealth Games Federation two years before that edition is held. The current rules also determine gender parity, whereby men and women have an equal (or broadly equal) share of events.\nInput to list of disciplines\nOn 22 December 2017, the BBC reported that the organisers of the games were in talks with the International Cricket Council (ICC) about the inclusion of women's cricket. In November 2018, the ICC confirmed that they have submitted a bid to include women's cricket in the Games. The bid was made in partnership with the England and Wales Cricket Board (ECB).\nIt was also reported that shooting was likely to be excluded from the games citing a lack of facilities around Birmingham. Shooting has been included at every Commonwealth Games since Christchurch 1974. In January 2018, the dropping of shooting from the games programme was confirmed by the then CEO of the CGF David Grevemberg. In December 2018, The International Shooting Sport Federation (ISSF) delegation including President of ISSF Vladimir Lisin and CEO of British Shooting (BS) Hamish McInnes visited Birmingham and discussed with the Birmingham organising committee to add shooting in the 2022 Commonwealth Games."]], "0_corpus": "wikipedia", "1_conv_id": "4cdf0352fff94d5eb86feff0b8a48470", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": "is there horse riding in the commonwealth games", "1_output": [["is there horse riding in the commonwealth games", "Equestrian events at the 2024 Summer Olympics\n\nThe equestrian events at the 2024 Summer Olympics in Paris are scheduled to run from 27 July to 6 August at the Palace of Versailles, featuring 200 riders across three disciplines for both individual and team competitions, namely dressage, eventing, and jumping."]], "1_corpus": "wikipedia"}
 {"tstamp": 1724187262.2859, "task_type": "retrieval", "type": "tievote", "models": ["sentence-transformers/all-MiniLM-L6-v2", "text-embedding-3-large"], "ip": "", "0_conv_id": "60d0a8c3992d4d0587d5130da30358c2", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "who sang the song i wanna be sedated", "0_output": [["who sang the song i wanna be sedated", "I Wanna Be Sedated\n\n\"I Wanna Be Sedated\" is a song by American punk rock band Ramones, originally released on the band's fourth studio album, Road to Ruin (1978), in September 1978. The B-side of the UK single \"She's the One\" was released on September 21, 1978. The song was later released as a single in the Netherlands in 1979, and in the U.S. in 1980 by RSO Records from the Times Square soundtrack album. It has since remained one of the band's best known songs."]], "0_corpus": "wikipedia", "1_conv_id": "ef75907373cc4770aa1226b4b006d434", "1_model_name": "text-embedding-3-large", "1_prompt": "who sang the song i wanna be sedated", "1_output": [["who sang the song i wanna be sedated", "I Wanna Be Sedated\n\n\"I Wanna Be Sedated\" is a song by American punk rock band Ramones, originally released on the band's fourth studio album, Road to Ruin (1978), in September 1978. The B-side of the UK single \"She's the One\" was released on September 21, 1978. The song was later released as a single in the Netherlands in 1979, and in the U.S. in 1980 by RSO Records from the Times Square soundtrack album. It has since remained one of the band's best known songs."]], "1_corpus": "wikipedia"}
+{"tstamp": 1724187309.0459, "task_type": "retrieval", "type": "tievote", "models": ["nomic-ai/nomic-embed-text-v1.5", "text-embedding-3-large"], "ip": "", "0_conv_id": "eb78d335ae7b444ba7efc4eeb895a293", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": "I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "0_output": [["I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "Title: MTEB: Massive Text Embedding Benchmark\n\nAbstract: Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation. To solve this problem, we introduce the Massive Text Embedding Benchmark (MTEB). MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages. Through the benchmarking of 33 models on MTEB, we establish the most comprehensive benchmark of text embeddings to date. We find that no particular text embedding method dominates across all tasks. This suggests that the field has yet to converge on a universal text embedding method and scale it up sufficiently to provide state-of-the-art results on all embedding tasks. MTEB comes with open-source code and a public leaderboard at https://github.com/embeddings-benchmark/mteb."]], "0_corpus": "arxiv", "1_conv_id": "e67a54895bd2417da9741be69bfe06bb", "1_model_name": "text-embedding-3-large", "1_prompt": "I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "1_output": [["I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "Title: MTEB: Massive Text Embedding Benchmark\n\nAbstract: Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation. To solve this problem, we introduce the Massive Text Embedding Benchmark (MTEB). MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages. Through the benchmarking of 33 models on MTEB, we establish the most comprehensive benchmark of text embeddings to date. We find that no particular text embedding method dominates across all tasks. This suggests that the field has yet to converge on a universal text embedding method and scale it up sufficiently to provide state-of-the-art results on all embedding tasks. MTEB comes with open-source code and a public leaderboard at https://github.com/embeddings-benchmark/mteb."]], "1_corpus": "arxiv"}