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data/clustering_individual-f62bcad9-19c4-430b-a7ba-aa2bfeba99dc.jsonl

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+{"tstamp": 1722404039.6586, "task_type": "clustering", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722404030.4965, "finish": 1722404039.6586, "ip": "", "conv_id": "645bc99853634e95b1d3d7d38bf439e4", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": ["canine", "wisdom tooth", "cricket", "tennis", "sneakers", "high heels", "loafers", "sandals", "boots", "hydroelectric", "biomass", "quill", "crayon", "fountain pen", "pencil", "chalk"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722404039.6586, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722404030.4965, "finish": 1722404039.6586, "ip": "", "conv_id": "8920ba375d3d4b5b8da667b39fb2aee1", "model_name": "text-embedding-004", "prompt": ["canine", "wisdom tooth", "cricket", "tennis", "sneakers", "high heels", "loafers", "sandals", "boots", "hydroelectric", "biomass", "quill", "crayon", "fountain pen", "pencil", "chalk"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/retrieval_individual-81269da8-6dc7-417a-8e0a-245c4a753482.jsonl

@@ -104,3 +104,5 @@
 {"tstamp": 1722402188.6288, "task_type": "retrieval", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1722402188.0897, "finish": 1722402188.6288, "ip": "", "conv_id": "35b60ee5cd7a4e50bfccda7f1185ddae", "model_name": "text-embedding-3-large", "prompt": "where did the easter lily get its name", "output": [["where did the easter lily get its name", "Lilium longiflorum\n\nUse in Christian symbolism Lilium longiflorum is known as the Easter lily because in Christianity, it is a symbol of the resurrection of Christ, which is celebrated during Eastertide. The \"lily has always been highly regarded in the Church\", as Jesus referenced the flower, saying \"Consider the lilies how they grow: they toil not, they spin not; and yet I say unto you, that Solomon in all his glory was not arrayed like one of these\" (). Moreover, according to pious legend, \"after Jesus' death and resurrection, some of these beautiful flowers were found growing in the Garden of Gethsemane, where Jesus went to pray the night before His crucifixion. Legend has it that these flowers sprung up where drops of Jesus' sweat fell as he prayed\". In many Christian churches, the chancel is adorned with Easter lilies throughout the Paschal season. A poem of the early 1900s titled Easter Lilies by  Mrs. S.R. Allen, delineates this custom:\nHistory\nIn 1903, the USDA's Agricultural Research Services (ARS) started to distribute disease-free plant materials and seeds.\nIn Bermuda, from the 1890s to the early 1900s, there was from over some 200 fields a thriving export trade of lily bulbs by sea to New York. A disease then started affecting the lilies: a virus was identified by Lawrence Ogilvie, the Bermuda Government's first plant pathologist, serving from 1923 to 1928.\nIn 1924 Lawrence Ogilvie (age 25) saved the industry by identifying the problem to be not aphid damage as previously thought, and instituting controls in the fields and packing houses. There was a marked improvement in exporting 23 cases of lily bulbs in 1918 to 6043 cases in 1927 from the then 204 lily fields. With the disease eliminated by him, he saved the economy of Bermuda (lilies and early vegetables sent by ship to New York were then economically much more important for Bermuda than hotels and financial services are now).\nLawrence Ogilvie was established by his skills as a successful young scientist, still in his 20s, when he had a 3-inch column describing his work published in 1927 in the world's premier scientific-journal Nature."]], "corpus": "wikipedia"}
 {"tstamp": 1722403583.4794, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722403583.172, "finish": 1722403583.4794, "ip": "", "conv_id": "6f8b359afa074fe9b99960410ca68854", "model_name": "embed-english-v3.0", "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": 1722403583.4794, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722403583.172, "finish": 1722403583.4794, "ip": "", "conv_id": "ed9faaf063f642cd872d3b354dbe1376", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "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: Gauge fixing, BRS invariance and Ward identities for randomly stirred flows\n\nAbstract: The Galilean invariance of the Navier-Stokes equation is shown to be akin to a global gauge symmetry familiar from quantum field theory. This symmetry leads to a multiple counting of infinitely many inertial reference frames in the path integral approach to randomly stirred fluids. This problem is solved by fixing the gauge, i.e., singling out one reference frame. The gauge fixed theory has an underlying Becchi-Rouet-Stora (BRS) symmetry which leads to the Ward identity relating the exact inverse response and vertex functions. This identification of Galilean invariance as a gauge symmetry is explored in detail, for different gauge choices and by performing a rigorous examination of a discretized version of the theory. The Navier-Stokes equation is also invariant under arbitrary rectilinear frame accelerations, known as extended Galilean invariance (EGI). We gauge fix this extended symmetry and derive the generalized Ward identity that follows from the BRS invariance of the gauge-fixed theory. This new Ward identity reduces to the standard one in the limit of zero acceleration. This gauge-fixing approach unambiguously shows that Galilean invariance and EGI constrain only the zero mode of the vertex but none of the higher wavenumber modes."]], "corpus": "arxiv"}
+{"tstamp": 1722403983.1594, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722403982.8263, "finish": 1722403983.1594, "ip": "", "conv_id": "1f792446858649febdcd92f6bf7b0b37", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "Research on combining sentiment analysis and market data for volatility forecasting.", "output": [["Research on combining sentiment analysis and market data for volatility forecasting.", "Title: Volatility forecasting using Deep Learning and sentiment analysis\n\nAbstract: Several studies have shown that deep learning models can provide more accurate volatility forecasts than the traditional methods used within this domain. This paper presents a composite model that merges a deep learning approach with sentiment analysis for predicting market volatility. To classify public sentiment, we use a Convolutional Neural Network, which obtained data from Reddit global news headlines. We then describe a composite forecasting model, a Long-Short-Term-Memory Neural Network method, to use historical sentiment and the previous day's volatility to make forecasts. We employed this method on the past volatility of the S&P500 and the major BRICS indices to corroborate its effectiveness. Our results demonstrate that including sentiment can improve Deep Learning volatility forecasting models. However, in contrast to return forecasting, the performance benefits of including sentiment appear for volatility forecasting appears to be market specific."]], "corpus": "arxiv"}
+{"tstamp": 1722403983.1594, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722403982.8263, "finish": 1722403983.1594, "ip": "", "conv_id": "50604820dc7b45a784d897f43d88f88f", "model_name": "text-embedding-004", "prompt": "Research on combining sentiment analysis and market data for volatility forecasting.", "output": [["Research on combining sentiment analysis and market data for volatility forecasting.", "Title: A Sentiment Analysis Approach to the Prediction of Market Volatility\n\nAbstract: Prediction and quantification of future volatility and returns play an important role in financial modelling, both in portfolio optimization and risk management. Natural language processing today allows to process news and social media comments to detect signals of investors' confidence. We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements. We investigated the strength of the correlation between sentiment measures on a given day and market volatility and returns observed the next day. The findings suggest that there is evidence of correlation between sentiment and stock market movements: the sentiment captured from news headlines could be used as a signal to predict market returns; the same does not apply for volatility. Also, in a surprising finding, for the sentiment found in Twitter comments we obtained a correlation coefficient of -0.7, and p-value below 0.05, which indicates a strong negative correlation between positive sentiment captured from the tweets on a given day and the volatility observed the next day. We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information by deploying topic modelling, based on Latent Dirichlet Allocation, to extract feature vectors from a collection of tweets and financial news. The obtained features were used as additional input to the classifier. Thanks to the combination of sentiment and topic modelling our classifier achieved a directional prediction accuracy for volatility of 63%."]], "corpus": "arxiv"}

data/retrieval_individual-f62bcad9-19c4-430b-a7ba-aa2bfeba99dc.jsonl

@@ -0,0 +1,4 @@
+{"tstamp": 1722404021.5866, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722404020.1107, "finish": 1722404021.5866, "ip": "", "conv_id": "e1b36a45539a4451b6a7d436badf788c", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "who scored the most points in a game nba history", "output": [["who scored the most points in a game nba history", "List of NBA single-game scoring leaders\n\nThis is a complete listing of National Basketball Association players who have scored 60 or more points in a regular season game.\nThis feat has been accomplished 89 times in NBA history. Thirty-six players have scored 60 or more points in a game, while just ten players have joined the 70-point club. Only 10 players have scored 60 or more points on more than one occasion: Wilt Chamberlain (32 times); Kobe Bryant (6 times); Damian Lillard (5 times); Michael Jordan and James Harden (4 times); Elgin Baylor (3 times); and Karl-Anthony Towns, Devin Booker, Luka Dončić, and Stephen Curry (2 times). Chamberlain holds the single-game scoring record, having scored 100 points in a game in 1962. The youngest player to score at least 60 points in a regular season game is Devin Booker (70 points; 20 years and 145 days), and the oldest is Bryant (60 points; 37 years and 234 days)."]], "corpus": "wikipedia"}
+{"tstamp": 1722404021.5866, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722404020.1107, "finish": 1722404021.5866, "ip": "", "conv_id": "cfd99c56377e4533b7115306e6bf5162", "model_name": "embed-english-v3.0", "prompt": "who scored the most points in a game nba history", "output": [["who scored the most points in a game nba history", "List of highest-scoring NBA games\n\nSummary The highest-scoring regular-season game in NBA history is the triple-overtime game between the Detroit Pistons and the Denver Nuggets on December 13, 1983. The two teams combined to score 370 points, with the Pistons defeating the Nuggets 186–184. An NBA-record four players scored over 40 points in the game, including the Nuggets' Kiki Vandeweghe with a game-high 51. The two teams also set several other NBA records, including the most points scored by one team (186 points), the most points scored by a losing team (184), the most field goals by two teams (142), most field goals by one team (74) and most assists by two teams (93).\nThe highest-scoring regular season game in regulation was between the Golden State Warriors and the Denver Nuggets on November 2, 1990. In that game, Golden State defeated Denver 162–158. The Warriors' Chris Mullin scored a game-high 38 points. The Nuggets were coached by Doug Moe from 1980 to 1990 and Paul Westhead from 1990 to 1992, both of whom employed a run-and-gun offensive strategy, which focuses on attempting a high number of field goals while also conceding a large number of points on defense. In fact, Moe's and Westhead's Nuggets were participants in four of the ten highest-scoring regular season games in NBA history. The Warriors were coached by Don Nelson from 1988 to 1995 and 2006 to 2010. He employed Nellie Ball, a style of run and gun that uses smaller, more athletic players to outrun opponents. Another notable high-scoring regular season game is a March 2, 1962, game between the Philadelphia Warriors and the New York Knicks. In that game, the Warriors' Wilt Chamberlain scored an NBA-record 100 points."]], "corpus": "wikipedia"}
+{"tstamp": 1722404025.3419, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722404024.8194, "finish": 1722404025.3419, "ip": "", "conv_id": "5ef17d307c164fcf9ce84bb32d4ddf44", "model_name": "GritLM/GritLM-7B", "prompt": "will system restore remove malware?", "output": [["will system restore remove malware?", "Superuser Stackexchange\n\nQ: Can system restore remove virus from the computer? Can a system restore to a past time remove the virus from the computer?\n\nA: The short answer is no.\nSystem Restore can help spread a virus though.\nSee here for more info: Removing Viruses with System Restore. Quoting the relevant parts:\n\nWhen System Restore first appeared in the market, computer manufacturers and other companies claimed ... that it could remove viruses and fix other problems with the computer.\nIn reality, System Restore can create copies of the infected files. And some viruses may be capable of infecting the restore volume as well as the actual system files. When a person cleans their computer using an anti-virus, then uses System Restore, they may inadvertently re-infect the computer. Or if they use System Restore as a means of removal, either the restore will fail (if the anti-virus cleans the virus out during the restore process) or the restore will replace the file with an infected version.\n\n"]], "corpus": "stackexchange"}
+{"tstamp": 1722404025.3419, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722404024.8194, "finish": 1722404025.3419, "ip": "", "conv_id": "6a4fe1dc03d94381ade5917f12c7933a", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "will system restore remove malware?", "output": [["will system restore remove malware?", "Superuser Stackexchange\n\nQ: Can system restore remove virus from the computer? Can a system restore to a past time remove the virus from the computer?\n\nA: The short answer is no.\nSystem Restore can help spread a virus though.\nSee here for more info: Removing Viruses with System Restore. Quoting the relevant parts:\n\nWhen System Restore first appeared in the market, computer manufacturers and other companies claimed ... that it could remove viruses and fix other problems with the computer.\nIn reality, System Restore can create copies of the infected files. And some viruses may be capable of infecting the restore volume as well as the actual system files. When a person cleans their computer using an anti-virus, then uses System Restore, they may inadvertently re-infect the computer. Or if they use System Restore as a means of removal, either the restore will fail (if the anti-virus cleans the virus out during the restore process) or the restore will replace the file with an infected version.\n\n"]], "corpus": "stackexchange"}

data/sts_individual-f62bcad9-19c4-430b-a7ba-aa2bfeba99dc.jsonl

@@ -0,0 +1,2 @@
+{"tstamp": 1722404046.7082, "task_type": "sts", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722404046.5438, "finish": 1722404046.7082, "ip": "", "conv_id": "7203b5a0e93b439b819a84dd0c31776b", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "txt0": "From the work that we do together, everyone benefits.", "txt1": "Nobody benefis from the work that we do.", "txt2": "Everyone benefits from the work we do together.", "output": ""}
+{"tstamp": 1722404046.7082, "task_type": "sts", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722404046.5438, "finish": 1722404046.7082, "ip": "", "conv_id": "e5c14c29ee6c4908a1100b932df0ab2c", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "txt0": "From the work that we do together, everyone benefits.", "txt1": "Nobody benefis from the work that we do.", "txt2": "Everyone benefits from the work we do together.", "output": ""}