Scheduled Commit

data/clustering_battle-8abaf4d0-63ed-4c7b-8187-1a073a1031a0.jsonl

@@ -1,2 +1,5 @@
 {"tstamp": 1725632290.809, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "0ba6cd3d56e84301b747194182f8c0d5", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "0_ncluster": 2, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "00df5477340248e388f84d866168e120", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "1_ncluster": 2, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1725746522.1025, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "f572473d732b4bb88a0f1c30586bdc43", "0_model_name": "text-embedding-004", "0_prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "0_ncluster": 2, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "8734540a774f4dd0b0b18b83d1cacecf", "1_model_name": "GritLM/GritLM-7B", "1_prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "1_ncluster": 2, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
+{"tstamp": 1725777116.9586, "task_type": "clustering", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "f48e0fe9c3b444679cde3a66d5993172", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": ["rectangle", "circle", "pentagon", "oak", "willow", "pine", "birch", "polar", "cold", "semi-arid", "composite", "shield", "cinder cone", "caldera", "lava dome", "Brave", "Firefox", "Chrome", "Opera"], "0_ncluster": 5, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "39a0ae36417743dbb4f59b21e6bd3369", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": ["rectangle", "circle", "pentagon", "oak", "willow", "pine", "birch", "polar", "cold", "semi-arid", "composite", "shield", "cinder cone", "caldera", "lava dome", "Brave", "Firefox", "Chrome", "Opera"], "1_ncluster": 5, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
+{"tstamp": 1725777170.6205, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "3e585274335a4748a16ee5493eba8e82", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": ["nebula", "galaxy", "asteroid", "comet", "gray", "brunette", "redhead", "blonde", "progressive", "convex", "toric", "concave", "ballad", "haiku", "sonnet", "ode"], "0_ncluster": 4, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "8b98a160849e4c0cb11baffd745b1390", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": ["nebula", "galaxy", "asteroid", "comet", "gray", "brunette", "redhead", "blonde", "progressive", "convex", "toric", "concave", "ballad", "haiku", "sonnet", "ode"], "1_ncluster": 4, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
+{"tstamp": 1725777281.7312, "task_type": "clustering", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "d9191dfc6ad4488b9d050d1bdff5015d", "0_model_name": "GritLM/GritLM-7B", "0_prompt": ["canoe", "catamaran", "sailboat", "kayak", "motorboat", "brain", "stomach", "lungs", "heart", "kidneys", "liver", "pancreas", "triangle", "pentagon", "rectangle", "hexagon", "circle", "square", "octagon", "Leo", "Taurus", "Orion", "Scorpius", "Cassiopeia", "Ursa Major", "hydrogen", "oxygen"], "0_ncluster": 5, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "b740c6f569aa44cba0bf49bcf288323d", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": ["canoe", "catamaran", "sailboat", "kayak", "motorboat", "brain", "stomach", "lungs", "heart", "kidneys", "liver", "pancreas", "triangle", "pentagon", "rectangle", "hexagon", "circle", "square", "octagon", "Leo", "Taurus", "Orion", "Scorpius", "Cassiopeia", "Ursa Major", "hydrogen", "oxygen"], "1_ncluster": 5, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}

data/clustering_individual-8abaf4d0-63ed-4c7b-8187-1a073a1031a0.jsonl

@@ -14,3 +14,7 @@
 {"tstamp": 1725746497.5178, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1725746497.2656, "finish": 1725746497.5178, "ip": "", "conv_id": "8734540a774f4dd0b0b18b83d1cacecf", "model_name": "GritLM/GritLM-7B", "prompt": ["Shanghai", "Beijing", "Shenzhen", "Hangzhou", "Seattle", "Boston", "New York", "San Francisco"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1725777046.3458, "task_type": "clustering", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1725777046.234, "finish": 1725777046.3458, "ip": "", "conv_id": "f48e0fe9c3b444679cde3a66d5993172", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": ["rectangle", "circle", "pentagon", "oak", "willow", "pine", "birch", "polar", "cold", "semi-arid", "composite", "shield", "cinder cone", "caldera", "lava dome", "Brave", "Firefox", "Chrome", "Opera"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1725777046.3458, "task_type": "clustering", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1725777046.234, "finish": 1725777046.3458, "ip": "", "conv_id": "39a0ae36417743dbb4f59b21e6bd3369", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": ["rectangle", "circle", "pentagon", "oak", "willow", "pine", "birch", "polar", "cold", "semi-arid", "composite", "shield", "cinder cone", "caldera", "lava dome", "Brave", "Firefox", "Chrome", "Opera"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1725777130.2242, "task_type": "clustering", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1725777130.1355, "finish": 1725777130.2242, "ip": "", "conv_id": "3e585274335a4748a16ee5493eba8e82", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": ["nebula", "galaxy", "asteroid", "comet", "gray", "brunette", "redhead", "blonde", "progressive", "convex", "toric", "concave", "ballad", "haiku", "sonnet", "ode"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1725777130.2242, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1725777130.1355, "finish": 1725777130.2242, "ip": "", "conv_id": "8b98a160849e4c0cb11baffd745b1390", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": ["nebula", "galaxy", "asteroid", "comet", "gray", "brunette", "redhead", "blonde", "progressive", "convex", "toric", "concave", "ballad", "haiku", "sonnet", "ode"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1725777236.4272, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1725777236.2951, "finish": 1725777236.4272, "ip": "", "conv_id": "d9191dfc6ad4488b9d050d1bdff5015d", "model_name": "GritLM/GritLM-7B", "prompt": ["canoe", "catamaran", "sailboat", "kayak", "motorboat", "brain", "stomach", "lungs", "heart", "kidneys", "liver", "pancreas", "triangle", "pentagon", "rectangle", "hexagon", "circle", "square", "octagon", "Leo", "Taurus", "Orion", "Scorpius", "Cassiopeia", "Ursa Major", "hydrogen", "oxygen"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1725777236.4272, "task_type": "clustering", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1725777236.2951, "finish": 1725777236.4272, "ip": "", "conv_id": "b740c6f569aa44cba0bf49bcf288323d", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": ["canoe", "catamaran", "sailboat", "kayak", "motorboat", "brain", "stomach", "lungs", "heart", "kidneys", "liver", "pancreas", "triangle", "pentagon", "rectangle", "hexagon", "circle", "square", "octagon", "Leo", "Taurus", "Orion", "Scorpius", "Cassiopeia", "Ursa Major", "hydrogen", "oxygen"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/retrieval_battle-8abaf4d0-63ed-4c7b-8187-1a073a1031a0.jsonl

@@ -21,3 +21,4 @@
 {"tstamp": 1725729216.8405, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "36b924247cd34f05ae5001d5588959e7", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "Why did the machines in The Matrix keep humans around?", "0_output": [["Why did the machines in The Matrix keep humans around?", "Scifi Stackexchange\n\nQ: Why use full brained humans as batteries? \nPossible Duplicate:\nIs the basic premise of humans as a power source in The Matrix reasonable?\nWhy Did the Machines Even Bother With a Matrix? \n\nThe machines in The Matrix series use humans as batteries. That's OK. But why did the machines not alter their livestock such that the matrix itself would be unnecessary?  \nConsider the recently proposed headless chicken farm. Something similar for the human batteries would seem to be an ideal solution for logical machines. No longer would they need to spend resources maintaining a virtual reality for their batteries. The energy, physical hardware and programs running the matrix could then be put to who knows what other uses.\n\nA: The explanation for the use of human brains would seem to be the fact that our complex thoughts lead to higher level of brain activity, thus resulting in exponentially more synapses firing (i.e. more electrical activity to harvest).\nThat makes human brains far more efficient than the brains of animals, even with the overhead of maintaining the matrix.  Plus, that movie would have really sucked... dumb cows and chickens revolt against the machines!  ;)\n"]], "0_corpus": "stackexchange", "1_conv_id": "0f5922c0a08348b79c0dddab0f013d81", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "Why did the machines in The Matrix keep humans around?", "1_output": [["Why did the machines in The Matrix keep humans around?", "Scifi Stackexchange\n\nQ: Why use full brained humans as batteries? \nPossible Duplicate:\nIs the basic premise of humans as a power source in The Matrix reasonable?\nWhy Did the Machines Even Bother With a Matrix? \n\nThe machines in The Matrix series use humans as batteries. That's OK. But why did the machines not alter their livestock such that the matrix itself would be unnecessary?  \nConsider the recently proposed headless chicken farm. Something similar for the human batteries would seem to be an ideal solution for logical machines. No longer would they need to spend resources maintaining a virtual reality for their batteries. The energy, physical hardware and programs running the matrix could then be put to who knows what other uses.\n\nA: The explanation for the use of human brains would seem to be the fact that our complex thoughts lead to higher level of brain activity, thus resulting in exponentially more synapses firing (i.e. more electrical activity to harvest).\nThat makes human brains far more efficient than the brains of animals, even with the overhead of maintaining the matrix.  Plus, that movie would have really sucked... dumb cows and chickens revolt against the machines!  ;)\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1725729243.2915, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "f6dcf2d1a64447feb6a1394ab0d0996b", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "In English when to use –, — vs -?", "0_output": [["In English when to use –, — vs -?", "Math Stackexchange\n\nQ: What do mathematicians mean by \"mild condition\"? On some papers you read online you will find theorems dabbled with:\n\n\"Under a relatively mild condition, ....\"\n\nWhat do mathematicians mean by this and what are some examples of correct usage?\n\nA: A condition that is not too hard to get, or one that is in practice always fulfilled. \nFor example in many circumstances a function being bounded or measurable or continuous. \nA mild condition would be the opposite of a strong condition.\nBeing continuous is a more mild condition than being analytic, which is a stronger condition than being smooth. \n"]], "0_corpus": "stackexchange", "1_conv_id": "29280e72e6f94fad82af97ac9621d99e", "1_model_name": "BM25", "1_prompt": "In English when to use –, — vs -?", "1_output": [["In English when to use –, — vs -?", "English Stackexchange\n\nQ: When to use \"If I was\" vs. \"If I were\"? \n*\n\n*If I was...\n\n*If I were...\n\n\nWhen is it correct to use \"If I was\" vs. \"If I were\" in standard English?\n\nA: When in doubt, always use the subjunctive mood:\n\nIf I were you...\n\nIt will make you sound smarter and it is technically correct since \"the subjunctive mood is used to express a wish or possible situation that is currently not true.\"\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1725744583.6767, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "3174aad863794924a66790c142f8249d", "0_model_name": "text-embedding-3-large", "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": "1855cb586e1c48c6932c2f2d885db6a0", "1_model_name": "BAAI/bge-large-en-v1.5", "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": 1725777386.1694, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "204ba524f02b454cb31f284cadb61272", "0_model_name": "embed-english-v3.0", "0_prompt": "Enhancing speaker representation in self-supervised learning with new data augmentation and contrastive loss methods", "0_output": [["Enhancing speaker representation in self-supervised learning with new data augmentation and contrastive loss methods", "Title: A comprehensive study on self-supervised distillation for speaker representation learning\n\nAbstract: In real application scenarios, it is often challenging to obtain a large amount of labeled data for speaker representation learning due to speaker privacy concerns. Self-supervised learning with no labels has become a more and more promising way to solve it. Compared with contrastive learning, self-distilled approaches use only positive samples in the loss function and thus are more attractive. In this paper, we present a comprehensive study on self-distilled self-supervised speaker representation learning, especially on critical data augmentation. Our proposed strategy of audio perturbation augmentation has pushed the performance of the speaker representation to a new limit. The experimental results show that our model can achieve a new SoTA on Voxceleb1 speaker verification evaluation benchmark ( i.e., equal error rate (EER) 2.505%, 2.473%, and 4.791% for trial Vox1-O, Vox1-E and Vox1-H , respectively), discarding any speaker labels in the training phase."]], "0_corpus": "arxiv", "1_conv_id": "aa824b4fb0614c6181fba27461922d38", "1_model_name": "voyage-multilingual-2", "1_prompt": "Enhancing speaker representation in self-supervised learning with new data augmentation and contrastive loss methods", "1_output": [["Enhancing speaker representation in self-supervised learning with new data augmentation and contrastive loss methods", "Title: Augmentation adversarial training for self-supervised speaker recognition\n\nAbstract: The goal of this work is to train robust speaker recognition models without speaker labels. Recent works on unsupervised speaker representations are based on contrastive learning in which they encourage within-utterance embeddings to be similar and across-utterance embeddings to be dissimilar. However, since the within-utterance segments share the same acoustic characteristics, it is difficult to separate the speaker information from the channel information. To this end, we propose augmentation adversarial training strategy that trains the network to be discriminative for the speaker information, while invariant to the augmentation applied. Since the augmentation simulates the acoustic characteristics, training the network to be invariant to augmentation also encourages the network to be invariant to the channel information in general. Extensive experiments on the VoxCeleb and VOiCES datasets show significant improvements over previous works using self-supervision, and the performance of our self-supervised models far exceed that of humans."]], "1_corpus": "arxiv"}

data/retrieval_individual-8abaf4d0-63ed-4c7b-8187-1a073a1031a0.jsonl

@@ -92,3 +92,5 @@
 {"tstamp": 1725744572.2052, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1725744570.0037, "finish": 1725744572.2052, "ip": "", "conv_id": "1855cb586e1c48c6932c2f2d885db6a0", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "Which test was devised to determine whether robots can think?", "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)."]], "corpus": "wikipedia"}
 {"tstamp": 1725744748.4427, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1725744747.998, "finish": 1725744748.4427, "ip": "", "conv_id": "e59b0dbcc3714de4ba7d6180b91f3357", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "can we delete nohup.out file?", "output": [["can we delete nohup.out file?", "Tex Stackexchange\n\nQ: Longtable - aligning caption to left \nPossible Duplicate:\nFormating captions in longtable tables \n\nIs it possible to align longtable caption to the left side of paper ? Standard table and tabular command align caption to the left and I would like to achieve the same effect in longtable. Is it possible ??\n\nA: As Martin noted, use the caption package. It has options to change the formatting of the caption. To left align the caption for all tables use the package option singlelinecheck=off. I've used data from the example in your other question to construct the following example:\n\\documentclass{article}\n\n\\usepackage{longtable}\n\n\\usepackage[singlelinecheck=off]{caption}\n\n\\begin{document}\n\n\\begin{longtable}{|c|c|c|c|c|c|c|c|c|}\n\\caption{Wyniki rozpoznawania obrazów dla obrazu zapytania 004\\_0001.jp w katalogu baseballbat}\\\\\\hline\n054\\_0020.jpg & 209 & ring &054\\_0020.jpg & 209 & 10089.1  & celu & time & 0.98  \\\\ \\hline\n\\end{longtable}\n\n\\end{document}\n\n"]], "corpus": "stackexchange"}
 {"tstamp": 1725744748.4427, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1725744747.998, "finish": 1725744748.4427, "ip": "", "conv_id": "3dac199bebf745c19b9add09e8df2077", "model_name": "voyage-multilingual-2", "prompt": "can we delete nohup.out file?", "output": [["can we delete nohup.out file?", "Serverfault Stackexchange\n\nQ: deleting nohup.out file I have a file called \"nohup.out\".\nIt is consuming a lot of space and I will like to remove it.\n/opt/apache-activemq-5.2.0/bin/nohup.out: 4.2G\n\nWill it affect the application in any way? I do not want logs, only disk space.\n\nA: That's the output of a file produced by the nohup program, which is used to run things in the context of a shell, but without the program being killed when the shell is exited.  You can delete it if you don't want what's in it, but if the program that created the file is still running, then the disk space won't actually be reclaimed until the program exits.\nAlso, the chances are that whatever created the file will probably do it again in the future, so you really want to run activemq as a proper daemon, rather than someone logging in and running it in a shell with nohup.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1725777330.9505, "task_type": "retrieval", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1725777330.5675, "finish": 1725777330.9505, "ip": "", "conv_id": "204ba524f02b454cb31f284cadb61272", "model_name": "embed-english-v3.0", "prompt": "Enhancing speaker representation in self-supervised learning with new data augmentation and contrastive loss methods", "output": [["Enhancing speaker representation in self-supervised learning with new data augmentation and contrastive loss methods", "Title: A comprehensive study on self-supervised distillation for speaker representation learning\n\nAbstract: In real application scenarios, it is often challenging to obtain a large amount of labeled data for speaker representation learning due to speaker privacy concerns. Self-supervised learning with no labels has become a more and more promising way to solve it. Compared with contrastive learning, self-distilled approaches use only positive samples in the loss function and thus are more attractive. In this paper, we present a comprehensive study on self-distilled self-supervised speaker representation learning, especially on critical data augmentation. Our proposed strategy of audio perturbation augmentation has pushed the performance of the speaker representation to a new limit. The experimental results show that our model can achieve a new SoTA on Voxceleb1 speaker verification evaluation benchmark ( i.e., equal error rate (EER) 2.505%, 2.473%, and 4.791% for trial Vox1-O, Vox1-E and Vox1-H , respectively), discarding any speaker labels in the training phase."]], "corpus": "arxiv"}
+{"tstamp": 1725777330.9505, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1725777330.5675, "finish": 1725777330.9505, "ip": "", "conv_id": "aa824b4fb0614c6181fba27461922d38", "model_name": "voyage-multilingual-2", "prompt": "Enhancing speaker representation in self-supervised learning with new data augmentation and contrastive loss methods", "output": [["Enhancing speaker representation in self-supervised learning with new data augmentation and contrastive loss methods", "Title: Augmentation adversarial training for self-supervised speaker recognition\n\nAbstract: The goal of this work is to train robust speaker recognition models without speaker labels. Recent works on unsupervised speaker representations are based on contrastive learning in which they encourage within-utterance embeddings to be similar and across-utterance embeddings to be dissimilar. However, since the within-utterance segments share the same acoustic characteristics, it is difficult to separate the speaker information from the channel information. To this end, we propose augmentation adversarial training strategy that trains the network to be discriminative for the speaker information, while invariant to the augmentation applied. Since the augmentation simulates the acoustic characteristics, training the network to be invariant to augmentation also encourages the network to be invariant to the channel information in general. Extensive experiments on the VoxCeleb and VOiCES datasets show significant improvements over previous works using self-supervision, and the performance of our self-supervised models far exceed that of humans."]], "corpus": "arxiv"}