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data/clustering_battle-3d6410bd-df84-4241-a31a-72548a10ef27.jsonl

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+{"tstamp": 1734940950.346, "task_type": "clustering", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "4266516c9b9048d7b28f61e7e68fb5d5", "0_model_name": "embed-english-v3.0", "0_prompt": ["If someone online buys something off of my Amazon wish list, do they get my full name and address?", "Package \"In Transit\" over a week. No scheduled delivery date, no locations. What's up?", "Can Amazon gift cards replace a debit card?", "Homesick GWS star Cameron McCarthy on road to recovery", "Accidently ordered 2 of an item, how do I only return 1? For free?", "Need help ASAP, someone ordering in my account", "So who's everyone tipping for Round 1?"], "0_ncluster": 2, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "01d1a51939c14e3e80e64b86215d95df", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": ["If someone online buys something off of my Amazon wish list, do they get my full name and address?", "Package \"In Transit\" over a week. No scheduled delivery date, no locations. What's up?", "Can Amazon gift cards replace a debit card?", "Homesick GWS star Cameron McCarthy on road to recovery", "Accidently ordered 2 of an item, how do I only return 1? For free?", "Need help ASAP, someone ordering in my account", "So who's everyone tipping for Round 1?"], "1_ncluster": 2, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}

data/clustering_individual-3d6410bd-df84-4241-a31a-72548a10ef27.jsonl

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+{"tstamp": 1734940899.0673, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1734940861.4652, "finish": 1734940899.0673, "ip": "", "conv_id": "4266516c9b9048d7b28f61e7e68fb5d5", "model_name": "embed-english-v3.0", "prompt": ["If someone online buys something off of my Amazon wish list, do they get my full name and address?", "Package \"In Transit\" over a week. No scheduled delivery date, no locations. What's up?", "Can Amazon gift cards replace a debit card?", "Homesick GWS star Cameron McCarthy on road to recovery", "Accidently ordered 2 of an item, how do I only return 1? For free?", "Need help ASAP, someone ordering in my account", "So who's everyone tipping for Round 1?"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1734940899.0673, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1734940861.4652, "finish": 1734940899.0673, "ip": "", "conv_id": "01d1a51939c14e3e80e64b86215d95df", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["If someone online buys something off of my Amazon wish list, do they get my full name and address?", "Package \"In Transit\" over a week. No scheduled delivery date, no locations. What's up?", "Can Amazon gift cards replace a debit card?", "Homesick GWS star Cameron McCarthy on road to recovery", "Accidently ordered 2 of an item, how do I only return 1? For free?", "Need help ASAP, someone ordering in my account", "So who's everyone tipping for Round 1?"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/retrieval_individual-3d6410bd-df84-4241-a31a-72548a10ef27.jsonl

@@ -28,3 +28,5 @@
 {"tstamp": 1734938509.499, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1734938509.1596, "finish": 1734938509.499, "ip": "", "conv_id": "de776b2c3c3a4252aea2054137a11053", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "מה המודל העסקי של npr", "output": [["מה המודל העסקי של npr", "Net promoter score\n\nNet promoter score (NPS) is a market research metric that is based on a single survey question asking respondents to rate the likelihood that they would recommend a company, product, or a service to a friend or colleague. The NPS is a proprietary instrument developed by Fred Reichheld, who owns the registered NPS trademark in conjunction with Bain & Company and Satmetrix. Its popularity and broad use have been attributed to its simplicity and transparent methodology.\nMethodology\nThe NPS assumes a subdivision of respondents into \"promoters\" who provide ratings of 9 or 10, \"passives\" who provide ratings of 7 or 8, and \"detractors\" who provide ratings of 6 or lower. Calculating the net promoter score involves subtracting the percentage of detractors from the percentage of promoters collected by the survey item.\nThe result of the calculation is typically expressed as an integer rather than a percentage. The core How likely would you be to recommend... question is almost always accompanied by an open-ended \"Why?\" and sometimes by so-called \"driver\" questions."]], "corpus": "wikipedia"}
 {"tstamp": 1734940672.7961, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1734940670.6377, "finish": 1734940672.7961, "ip": "", "conv_id": "cefad0d74ef94a1d9fbe034a31b71d86", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "kalman filter", "output": [["kalman filter", "Kalman filter\n\nTechnical description and context The Kalman filter is an efficient recursive filter estimating the internal state of a linear dynamic system from a series of noisy measurements.  It is used in a wide range of engineering and econometric applications from radar and computer vision to estimation of structural macroeconomic models, and is an important topic in control theory and control systems engineering.  Together with the linear-quadratic regulator (LQR), the Kalman filter solves the linear–quadratic–Gaussian control problem (LQG).  The Kalman filter, the linear-quadratic regulator, and the linear–quadratic–Gaussian controller are solutions to what arguably are the most fundamental problems of control theory.\nIn most applications, the internal state is much larger (has more degrees of freedom) than the few \"observable\" parameters which are measured.  However, by combining a series of measurements, the Kalman filter can estimate the entire internal state.\nFor the Dempster–Shafer theory, each state equation or observation is considered a special case of a linear belief function and the Kalman filtering is a special case of combining linear belief functions on a join-tree or Markov tree. Additional methods include belief filtering which use Bayes or evidential updates to the state equations.\nA wide variety of Kalman filters exists by now: Kalman's original formulation - now termed the \"simple\" Kalman filter, the Kalman–Bucy filter, Schmidt's \"extended\" filter, the information filter, and a variety of \"square-root\" filters that were developed by Bierman, Thornton, and many others. Perhaps the most commonly used type of very simple Kalman filter is the phase-locked loop, which is now ubiquitous in radios, especially frequency modulation (FM) radios, television sets, satellite communications receivers, outer space communications systems, and nearly any other electronic communications equipment."]], "corpus": "wikipedia"}
 {"tstamp": 1734940672.7961, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1734940670.6377, "finish": 1734940672.7961, "ip": "", "conv_id": "b0fe7fee036c4a529a178ba30594cb0f", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "kalman filter", "output": [["kalman filter", "Kalman filter\n\nTechnical description and context The Kalman filter is an efficient recursive filter estimating the internal state of a linear dynamic system from a series of noisy measurements.  It is used in a wide range of engineering and econometric applications from radar and computer vision to estimation of structural macroeconomic models, and is an important topic in control theory and control systems engineering.  Together with the linear-quadratic regulator (LQR), the Kalman filter solves the linear–quadratic–Gaussian control problem (LQG).  The Kalman filter, the linear-quadratic regulator, and the linear–quadratic–Gaussian controller are solutions to what arguably are the most fundamental problems of control theory.\nIn most applications, the internal state is much larger (has more degrees of freedom) than the few \"observable\" parameters which are measured.  However, by combining a series of measurements, the Kalman filter can estimate the entire internal state.\nFor the Dempster–Shafer theory, each state equation or observation is considered a special case of a linear belief function and the Kalman filtering is a special case of combining linear belief functions on a join-tree or Markov tree. Additional methods include belief filtering which use Bayes or evidential updates to the state equations.\nA wide variety of Kalman filters exists by now: Kalman's original formulation - now termed the \"simple\" Kalman filter, the Kalman–Bucy filter, Schmidt's \"extended\" filter, the information filter, and a variety of \"square-root\" filters that were developed by Bierman, Thornton, and many others. Perhaps the most commonly used type of very simple Kalman filter is the phase-locked loop, which is now ubiquitous in radios, especially frequency modulation (FM) radios, television sets, satellite communications receivers, outer space communications systems, and nearly any other electronic communications equipment."]], "corpus": "wikipedia"}
+{"tstamp": 1734940766.6808, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1734940763.8652, "finish": 1734940766.6808, "ip": "", "conv_id": "fb78aee8ab6c4b278c30e853b6d8a150", "model_name": "BM25", "prompt": "kalman filter limitations", "output": [["kalman filter limitations", "Kalman filter\n\nNonlinear filters The basic Kalman filter is limited to a linear assumption. More complex systems, however, can be nonlinear. The nonlinearity can be associated either with the process model or with the observation model or with both.\nThe most common variants of Kalman filters for non-linear systems are the Extended Kalman Filter and Unscented Kalman filter. The suitability of which filter to use depends on the non-linearity indices of the process and observation model.\nExtended Kalman filter\nIn the extended Kalman filter (EKF), the state transition and observation models need not be linear functions of the state but may instead be nonlinear functions. These functions are of differentiable type.\nThe function f can be used to compute the predicted state from the previous estimate and similarly the function h can be used to compute the predicted measurement from the predicted state. However, f and h cannot be applied to the covariance directly. Instead a matrix of partial derivatives (the Jacobian) is computed.\nAt each timestep the Jacobian is evaluated with current predicted states. These matrices can be used in the Kalman filter equations. This process essentially linearizes the nonlinear function around the current estimate.\nUnscented Kalman filter\nWhen the state transition and observation models—that is, the predict and update functions  and —are highly nonlinear, the extended Kalman filter can give particularly poor performance."]], "corpus": "wikipedia"}
+{"tstamp": 1734940766.6808, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1734940763.8652, "finish": 1734940766.6808, "ip": "", "conv_id": "f19c89ebb396405ebb5d416ca0bf5ce5", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "kalman filter limitations", "output": [["kalman filter limitations", "Kalman filter\n\nFor statistics and control theory, Kalman filtering, also known as linear quadratic estimation, is an algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe. The filter is constructed as a mean squared error minimiser, but an alternative derivation of the filter is also provided showing how the filter relates to maximum likelihood statistics. The filter is named after Rudolf E. Kálmán, who was one of the primary developers of its theory.\nThis digital filter is sometimes termed the Stratonovich–Kalman–Bucy filter because it is a special case of a more general, nonlinear filter developed somewhat earlier by the Soviet mathematician Ruslan Stratonovich. In fact, some of the special case linear filter's equations appeared in  papers by Stratonovich that were published before the summer of 1961, when Kalman met with Stratonovich during a conference in Moscow."]], "corpus": "wikipedia"}

data/retrieval_side_by_side-3d6410bd-df84-4241-a31a-72548a10ef27.jsonl

@@ -0,0 +1 @@
+{"tstamp": 1734940807.9125, "task_type": "retrieval", "type": "leftvote", "models": ["BM25", "sentence-transformers/all-MiniLM-L6-v2"], "ip": "", "0_conv_id": "fb78aee8ab6c4b278c30e853b6d8a150", "0_model_name": "BM25", "0_prompt": "kalman filter limitations", "0_output": [["kalman filter limitations", "Kalman filter\n\nNonlinear filters The basic Kalman filter is limited to a linear assumption. More complex systems, however, can be nonlinear. The nonlinearity can be associated either with the process model or with the observation model or with both.\nThe most common variants of Kalman filters for non-linear systems are the Extended Kalman Filter and Unscented Kalman filter. The suitability of which filter to use depends on the non-linearity indices of the process and observation model.\nExtended Kalman filter\nIn the extended Kalman filter (EKF), the state transition and observation models need not be linear functions of the state but may instead be nonlinear functions. These functions are of differentiable type.\nThe function f can be used to compute the predicted state from the previous estimate and similarly the function h can be used to compute the predicted measurement from the predicted state. However, f and h cannot be applied to the covariance directly. Instead a matrix of partial derivatives (the Jacobian) is computed.\nAt each timestep the Jacobian is evaluated with current predicted states. These matrices can be used in the Kalman filter equations. This process essentially linearizes the nonlinear function around the current estimate.\nUnscented Kalman filter\nWhen the state transition and observation models—that is, the predict and update functions  and —are highly nonlinear, the extended Kalman filter can give particularly poor performance."]], "0_corpus": "wikipedia", "1_conv_id": "f19c89ebb396405ebb5d416ca0bf5ce5", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "kalman filter limitations", "1_output": [["kalman filter limitations", "Kalman filter\n\nFor statistics and control theory, Kalman filtering, also known as linear quadratic estimation, is an algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe. The filter is constructed as a mean squared error minimiser, but an alternative derivation of the filter is also provided showing how the filter relates to maximum likelihood statistics. The filter is named after Rudolf E. Kálmán, who was one of the primary developers of its theory.\nThis digital filter is sometimes termed the Stratonovich–Kalman–Bucy filter because it is a special case of a more general, nonlinear filter developed somewhat earlier by the Soviet mathematician Ruslan Stratonovich. In fact, some of the special case linear filter's equations appeared in  papers by Stratonovich that were published before the summer of 1961, when Kalman met with Stratonovich during a conference in Moscow."]], "1_corpus": "wikipedia"}