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data/clustering_individual-1b2a6102-80bb-417d-a081-cf780f49c66c.jsonl

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mat bundestagswahl", "wochenhoroskop", "vfc plauen fsv zwickau", "xabi alonso", "erdbeben santorini vulkanausbruch", "donald trump", "super bowl donald trump", "verdi warnstreiks", "rentner einmalzahlung", "olaf scholz friedrich merz", "bauarbeiten berlin hauptbahnhof", "aldi 12 euro eintrittsgeld", "finn zecher handball", "eu mehlwürmer lebensmittel", "byd aktie gottes auge", "fc hansa rostock viktoria köln", "biathlon wm lenzerheide zeitplan", "michy batshuayi", "konrad adenauer bus", "zdf doku nato", "biggest loser kandidaten", "valneva aktie", "narrenzunft ulm umzug", "hallescher fc", "karim aga khan iv", "oneui 7", "plymouth argyle fc liverpool", "xiaomi aktie", "besteuerung einkommen spitzensteuersatz", "ukraine", "dominik fischnaller", "heinrich august winkler spd", "fck ultras berlin", "msv duisburg", "schnee", "vfl bochum holstein kiel", "trump zölle", "fortuna düsseldorf hannover 96", "omas gegen rechts braunschweig", "rheinmetall aktien", "brauerei", "erdbeben santorini", "depot erfurt schließt", "wintereinbruch in niedersachsen", "iphone se4", "xiaomi aktie prognose", "streik reutlingen bus", "nintendo switch 2 preis", "rodelbahn bramberg unfall", "demonstration", "isolde barth", "kosovo wahlen 2025", "bayreuth tigers", "teilnehmer fc sevilla gegen fc barcelona"], "ncluster": 15, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1739287820.9782, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1739287820.8696, "finish": 1739287820.9782, "ip": "", "conv_id": "f855bb331c7f459b8af69a682c868a95", "model_name": "GritLM/GritLM-7B", "prompt": ["sneakers", "loafers", "flats", "high heels", "drought", "fog", "hailstorm"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1739287820.9782, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1739287820.8696, "finish": 1739287820.9782, "ip": "", "conv_id": "98db23819cb74ab199e07b1174c6999e", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["sneakers", "loafers", "flats", "high heels", "drought", "fog", "hailstorm"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/clustering_side_by_side-1b2a6102-80bb-417d-a081-cf780f49c66c.jsonl

@@ -1,2 +1,3 @@
 {"tstamp": 1738763425.0328, "task_type": "clustering", "type": "rightvote", "models": ["embed-english-v3.0", "nomic-ai/nomic-embed-text-v1.5"], "ip": "", "0_conv_id": "ef445197357842379bff55e4a6dfea7e", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": ["I normalise the spectrum to units of $\\sigma_\\mathrm{thermal}$, taking $p_\\mathrm{\\alpha}=1.7 \\sigma_\\mathrm{thermal}$, roughly what we computed in earlier in the [thermal noise](#thermal-noise) and momentum sections.", "We can estimate the thermal noise of momentum measurements as gaussian noise with variance $\\sigma_\\mathrm{thermal}^2=4 m k_B T \\frac{\\omega}{Q}t$. If we take $m \\approx 10 \\,\\mathrm{\\mu g/cm}^3 \\times \\frac{4}{3} \\pi (3 \\,\\mathrm{mm})^3 \\approx 1 \\, \\mathrm{ng}$, $Q\\approx10^4$, $T=1\\, \\mathrm{mK}$, $\\omega = 650 \\, \\mathrm{Hz}$ and $t\\approx1\\,\\mathrm{s}$, we get $\\sigma_\\mathrm{thermal} \\approx 6\\times10^{-20} \\,\\mathrm{kg\\, m/s}$. ", "Calibration is key with all measurement devices. In particle physics, we often calibrate our experiments using calibration sources that can mimic the signal we are measuring. Such alibration sources can be used to establish absolute calibrations of particle detectors, instead of having to model the entire chain of detector physics; in practice, often both approaches are used to establish confidence in our understanding of the detector.", "Consider a function $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, which represents an invertible mapping between spaces. This mapping can be utilized to transform a random variable $X$ according to: $$Y = f(X)$$", "Given samples from $Y$ obtained through simulation-based inference, we aim to optimize the log-posterior $P_Y(y)$ through the following procedure: For each sample $y_i$, we compute: $$P_X(f_{\\theta}^{-1}(x)) \\left|\\frac{\\partial f^{-1}}{\\partial x}\\right|$$ and minimize this expression with respect to parameters $\\theta$.", "When there is a transformation between distributions, $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, distributions represented by the initial and the transformed spaces are related as such: ", "Find out which users' home directories are biggest: `sudo du -sh /home/* | sort -rh`", "Force unmount broken sshfs directory (after killing ssh and sshfs): `sudo umount -l [MOUNT_DIRECTORY]`", "I normalise the spectrum to units of $\\sigma_\\mathrm{thermal}$, taking $p_\\mathrm{\\alpha}=1.7 \\sigma_\\mathrm{thermal}$, roughly what we computed in earlier in the [thermal noise](#thermal-noise) and momentum sections.", "We can estimate the thermal noise of momentum measurements as gaussian noise with variance $\\sigma_\\mathrm{thermal}^2=4 m k_B T \\frac{\\omega}{Q}t$. If we take $m \\approx 10 \\,\\mathrm{\\mu g/cm}^3 \\times \\frac{4}{3} \\pi (3 \\,\\mathrm{mm})^3 \\approx 1 \\, \\mathrm{ng}$, $Q\\approx10^4$, $T=1\\, \\mathrm{mK}$, $\\omega = 650 \\, \\mathrm{Hz}$ and $t\\approx1\\,\\mathrm{s}$, we get $\\sigma_\\mathrm{thermal} \\approx 6\\times10^{-20} \\,\\mathrm{kg\\, m/s}$. ", "Calibration is key with all measurement devices. In particle physics, we often calibrate our experiments using calibration sources that can mimic the signal we are measuring. Such alibration sources can be used to establish absolute calibrations of particle detectors, instead of having to model the entire chain of detector physics; in practice, often both approaches are used to establish confidence in our understanding of the detector.", " This would provide spectrum that can be determined theoretically; a spectral fit can then be performed to conduct an absolute calibration of the force sensor. We choose to use $\\alpha$ sources, as they are mono-energetic and have high momentum due to their mass.", " Consider a function $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, which represents an invertible mapping between spaces. This mapping can be utilized to transform a random variable $X$ according to: $$Y = f(X)$$", "Given samples from $Y$ obtained through simulation-based inference, we aim to optimize the log-posterior $P_Y(y)$ through the following procedure: For each sample $y_i$, we compute: $$P_X(f_{\\theta}^{-1}(x)) \\left|\\frac{\\partial f^{-1}}{\\partial x}\\right|$$ and minimize this expression with respect to parameters $\\theta$.", "When there is a transformation between distributions, $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, distributions represented by the initial and the transformed spaces are related as such: ", "Find out which users' home directories are biggest: `sudo du -sh /home/* | sort -rh`", "Force unmount broken sshfs directory (after killing ssh and sshfs): `sudo umount -l [MOUNT_DIRECTORY]`"], "0_ncluster": 3, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "cdad0c086aef460c9e999a848b7f30ea", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": ["I normalise the spectrum to units of $\\sigma_\\mathrm{thermal}$, taking $p_\\mathrm{\\alpha}=1.7 \\sigma_\\mathrm{thermal}$, roughly what we computed in earlier in the [thermal noise](#thermal-noise) and momentum sections.", "We can estimate the thermal noise of momentum measurements as gaussian noise with variance $\\sigma_\\mathrm{thermal}^2=4 m k_B T \\frac{\\omega}{Q}t$. If we take $m \\approx 10 \\,\\mathrm{\\mu g/cm}^3 \\times \\frac{4}{3} \\pi (3 \\,\\mathrm{mm})^3 \\approx 1 \\, \\mathrm{ng}$, $Q\\approx10^4$, $T=1\\, \\mathrm{mK}$, $\\omega = 650 \\, \\mathrm{Hz}$ and $t\\approx1\\,\\mathrm{s}$, we get $\\sigma_\\mathrm{thermal} \\approx 6\\times10^{-20} \\,\\mathrm{kg\\, m/s}$. ", "Calibration is key with all measurement devices. In particle physics, we often calibrate our experiments using calibration sources that can mimic the signal we are measuring. Such alibration sources can be used to establish absolute calibrations of particle detectors, instead of having to model the entire chain of detector physics; in practice, often both approaches are used to establish confidence in our understanding of the detector.", "Consider a function $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, which represents an invertible mapping between spaces. This mapping can be utilized to transform a random variable $X$ according to: $$Y = f(X)$$", "Given samples from $Y$ obtained through simulation-based inference, we aim to optimize the log-posterior $P_Y(y)$ through the following procedure: For each sample $y_i$, we compute: $$P_X(f_{\\theta}^{-1}(x)) \\left|\\frac{\\partial f^{-1}}{\\partial x}\\right|$$ and minimize this expression with respect to parameters $\\theta$.", "When there is a transformation between distributions, $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, distributions represented by the initial and the transformed spaces are related as such: ", "Find out which users' home directories are biggest: `sudo du -sh /home/* | sort -rh`", "Force unmount broken sshfs directory (after killing ssh and sshfs): `sudo umount -l [MOUNT_DIRECTORY]`", "I normalise the spectrum to units of $\\sigma_\\mathrm{thermal}$, taking $p_\\mathrm{\\alpha}=1.7 \\sigma_\\mathrm{thermal}$, roughly what we computed in earlier in the [thermal noise](#thermal-noise) and momentum sections.", "We can estimate the thermal noise of momentum measurements as gaussian noise with variance $\\sigma_\\mathrm{thermal}^2=4 m k_B T \\frac{\\omega}{Q}t$. If we take $m \\approx 10 \\,\\mathrm{\\mu g/cm}^3 \\times \\frac{4}{3} \\pi (3 \\,\\mathrm{mm})^3 \\approx 1 \\, \\mathrm{ng}$, $Q\\approx10^4$, $T=1\\, \\mathrm{mK}$, $\\omega = 650 \\, \\mathrm{Hz}$ and $t\\approx1\\,\\mathrm{s}$, we get $\\sigma_\\mathrm{thermal} \\approx 6\\times10^{-20} \\,\\mathrm{kg\\, m/s}$. ", "Calibration is key with all measurement devices. In particle physics, we often calibrate our experiments using calibration sources that can mimic the signal we are measuring. Such alibration sources can be used to establish absolute calibrations of particle detectors, instead of having to model the entire chain of detector physics; in practice, often both approaches are used to establish confidence in our understanding of the detector.", " This would provide spectrum that can be determined theoretically; a spectral fit can then be performed to conduct an absolute calibration of the force sensor. We choose to use $\\alpha$ sources, as they are mono-energetic and have high momentum due to their mass.", " Consider a function $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, which represents an invertible mapping between spaces. This mapping can be utilized to transform a random variable $X$ according to: $$Y = f(X)$$", "Given samples from $Y$ obtained through simulation-based inference, we aim to optimize the log-posterior $P_Y(y)$ through the following procedure: For each sample $y_i$, we compute: $$P_X(f_{\\theta}^{-1}(x)) \\left|\\frac{\\partial f^{-1}}{\\partial x}\\right|$$ and minimize this expression with respect to parameters $\\theta$.", "When there is a transformation between distributions, $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, distributions represented by the initial and the transformed spaces are related as such: ", "Find out which users' home directories are biggest: `sudo du -sh /home/* | sort -rh`", "Force unmount broken sshfs directory (after killing ssh and sshfs): `sudo umount -l [MOUNT_DIRECTORY]`"], "1_ncluster": 3, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1738763606.6934, "task_type": "clustering", "type": "rightvote", "models": ["nomic-ai/nomic-embed-text-v1.5", "intfloat/multilingual-e5-large-instruct"], "ip": "", "0_conv_id": "d6af6c8926874d05beddd73160a34b63", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_prompt": ["I normalise the spectrum to units of $\\sigma_\\mathrm{thermal}$, taking $p_\\mathrm{\\alpha}=1.7 \\sigma_\\mathrm{thermal}$, roughly what we computed in earlier in the [thermal noise](#thermal-noise) and momentum sections.", "We can estimate the thermal noise of momentum measurements as gaussian noise with variance $\\sigma_\\mathrm{thermal}^2=4 m k_B T \\frac{\\omega}{Q}t$. If we take $m \\approx 10 \\,\\mathrm{\\mu g/cm}^3 \\times \\frac{4}{3} \\pi (3 \\,\\mathrm{mm})^3 \\approx 1 \\, \\mathrm{ng}$, $Q\\approx10^4$, $T=1\\, \\mathrm{mK}$, $\\omega = 650 \\, \\mathrm{Hz}$ and $t\\approx1\\,\\mathrm{s}$, we get $\\sigma_\\mathrm{thermal} \\approx 6\\times10^{-20} \\,\\mathrm{kg\\, m/s}$. ", "Calibration is key with all measurement devices. In particle physics, we often calibrate our experiments using calibration sources that can mimic the signal we are measuring. Such alibration sources can be used to establish absolute calibrations of particle detectors, instead of having to model the entire chain of detector physics; in practice, often both approaches are used to establish confidence in our understanding of the detector.", " This would provide spectrum that can be determined theoretically; a spectral fit can then be performed to conduct an absolute calibration of the force sensor. We choose to use $\\alpha$ sources, as they are mono-energetic and have high momentum due to their mass.", " Consider a function $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, which represents an invertible mapping between spaces. This mapping can be utilized to transform a random variable $X$ according to: $$Y = f(X)$$", "Given samples from $Y$ obtained through simulation-based inference, we aim to optimize the log-posterior $P_Y(y)$ through the following procedure: For each sample $y_i$, we compute: $$P_X(f_{\\theta}^{-1}(x)) \\left|\\frac{\\partial f^{-1}}{\\partial x}\\right|$$ and minimize this expression with respect to parameters $\\theta$.", "When there is a transformation between distributions, $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, distributions represented by the initial and the transformed spaces are related as such: ", "Find out which users' home directories are biggest: `sudo du -sh /home/* | sort -rh`", "Force unmount broken sshfs directory (after killing ssh and sshfs): `sudo umount -l [MOUNT_DIRECTORY]`"], "0_ncluster": 3, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "83b92bd4d208454296242ddd24156ac5", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": ["I normalise the spectrum to units of $\\sigma_\\mathrm{thermal}$, taking $p_\\mathrm{\\alpha}=1.7 \\sigma_\\mathrm{thermal}$, roughly what we computed in earlier in the [thermal noise](#thermal-noise) and momentum sections.", "We can estimate the thermal noise of momentum measurements as gaussian noise with variance $\\sigma_\\mathrm{thermal}^2=4 m k_B T \\frac{\\omega}{Q}t$. If we take $m \\approx 10 \\,\\mathrm{\\mu g/cm}^3 \\times \\frac{4}{3} \\pi (3 \\,\\mathrm{mm})^3 \\approx 1 \\, \\mathrm{ng}$, $Q\\approx10^4$, $T=1\\, \\mathrm{mK}$, $\\omega = 650 \\, \\mathrm{Hz}$ and $t\\approx1\\,\\mathrm{s}$, we get $\\sigma_\\mathrm{thermal} \\approx 6\\times10^{-20} \\,\\mathrm{kg\\, m/s}$. ", "Calibration is key with all measurement devices. In particle physics, we often calibrate our experiments using calibration sources that can mimic the signal we are measuring. Such alibration sources can be used to establish absolute calibrations of particle detectors, instead of having to model the entire chain of detector physics; in practice, often both approaches are used to establish confidence in our understanding of the detector.", " This would provide spectrum that can be determined theoretically; a spectral fit can then be performed to conduct an absolute calibration of the force sensor. We choose to use $\\alpha$ sources, as they are mono-energetic and have high momentum due to their mass.", " Consider a function $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, which represents an invertible mapping between spaces. This mapping can be utilized to transform a random variable $X$ according to: $$Y = f(X)$$", "Given samples from $Y$ obtained through simulation-based inference, we aim to optimize the log-posterior $P_Y(y)$ through the following procedure: For each sample $y_i$, we compute: $$P_X(f_{\\theta}^{-1}(x)) \\left|\\frac{\\partial f^{-1}}{\\partial x}\\right|$$ and minimize this expression with respect to parameters $\\theta$.", "When there is a transformation between distributions, $f: \\mathbb{R}^n \\rightarrow \\mathbb{R}^n$, distributions represented by the initial and the transformed spaces are related as such: ", "Find out which users' home directories are biggest: `sudo du -sh /home/* | sort -rh`", "Force unmount broken sshfs directory (after killing ssh and sshfs): `sudo umount -l [MOUNT_DIRECTORY]`"], "1_ncluster": 3, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
+{"tstamp": 1739287841.2614, "task_type": "clustering", "type": "tievote", "models": ["GritLM/GritLM-7B", "BAAI/bge-large-en-v1.5"], "ip": "", "0_conv_id": "f855bb331c7f459b8af69a682c868a95", "0_model_name": "GritLM/GritLM-7B", "0_prompt": ["sneakers", "loafers", "flats", "high heels", "drought", "fog", "hailstorm"], "0_ncluster": 2, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "98db23819cb74ab199e07b1174c6999e", "1_model_name": "BAAI/bge-large-en-v1.5", "1_prompt": ["sneakers", "loafers", "flats", "high heels", "drought", "fog", "hailstorm"], "1_ncluster": 2, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}

data/sts_battle-1b2a6102-80bb-417d-a081-cf780f49c66c.jsonl

@@ -12,3 +12,4 @@
 {"tstamp": 1738963393.4359, "task_type": "sts", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "505f78ca501548389760ce041d6fc3a4", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_txt0": "A dog ran to the store", "0_txt1": "A cat pranced to the grocer.", "0_txt2": "A man ran a marathon.", "0_output": "", "1_conv_id": "f29484e3538a424d94440a77382b81df", "1_model_name": "voyage-multilingual-2", "1_txt0": "A dog ran to the store", "1_txt1": "A cat pranced to the grocer.", "1_txt2": "A man ran a marathon.", "1_output": ""}
 {"tstamp": 1739151627.0647, "task_type": "sts", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "9d1c4a9393ae44edb7c55581d65a6a9e", "0_model_name": "text-embedding-3-large", "0_txt0": "A baseball player in the act of pick up a baseball with his hand.", "0_txt1": "A baseball player playing in a game.", "0_txt2": "A basketball player dunking the ball.", "0_output": "", "1_conv_id": "66c30231034949e5a435e4d750ca1fb7", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_txt0": "A baseball player in the act of pick up a baseball with his hand.", "1_txt1": "A baseball player playing in a game.", "1_txt2": "A basketball player dunking the ball.", "1_output": ""}
 {"tstamp": 1739167777.8825, "task_type": "sts", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "663bac618ff74f3288577221b9ec501c", "0_model_name": "nomic-ai/nomic-embed-text-v1.5", "0_txt0": "crypto", "0_txt1": "fintech", "0_txt2": "logistics", "0_output": "", "1_conv_id": "d5c14d522e924323a5284a8812cbb4c9", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_txt0": "crypto", "1_txt1": "fintech", "1_txt2": "logistics", "1_output": ""}
+{"tstamp": 1739287882.5357, "task_type": "sts", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "9c9fee6a10d044c9a15240eef78a568f", "0_model_name": "embed-english-v3.0", "0_txt0": "While traveling 35,000 feet above eastern Ohio, United 93 suddenly dropped 700 feet.", "0_txt1": "There was a decrease of 700 feet in the altitude of the plane.", "0_txt2": "The plane began to increase its altitude rapidly.", "0_output": "", "1_conv_id": "b716775aa9e04953aa9ab25ef642c4e3", "1_model_name": "GritLM/GritLM-7B", "1_txt0": "While traveling 35,000 feet above eastern Ohio, United 93 suddenly dropped 700 feet.", "1_txt1": "There was a decrease of 700 feet in the altitude of the plane.", "1_txt2": "The plane began to increase its altitude rapidly.", "1_output": ""}

data/sts_individual-1b2a6102-80bb-417d-a081-cf780f49c66c.jsonl

@@ -66,3 +66,5 @@
 {"tstamp": 1739151531.4618, "task_type": "sts", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1739151530.7264, "finish": 1739151531.4618, "ip": "", "conv_id": "66c30231034949e5a435e4d750ca1fb7", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "txt0": "A baseball player in the act of pick up a baseball with his hand.", "txt1": "A baseball player playing in a game.", "txt2": "A basketball player dunking the ball.", "output": ""}
 {"tstamp": 1739167725.8723, "task_type": "sts", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1739167725.8205, "finish": 1739167725.8723, "ip": "", "conv_id": "663bac618ff74f3288577221b9ec501c", "model_name": "nomic-ai/nomic-embed-text-v1.5", "txt0": "crypto", "txt1": "fintech", "txt2": "logistics", "output": ""}
 {"tstamp": 1739167725.8723, "task_type": "sts", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1739167725.8205, "finish": 1739167725.8723, "ip": "", "conv_id": "d5c14d522e924323a5284a8812cbb4c9", "model_name": "Salesforce/SFR-Embedding-2_R", "txt0": "crypto", "txt1": "fintech", "txt2": "logistics", "output": ""}
+{"tstamp": 1739287851.9673, "task_type": "sts", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1739287851.7926, "finish": 1739287851.9673, "ip": "", "conv_id": "9c9fee6a10d044c9a15240eef78a568f", "model_name": "embed-english-v3.0", "txt0": "While traveling 35,000 feet above eastern Ohio, United 93 suddenly dropped 700 feet.", "txt1": "There was a decrease of 700 feet in the altitude of the plane.", "txt2": "The plane began to increase its altitude rapidly.", "output": ""}
+{"tstamp": 1739287851.9673, "task_type": "sts", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1739287851.7926, "finish": 1739287851.9673, "ip": "", "conv_id": "b716775aa9e04953aa9ab25ef642c4e3", "model_name": "GritLM/GritLM-7B", "txt0": "While traveling 35,000 feet above eastern Ohio, United 93 suddenly dropped 700 feet.", "txt1": "There was a decrease of 700 feet in the altitude of the plane.", "txt2": "The plane began to increase its altitude rapidly.", "output": ""}