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arena-results / 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"}
{"tstamp": 1722406142.1568, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722406142.1303, "finish": 1722406142.1568, "ip": "", "conv_id": "65e94e06e60049a0b93f76e75f5f3795", "model_name": "GritLM/GritLM-7B", "prompt": ["T\u00fcrk\u00e7e biliyor musun "], "ncluster": 1, "output": "", "ndim": "3D", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722418842.482, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722418842.1963, "finish": 1722418842.482, "ip": "", "conv_id": "c1ac01c2f4384e999c782170c0e08531", "model_name": "embed-english-v3.0", "prompt": ["green", "red", "orange", "yellow", "blue", "purple", "pink", "banana", "mango", "pear", "apple", "kiwi", "hoe", "wheelbarrow", "shovel", "RAM", "power supply", "SSD", "motherboard", "CPU", "hard drive", "AWS", "Azure", "IBM Cloud", "Google Cloud"], "ncluster": 5, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722418849.5116, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722418849.2741, "finish": 1722418849.5116, "ip": "", "conv_id": "c1ac01c2f4384e999c782170c0e08531", "model_name": "embed-english-v3.0", "prompt": ["green", "red", "orange", "yellow", "blue", "purple", "pink", "banana", "mango", "pear", "apple", "kiwi", "hoe", "wheelbarrow", "shovel", "RAM", "power supply", "SSD", "motherboard", "CPU", "hard drive", "AWS", "Azure", "IBM Cloud", "Google Cloud"], "ncluster": 5, "output": "", "ndim": "2D (press for 3D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722419201.6559, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722419201.4263, "finish": 1722419201.6559, "ip": "", "conv_id": "1e7547b40a7247f798f92e91dba29f04", "model_name": "embed-english-v3.0", "prompt": ["LHR", "BER", "DUB", "SYD", "JFK", "LTN", "FRA", "SFO", "LTN", "SIN", "IST", "DBX"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722419250.4205, "task_type": "clustering", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722419250.3309, "finish": 1722419250.4205, "ip": "", "conv_id": "346e5cbb1f934cf6911d6ce7bfa3d236", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": ["LHR", "BER", "DUB", "SYD", "JFK", "LTN", "FRA", "SFO", "LTN", "SIN", "IST", "DBX"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722419250.4205, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722419250.3309, "finish": 1722419250.4205, "ip": "", "conv_id": "ba8218c6c5ce4b98ae2e8e4e97573696", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["LHR", "BER", "DUB", "SYD", "JFK", "LTN", "FRA", "SFO", "LTN", "SIN", "IST", "DBX"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722434299.6602, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722434299.6151, "finish": 1722434299.6602, "ip": "", "conv_id": "938bf910e95f4eb393b20ba88e335007", "model_name": "GritLM/GritLM-7B", "prompt": ["Mexico"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722434299.6602, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722434299.6151, "finish": 1722434299.6602, "ip": "", "conv_id": "ebdaf08c76f649468390d3d01dbedc98", "model_name": "text-embedding-004", "prompt": ["Mexico"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722434304.705, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722434304.4767, "finish": 1722434304.705, "ip": "", "conv_id": "938bf910e95f4eb393b20ba88e335007", "model_name": "GritLM/GritLM-7B", "prompt": ["Mexico", "Munich"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722434304.705, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722434304.4767, "finish": 1722434304.705, "ip": "", "conv_id": "ebdaf08c76f649468390d3d01dbedc98", "model_name": "text-embedding-004", "prompt": ["Mexico", "Munich"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722434315.3251, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722434315.0532, "finish": 1722434315.3251, "ip": "", "conv_id": "938bf910e95f4eb393b20ba88e335007", "model_name": "GritLM/GritLM-7B", "prompt": ["Mexico", "Munich", "green"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722434315.3251, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722434315.0532, "finish": 1722434315.3251, "ip": "", "conv_id": "ebdaf08c76f649468390d3d01dbedc98", "model_name": "text-embedding-004", "prompt": ["Mexico", "Munich", "green"], "ncluster": 1, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722434360.303, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722434360.0462, "finish": 1722434360.303, "ip": "", "conv_id": "938bf910e95f4eb393b20ba88e335007", "model_name": "GritLM/GritLM-7B", "prompt": ["Mexico", "Munich", "green", "pink"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722434360.303, "task_type": "clustering", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722434360.0462, "finish": 1722434360.303, "ip": "", "conv_id": "ebdaf08c76f649468390d3d01dbedc98", "model_name": "text-embedding-004", "prompt": ["Mexico", "Munich", "green", "pink"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722434443.3336, "task_type": "clustering", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722434443.2604, "finish": 1722434443.3336, "ip": "", "conv_id": "41a32e35270b49dfb313c0d81ca5f458", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": ["", "Mexico ", "Tokyo ", " red ", "Munich ", "blue ", " green ", "Amsterdam"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722434443.3336, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722434443.2604, "finish": 1722434443.3336, "ip": "", "conv_id": "9d368d073ffd4980a322406fbb248f34", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["", "Mexico ", "Tokyo ", " red ", "Munich ", "blue ", " green ", "Amsterdam"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722434480.4481, "task_type": "clustering", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722434480.3749, "finish": 1722434480.4481, "ip": "", "conv_id": "41a32e35270b49dfb313c0d81ca5f458", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": ["", "Mexico ", "Tokyo ", " red ", "Munich ", "blue ", " green ", "Amsterdam", "", "Mexico ", "Tokyo ", " red ", "Munich ", "blue ", " green ", "Amsterdam"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722434480.4481, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722434480.3749, "finish": 1722434480.4481, "ip": "", "conv_id": "9d368d073ffd4980a322406fbb248f34", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["", "Mexico ", "Tokyo ", " red ", "Munich ", "blue ", " green ", "Amsterdam", "", "Mexico ", "Tokyo ", " red ", "Munich ", "blue ", " green ", "Amsterdam"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722434626.2583, "task_type": "clustering", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722434626.173, "finish": 1722434626.2583, "ip": "", "conv_id": "ad61a18f39b34b538a916dd430904271", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": ["I'd be happy to help you create some queries for testing embedding models. Let's start with clustering and retrieval examples.\nFor clustering, here's a list of words that can be clustered together or separately:\n", "apple", "banana", "carrot", "dog", "cat", "hamster", "Paris", "London", "Berlin", "violin", "piano", "guitar", "red", "blue", "green", "lawyer", "doctor", "engineer"], "ncluster": 7, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722434626.2583, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722434626.173, "finish": 1722434626.2583, "ip": "", "conv_id": "244a8108008549ec96b42f6f351c56eb", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["I'd be happy to help you create some queries for testing embedding models. Let's start with clustering and retrieval examples.\nFor clustering, here's a list of words that can be clustered together or separately:\n", "apple", "banana", "carrot", "dog", "cat", "hamster", "Paris", "London", "Berlin", "violin", "piano", "guitar", "red", "blue", "green", "lawyer", "doctor", "engineer"], "ncluster": 7, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722434654.8367, "task_type": "clustering", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722434654.7296, "finish": 1722434654.8367, "ip": "", "conv_id": "ad61a18f39b34b538a916dd430904271", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": ["I'd be happy to help you create some queries for testing embedding models. Let's start with clustering and retrieval examples.\nFor clustering, here's a list of words that can be clustered together or separately:\n", "apple", "banana", "carrot", "dog", "cat", "hamster", "Paris", "London", "Berlin", "violin", "piano", "guitar", "red", "blue", "green", "lawyer", "doctor", "engineer", "", "apple", "banana", "carrot", "dog", "cat", "hamster", "Paris", "London", "Berlin", "violin", "piano", "guitar", "red", "blue", "green", "lawyer", "doctor", "engineer"], "ncluster": 7, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722434654.8367, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722434654.7296, "finish": 1722434654.8367, "ip": "", "conv_id": "244a8108008549ec96b42f6f351c56eb", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["I'd be happy to help you create some queries for testing embedding models. Let's start with clustering and retrieval examples.\nFor clustering, here's a list of words that can be clustered together or separately:\n", "apple", "banana", "carrot", "dog", "cat", "hamster", "Paris", "London", "Berlin", "violin", "piano", "guitar", "red", "blue", "green", "lawyer", "doctor", "engineer", "", "apple", "banana", "carrot", "dog", "cat", "hamster", "Paris", "London", "Berlin", "violin", "piano", "guitar", "red", "blue", "green", "lawyer", "doctor", "engineer"], "ncluster": 7, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722434692.6124, "task_type": "clustering", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722434692.5034, "finish": 1722434692.6124, "ip": "", "conv_id": "ad61a18f39b34b538a916dd430904271", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": ["I'd be happy to help you create some queries for testing embedding models. Let's start with clustering and retrieval examples.\nFor clustering, here's a list of words that can be clustered together or separately:\n", "apple", "banana", "carrot", "dog", "cat", "hamster", "Paris", "London", "Berlin", "violin", "piano", "guitar", "red", "blue", "green", "lawyer", "doctor", "engineer", "", "apple", "banana", "carrot", "dog", "cat", "hamster", "Paris", "London", "Berlin", "violin", "piano", "guitar", "red", "blue", "green", "lawyer", "doctor", "engineer", "", "apple", "banana", "carrot", "dog", "cat", "hamster", "Paris", "London", "Berlin", "violin", "piano", "guitar", "red", "blue", "green", "lawyer", "doctor", "engineer"], "ncluster": 7, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722434692.6124, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722434692.5034, "finish": 1722434692.6124, "ip": "", "conv_id": "244a8108008549ec96b42f6f351c56eb", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["I'd be happy to help you create some queries for testing embedding models. Let's start with clustering and retrieval examples.\nFor clustering, here's a list of words that can be clustered together or separately:\n", "apple", "banana", "carrot", "dog", "cat", "hamster", "Paris", "London", "Berlin", "violin", "piano", "guitar", "red", "blue", "green", "lawyer", "doctor", "engineer", "", "apple", "banana", "carrot", "dog", "cat", "hamster", "Paris", "London", "Berlin", "violin", "piano", "guitar", "red", "blue", "green", "lawyer", "doctor", "engineer", "", "apple", "banana", "carrot", "dog", "cat", "hamster", "Paris", "London", "Berlin", "violin", "piano", "guitar", "red", "blue", "green", "lawyer", "doctor", "engineer"], "ncluster": 7, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722434710.4181, "task_type": "clustering", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722434710.343, "finish": 1722434710.4181, "ip": "", "conv_id": "84e465ac0d8146469d04220067f5a378", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": ["", "apple", "banana", "carrot", "dog", "cat", "hamster", "Paris", "London", "Berlin", "violin", "piano", "guitar", "red", "blue", "green", "lawyer", "doctor", "engineer"], "ncluster": 7, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722434710.4181, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722434710.343, "finish": 1722434710.4181, "ip": "", "conv_id": "887dadbf87b145118d7e2e17bce65d98", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["", "apple", "banana", "carrot", "dog", "cat", "hamster", "Paris", "London", "Berlin", "violin", "piano", "guitar", "red", "blue", "green", "lawyer", "doctor", "engineer"], "ncluster": 7, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722434745.5092, "task_type": "clustering", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722434745.4071, "finish": 1722434745.5092, "ip": "", "conv_id": "84e465ac0d8146469d04220067f5a378", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": ["", "apple", "banana", "carrot", "dog", "cat", "hamster", "Paris", "London", "Berlin", "violin", "piano", "guitar", "red", "blue", "green", "lawyer", "doctor", "engineer", "", "apple", "banana", "carrot", "dog", "cat", "hamster", "Paris", "London", "Berlin", "violin", "piano", "guitar", "red", "blue", "green", "lawyer", "doctor", "engineer"], "ncluster": 7, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
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