Scheduled Commit

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

@@ -27,3 +27,7 @@
 {"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"}
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+{"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"}
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+{"tstamp": 1722434745.5092, "task_type": "clustering", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722434745.4071, "finish": 1722434745.5092, "ip": "", "conv_id": "887dadbf87b145118d7e2e17bce65d98", "model_name": "Salesforce/SFR-Embedding-2_R", "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"}