Scheduled Commit

data/clustering_individual-77467e4a-d00c-437a-8c8c-e7129d54ef5b.jsonl

@@ -30,3 +30,11 @@
 {"tstamp": 1722291344.4123, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722291344.3162, "finish": 1722291344.4123, "ip": "", "conv_id": "6cfac98b7d934939aedeb2f393dc773a", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["It's official! 1 Bitcoin = $10,000 USD", "Everyone who's trading BTC right now", "Age reversal not only achievable but also possibly imminent: Retro Biosciences", "MicroRNA regrows 90% of lost hair, study finds", "Researchers have found that people who live beyond 105 years tend to have a unique genetic background that makes their bodies more efficient at repairing DNA, according to a new study.", "[D] A Demo from 1993 of 32-year-old Yann LeCun showing off the World's first Convolutional Network for Text Recognition", "Speech-to-speech translation for a real-world unwritten language", "Seeking the Best Embedding Model: Experiences with bge & GritLM?"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722291354.8285, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722291354.7336, "finish": 1722291354.8285, "ip": "", "conv_id": "8f44e42d378b4e2dbff1a3184b6103d1", "model_name": "GritLM/GritLM-7B", "prompt": ["It's official! 1 Bitcoin = $10,000 USD", "Everyone who's trading BTC right now", "Age reversal not only achievable but also possibly imminent: Retro Biosciences", "MicroRNA regrows 90% of lost hair, study finds", "Researchers have found that people who live beyond 105 years tend to have a unique genetic background that makes their bodies more efficient at repairing DNA, according to a new study.", "[D] A Demo from 1993 of 32-year-old Yann LeCun showing off the World's first Convolutional Network for Text Recognition", "Speech-to-speech translation for a real-world unwritten language", "Seeking the Best Embedding Model: Experiences with bge & GritLM?"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722291354.8285, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722291354.7336, "finish": 1722291354.8285, "ip": "", "conv_id": "18739d5906744efd8cf27fc396a00566", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["It's official! 1 Bitcoin = $10,000 USD", "Everyone who's trading BTC right now", "Age reversal not only achievable but also possibly imminent: Retro Biosciences", "MicroRNA regrows 90% of lost hair, study finds", "Researchers have found that people who live beyond 105 years tend to have a unique genetic background that makes their bodies more efficient at repairing DNA, according to a new study.", "[D] A Demo from 1993 of 32-year-old Yann LeCun showing off the World's first Convolutional Network for Text Recognition", "Speech-to-speech translation for a real-world unwritten language", "Seeking the Best Embedding Model: Experiences with bge & GritLM?"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722291545.3209, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722291545.225, "finish": 1722291545.3209, "ip": "", "conv_id": "39bf130bdd334613a3059d84ea53394b", "model_name": "GritLM/GritLM-7B", "prompt": ["SGPT: GPT sentence embeddings for semantic search", "MTEB: Massive text embedding benchmark", "C-pack: Packaged resources to advance general chinese embedding", "BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval", "Bloom: A 176b-parameter open-access multilingual language model", "Olmo: Accelerating the science of language models", "FinGPT: Large Generative Models for a Small Language"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722291545.3209, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722291545.225, "finish": 1722291545.3209, "ip": "", "conv_id": "7c3ea3f94dfc4c30bc67900a79547c63", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["SGPT: GPT sentence embeddings for semantic search", "MTEB: Massive text embedding benchmark", "C-pack: Packaged resources to advance general chinese embedding", "BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval", "Bloom: A 176b-parameter open-access multilingual language model", "Olmo: Accelerating the science of language models", "FinGPT: Large Generative Models for a Small Language"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722291645.1745, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722291645.0777, "finish": 1722291645.1745, "ip": "", "conv_id": "796815cfe8cf47b192b617b6c5207483", "model_name": "GritLM/GritLM-7B", "prompt": ["SGPT: GPT sentence embeddings for semantic search", "MTEB: Massive text embedding benchmark", "C-pack: Packaged resources to advance general chinese embedding", "BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval", "Deep Residual Learning for Image Recognition", "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722291645.1745, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722291645.0777, "finish": 1722291645.1745, "ip": "", "conv_id": "55e24e0730a54d35af08d2b171164a62", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["SGPT: GPT sentence embeddings for semantic search", "MTEB: Massive text embedding benchmark", "C-pack: Packaged resources to advance general chinese embedding", "BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval", "Deep Residual Learning for Image Recognition", "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722291677.7153, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722291677.6204, "finish": 1722291677.7153, "ip": "", "conv_id": "8fecaacd043445bb8c502c21fbc2904e", "model_name": "GritLM/GritLM-7B", "prompt": ["SGPT: GPT sentence embeddings for semantic search", "MTEB: Massive text embedding benchmark", "C-Pack: Packaged Resources To Advance General Chinese Embedding", "BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval", "Deep Residual Learning for Image Recognition", "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722291677.7153, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722291677.6204, "finish": 1722291677.7153, "ip": "", "conv_id": "feaf1621fd304f57b5b3d50f5e85eb75", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["SGPT: GPT sentence embeddings for semantic search", "MTEB: Massive text embedding benchmark", "C-Pack: Packaged Resources To Advance General Chinese Embedding", "BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval", "Deep Residual Learning for Image Recognition", "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722291721.7903, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722291721.6928, "finish": 1722291721.7903, "ip": "", "conv_id": "bf31da2ca5b049bd9d082a21e26d8a26", "model_name": "GritLM/GritLM-7B", "prompt": ["SGPT: GPT sentence embeddings for semantic search", "MTEB: Massive text embedding benchmark", "Generative representational instruction tuning", "BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval", "Deep Residual Learning for Image Recognition", "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722291721.7903, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722291721.6928, "finish": 1722291721.7903, "ip": "", "conv_id": "11d038fb4ecf478fbd8f7d8562325713", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["SGPT: GPT sentence embeddings for semantic search", "MTEB: Massive text embedding benchmark", "Generative representational instruction tuning", "BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval", "Deep Residual Learning for Image Recognition", "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}