Scheduled Commit

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

@@ -43,3 +43,5 @@
 {"tstamp": 1722291915.271, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722291915.1809, "finish": 1722291915.271, "ip": "", "conv_id": "4dd1657267504ebba4f3a215248029a0", "model_name": "GritLM/GritLM-7B", "prompt": ["MTEB: Massive text embedding benchmark", "BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval", "The Scandinavian Embedding Benchmarks: Comprehensive Assessment of Multilingual and Monolingual Text Embedding", "Beyond the imitation game: Quantifying and extrapolating the capabilities of language models", "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification", "Deep Residual Learning for Image Recognition"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722291915.271, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722291915.1809, "finish": 1722291915.271, "ip": "", "conv_id": "401f530ea2e64235a906e5e8f9c6b9c5", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["MTEB: Massive text embedding benchmark", "BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval", "The Scandinavian Embedding Benchmarks: Comprehensive Assessment of Multilingual and Monolingual Text Embedding", "Beyond the imitation game: Quantifying and extrapolating the capabilities of language models", "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification", "Deep Residual Learning for Image Recognition"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1722292121.8444, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722292121.7878, "finish": 1722292121.8444, "ip": "", "conv_id": "c1a4ede6b5384186851ac40d11025d9a", "model_name": "GritLM/GritLM-7B", "prompt": ["MTEB: Massive text embedding benchmark", "BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval", "The Scandinavian Embedding Benchmarks: Comprehensive Assessment of Multilingual and Monolingual Text Embedding", "Beyond the imitation game: Quantifying and extrapolating the capabilities of language models", "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification", "Deep Residual Learning for Image Recognition"], "ncluster": 3, "output": "", "ndim": "3D", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722292135.9438, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722292135.8534, "finish": 1722292135.9438, "ip": "", "conv_id": "6c70be256d2f432086502da2ff9043db", "model_name": "GritLM/GritLM-7B", "prompt": ["MTEB: Massive text embedding benchmark", "BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval", "The Scandinavian Embedding Benchmarks: Comprehensive Assessment of Multilingual and Monolingual Text Embedding", "Beyond the imitation game: Quantifying and extrapolating the capabilities of language models", "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification", "Deep Residual Learning for Image Recognition"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1722292135.9438, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722292135.8534, "finish": 1722292135.9438, "ip": "", "conv_id": "9408c6f89a3740df8d96a0f34f4a82a2", "model_name": "BAAI/bge-large-en-v1.5", "prompt": ["MTEB: Massive text embedding benchmark", "BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval", "The Scandinavian Embedding Benchmarks: Comprehensive Assessment of Multilingual and Monolingual Text Embedding", "Beyond the imitation game: Quantifying and extrapolating the capabilities of language models", "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification", "Deep Residual Learning for Image Recognition"], "ncluster": 3, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}