data/clustering_individual-e24d4f9c-7b24-4491-968b-6ff7be4d9e89.jsonl

{"tstamp": 1722293131.1444, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722293091.7652, "finish": 1722293131.1444, "ip": "", "conv_id": "822595a1ec2246e4b8dd415b43cbb928", "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", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722293147.106, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722293147.0371, "finish": 1722293147.106, "ip": "", "conv_id": "822595a1ec2246e4b8dd415b43cbb928", "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", "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 EmbeddingThe 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", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722293305.7687, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722293305.703, "finish": 1722293305.7687, "ip": "", "conv_id": "1f073d817caf44eba202aa82947cf6dc", "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 RecognitionDeep Residual Learning for Image RecognitionDeep Residual Learning for Image RecognitionDeep Residual Learning for Image RecognitionDeep Residual Learning for Image RecognitionDeep Residual Learning for Image Recognition"], "ncluster": 2, "output": "", "ndim": "3D", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722293326.3415, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722293326.0434, "finish": 1722293326.3415, "ip": "", "conv_id": "914d9cb5ea0148d78c71ebcc5d866ac2", "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 RecognitionDeep Residual Learning for Image RecognitionDeep Residual Learning for Image RecognitionDeep Residual Learning for Image RecognitionDeep Residual Learning for Image RecognitionDeep Residual Learning for Image Recognition"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722293326.3415, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722293326.0434, "finish": 1722293326.3415, "ip": "", "conv_id": "9f51095d9ede4874a4feaffa9a90cf50", "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 RecognitionDeep Residual Learning for Image RecognitionDeep Residual Learning for Image RecognitionDeep Residual Learning for Image RecognitionDeep Residual Learning for Image RecognitionDeep Residual Learning for Image Recognition"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}