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arena-results / data /clustering_individual-60525d87-3fc2-46d0-b2a2-1d09ad087d2a.jsonl
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{"tstamp": 1722293462.6276, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722293449.0254, "finish": 1722293462.6276, "ip": "", "conv_id": "3ef0dd66cb5247fc8b99bb1b55ff4586", "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 Deep Residual Learning for Image Recognition Deep Residual Learning for Image Recognition Deep Residual Learning for Image Recognition"], "ncluster": 2, "output": "", "ndim": "3D", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722293554.1646, "task_type": "clustering", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722293496.4569, "finish": 1722293554.1646, "ip": "", "conv_id": "dceca4d8e09c437989192fcfa3afbaf3", "model_name": "intfloat/multilingual-e5-large-instruct", "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 Deep Residual Learning for Image Recognition Deep Residual Learning for Image Recognition Deep Residual Learning for Image Recognition"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722293554.1646, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1722293496.4569, "finish": 1722293554.1646, "ip": "", "conv_id": "b9746d06bc884c1b81526225da06afed", "model_name": "embed-english-v3.0", "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 Deep Residual Learning for Image Recognition Deep Residual Learning for Image Recognition Deep Residual Learning for Image Recognition"], "ncluster": 2, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722293964.9579, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722293963.3091, "finish": 1722293964.9579, "ip": "", "conv_id": "c855aae2ebad4645adda5fc4c5d111e2", "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", "FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions", "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": 1722293964.9579, "task_type": "clustering", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722293963.3091, "finish": 1722293964.9579, "ip": "", "conv_id": "81abc61206eb4d5bb853d309b0856f16", "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", "FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions", "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": 1722294092.2982, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722294092.2382, "finish": 1722294092.2982, "ip": "", "conv_id": "520210c2c13a4c9cb747d9943ddec7b8", "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", "FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions", "Aya model: An instruction finetuned open-access multilingual language model", "Scaling Data-Constrained Language Models", "Bloom: A 176b-parameter open-access multilingual language model\""], "ncluster": 2, "output": "", "ndim": "3D", "dim_method": "PCA", "clustering_method": "KMeans"}
{"tstamp": 1722294125.4238, "task_type": "clustering", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722294125.3726, "finish": 1722294125.4238, "ip": "", "conv_id": "33664d90cd804c97913f5cc3c23b466c", "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", "FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions", "Aya model: An instruction finetuned open-access multilingual language model", "Scaling Data-Constrained Language Models", "Bloom: A 176b-parameter open-access multilingual language model\""], "ncluster": 3, "output": "", "ndim": "3D", "dim_method": "PCA", "clustering_method": "KMeans"}