Scheduled Commit
Browse files
data/clustering_individual-60525d87-3fc2-46d0-b2a2-1d09ad087d2a.jsonl
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{"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"}
|
| 2 |
+
{"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"}
|
| 3 |
+
{"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"}
|