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latentscope/clusters/cluster-001-labels-001.json

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latentscope/clusters/cluster-001.json

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latentscope/embeddings/embedding-001.json

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latentscope/jobs/0beda638-628d-47b5-99e3-4abc44d3e9a1.json

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+{"id": "0beda638-628d-47b5-99e3-4abc44d3e9a1", "dataset": "google_wellformed_query", "job_name": "ingest", "command": "ls-ingest \"google_wellformed_query\" --path=\"/Users/enjalot/latent-scope-demo/google_wellformed_query/google_wellformed_query.parquet\"", "status": "completed", "last_update": "2025-01-16 23:37:01.264646", "progress": ["Loading environment variables from: /Users/enjalot/code/latent-scope/.env", "RUNNING: google_wellformed_query", "File type detected: parquet", "reading /Users/enjalot/latent-scope-demo/google_wellformed_query/google_wellformed_query.parquet", "Loading environment variables from: /Users/enjalot/code/latent-scope/.env", "DATA DIR /Users/enjalot/latent-scope-demo", "DIRECTORY /Users/enjalot/latent-scope-demo/google_wellformed_query", "rating                                            content", "0     0.2             The European Union includes how many ?", "1     0.4                What are Mia Hamms accomplishment ?", "2     1.0  Which form of government is still in place in ...", "3     0.8               When was the canal de panama built ?", "4     0.6  What color is the black box on commercial aero...", "rating                                            content", "17495     0.4            What is the youngest college graduate ?", "17496     0.0                     Pros and cons of making dams ?", "17497     0.0             Life and story of jose ma panganiban ?", "17498     1.0   How do you apply for a super delegate position ?", "17499     1.0  What is the incubation period for a giant tort...", "Index(['rating', 'content'], dtype='object')", "checking column types", "COLUMN rating TYPE number", "COLUMN content TYPE string", "wrote /Users/enjalot/latent-scope-demo/google_wellformed_query/input.parquet"], "times": ["2025-01-16 23:37:01.220925", "2025-01-16 23:37:01.222910", "2025-01-16 23:37:01.223776", "2025-01-16 23:37:01.224169", "2025-01-16 23:37:01.247684", "2025-01-16 23:37:01.248220", "2025-01-16 23:37:01.248378", "2025-01-16 23:37:01.250883", "2025-01-16 23:37:01.251412", "2025-01-16 23:37:01.251516", "2025-01-16 23:37:01.251600", "2025-01-16 23:37:01.251701", "2025-01-16 23:37:01.251789", "2025-01-16 23:37:01.252172", "2025-01-16 23:37:01.252275", "2025-01-16 23:37:01.252391", "2025-01-16 23:37:01.252493", "2025-01-16 23:37:01.252580", "2025-01-16 23:37:01.252679", "2025-01-16 23:37:01.252936", "2025-01-16 23:37:01.253059", "2025-01-16 23:37:01.253159", "2025-01-16 23:37:01.253380", "2025-01-16 23:37:01.264643"], "run_id": "google_wellformed_query"}

latentscope/jobs/17299da3-2856-4e5c-88af-83bb3f6c7d8f.json

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+{"id": "17299da3-2856-4e5c-88af-83bb3f6c7d8f", "dataset": "google_wellformed_query", "job_name": "embed", "command": "ls-embed \"google_wellformed_query\" \"content\" \"\ud83e\udd17-nomic-ai___nomic-embed-text-v1.5\" --prefix=\"\" --batch_size=100 --max_seq_length=512", "status": "completed", "last_update": "2025-01-16 23:38:00.249745", "progress": ["Loading environment variables from: /Users/enjalot/code/latent-scope/.env", "RUNNING: embedding-001", "MODEL ID \ud83e\udd17-nomic-ai___nomic-embed-text-v1.5", "MODEL <latentscope.models.providers.transformers.TransformersEmbedProvider object at 0x1043ba930>", "loading nomic-ai/nomic-embed-text-v1.5", "<All keys matched successfully>", "Checking for empty inputs", "embedding 17500 sentences in 175 batches", "", "0%|          | 0/175 [00:00<?, ?it/s]", "1%|          | 1/175 [00:00<01:49,  1.59it/s]", "1%|          | 2/175 [00:00<01:16,  2.25it/s]", "2%|\u258f         | 3/175 [00:01<01:20,  2.14it/s]", "2%|\u258f         | 4/175 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[00:34<00:00,  4.17it/s]", "99%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589| 173/175 [00:35<00:00,  4.88it/s]", "99%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589| 174/175 [00:35<00:00,  5.62it/s]", "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 175/175 [00:35<00:00,  6.25it/s]", "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 175/175 [00:35<00:00,  4.95it/s]", "done with embedding-001"], "times": ["2025-01-16 23:37:18.146617", "2025-01-16 23:37:18.170825", "2025-01-16 23:37:18.171024", "2025-01-16 23:37:18.907328", "2025-01-16 23:37:18.907637", "2025-01-16 23:37:24.402079", "2025-01-16 23:37:24.905637", "2025-01-16 23:37:24.906953", "2025-01-16 23:37:24.907294", "2025-01-16 23:37:25.537871", "2025-01-16 23:37:25.851544", "2025-01-16 23:37:26.347505", "2025-01-16 23:37:26.578700", "2025-01-16 23:37:26.831313", "2025-01-16 23:37:27.092772", "2025-01-16 23:37:27.349195", "2025-01-16 23:37:27.530479", "2025-01-16 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23:38:00.249742"], "run_id": "embedding-001"}

latentscope/jobs/24652e83-e17e-4beb-9e0f-1efb386b2bc0.json

@@ -0,0 +1 @@
+{"id": "24652e83-e17e-4beb-9e0f-1efb386b2bc0", "dataset": "google_wellformed_query", "job_name": "sae", "command": "ls-sae \"google_wellformed_query\" \"embedding-001\" \"enjalot/sae-nomic-text-v1.5-FineWeb-edu-100BT\" 64_32", "status": "completed", "last_update": "2025-01-16 23:38:16.985598", "progress": ["Loading environment variables from: /Users/enjalot/code/latent-scope/.env", "RUNNING: sae-001", "Triton not installed, using eager implementation of SAE decoder.", "loading embeddings", "", "Fetching 2 files:   0%|          | 0/2 [00:00<?, ?it/s]", "Fetching 2 files: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2/2 [00:00<00:00, 5886.74it/s]", "Dropping extra args {'signed': False}", "Encoding embeddings with SAE", "", "Encoding batches:   0%|          | 0/137 [00:00<?, ?it/s]", "Encoding batches:  59%|\u2588\u2588\u2588\u2588\u2588\u2589    | 81/137 [00:00<00:00, 799.43it/s]", "Encoding batches: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 137/137 [00:00<00:00, 348.03it/s]", "encoding completed", "saving to disk", "calculating summary statistics", "ALL ACTS SHAPE (17500, 64)", "ALL INDS SHAPE (17500, 64)", "MATRIX (17500, 24576)", "MAX ACTIVATIONS (24576,)", "MAX ACTIVATIONS [0.03691021 0.04693962 0.         ... 0.         0.17095391 0.0995689 ]", "Number of dead features: 4988", "done with sae-001"], "times": ["2025-01-16 23:38:13.176310", "2025-01-16 23:38:13.176864", "2025-01-16 23:38:14.913535", "2025-01-16 23:38:15.218001", "2025-01-16 23:38:15.556089", "2025-01-16 23:38:15.556517", "2025-01-16 23:38:15.556661", "2025-01-16 23:38:15.557567", "2025-01-16 23:38:16.202749", "2025-01-16 23:38:16.203096", "2025-01-16 23:38:16.304580", "2025-01-16 23:38:16.596870", "2025-01-16 23:38:16.597223", "2025-01-16 23:38:16.879031", "2025-01-16 23:38:16.879564", "2025-01-16 23:38:16.894978", "2025-01-16 23:38:16.895226", "2025-01-16 23:38:16.895407", "2025-01-16 23:38:16.947326", "2025-01-16 23:38:16.970566", "2025-01-16 23:38:16.970877", "2025-01-16 23:38:16.971031", "2025-01-16 23:38:16.985590"], "run_id": "sae-001"}

latentscope/jobs/471047c6-36d7-4383-b57c-b879f192dafc.json

@@ -0,0 +1 @@
+{"id": "471047c6-36d7-4383-b57c-b879f192dafc", "dataset": "google_wellformed_query", "job_name": "cluster", "command": "ls-cluster \"google_wellformed_query\" \"umap-001\" 25 5 0.005", "status": "completed", "last_update": "2025-01-16 23:39:40.940917", "progress": ["Loading environment variables from: /Users/enjalot/code/latent-scope/.env", "RUNNING: cluster-001", "n_clusters: 126", "noise points assigned to clusters: 5781", "cluster  raw_cluster", "0       34           34", "1       98           98", "2       97           97", "3       99           99", "4       48           48", "wrote /Users/enjalot/latent-scope-demo/google_wellformed_query/clusters/cluster-001.parquet", "POINT SIZE 30 for 17500 points", "", "0%|          | 0/126 [00:00<?, ?it/s]", "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 126/126 [00:00<00:00, 8574.94it/s]", "done with cluster-001"], "times": ["2025-01-16 23:39:38.318746", "2025-01-16 23:39:38.319205", "2025-01-16 23:39:40.173555", "2025-01-16 23:39:40.173830", "2025-01-16 23:39:40.177758", "2025-01-16 23:39:40.177899", "2025-01-16 23:39:40.177982", "2025-01-16 23:39:40.178057", "2025-01-16 23:39:40.178127", "2025-01-16 23:39:40.178209", "2025-01-16 23:39:40.178314", "2025-01-16 23:39:40.278673", "2025-01-16 23:39:40.923625", "2025-01-16 23:39:40.938369", "2025-01-16 23:39:40.938545", "2025-01-16 23:39:40.940915"], "run_id": "cluster-001"}

latentscope/jobs/5259606f-6027-4d59-8272-65b9229b7df3.json

@@ -0,0 +1 @@
+{"id": "5259606f-6027-4d59-8272-65b9229b7df3", "dataset": "google_wellformed_query", "job_name": "scope", "command": "ls-scope \"google_wellformed_query\" \"embedding-001\" \"umap-001\" \"cluster-001\" \"cluster-001-labels-001\" \"\ud83e\udd17-nomic-ai/nomic-embed-text-v1.5 + openai-gpt-4o-mini\" \"\" --sae_id=sae-001", "status": "completed", "last_update": "2025-01-16 23:43:05.919728", "progress": ["Loading environment variables from: /Users/enjalot/code/latent-scope/.env", "DATA DIR /Users/enjalot/latent-scope-demo", "RUNNING: scopes-001", "umap columns Index(['x', 'y'], dtype='object')", "cluster columns Index(['cluster', 'raw_cluster', 'label'], dtype='object')", "scope_id None", "scope columns Index(['x', 'y', 'tile_index_64', 'cluster', 'raw_cluster', 'label', 'deleted',", "'ls_index'],", "dtype='object')", "wrote scope scopes-001"], "times": ["2025-01-16 23:43:04.897159", "2025-01-16 23:43:04.898132", "2025-01-16 23:43:04.898594", "2025-01-16 23:43:05.371672", "2025-01-16 23:43:05.846999", "2025-01-16 23:43:05.847550", "2025-01-16 23:43:05.847902", "2025-01-16 23:43:05.848197", "2025-01-16 23:43:05.848466", "2025-01-16 23:43:05.919710"], "run_id": "scopes-001"}

latentscope/jobs/a32a9724-7a6b-405b-8f13-0de7b0d49c27.json

@@ -0,0 +1 @@
+{"id": "a32a9724-7a6b-405b-8f13-0de7b0d49c27", "dataset": "google_wellformed_query", "job_name": "label", "command": "ls-label \"google_wellformed_query\" \"content\" \"cluster-001\" \"openai-gpt-4o-mini\" 10 \"\" --max_tokens_per_sample=512 --max_tokens_total=8192", "status": "completed", "last_update": "2025-01-16 23:42:57.851988", "progress": ["Loading environment variables from: /Users/enjalot/code/latent-scope/.env", "RUNNING: cluster-001-labels-001", "Loading model openai-gpt-4o-mini (may take a while if first time downloading from HF)", "get key /Users/enjalot/code/latent-scope", "Model loaded", "", "Preparing extracts:   0%|          | 0/126 [00:00<?, ?it/s]", "Preparing extracts:  86%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 108/126 [00:00<00:00, 1069.31it/s]", "Preparing extracts: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 126/126 [00:00<00:00, 1074.02it/s]", "", "0%|          | 0/126 [00:00<?, ?it/s]", "", "cluster 0 label: Automotive Repair Questions", "", "0%|          | 0/126 [00:00<?, ?it/s]", "1%|          | 1/126 [00:00<01:21,  1.54it/s]", "", "cluster 1 label: Pokemon Evolution Levels", "", "1%|          | 1/126 [00:02<01:21,  1.54it/s]", "2%|\u258f         | 2/126 [00:02<02:24,  1.17s/it]", "", "cluster 2 label: Travel Time and Distance", "", "2%|\u258f         | 2/126 [00:03<02:24,  1.17s/it]", "2%|\u258f         | 3/126 [00:03<02:27,  1.20s/it]", "", "cluster 3 label: Postal Code Inquiries", "", "2%|\u258f         | 3/126 [00:04<02:27,  1.20s/it]", "3%|\u258e         | 4/126 [00:04<02:27,  1.21s/it]", "", "cluster 4 label: Cost of Various Items", "", "3%|\u258e         | 4/126 [00:05<02:27,  1.21s/it]", "4%|\u258d         | 5/126 [00:05<02:26,  1.21s/it]", "", "cluster 5 label: Salary Inquiry by Profession", "", "4%|\u258d         | 5/126 [00:07<02:26,  1.21s/it]", "5%|\u258d         | 6/126 [00:07<02:34,  1.29s/it]", "", "cluster 6 label: Celebrity Asset Values", "", "5%|\u258d         | 6/126 [00:08<02:34,  1.29s/it]", "6%|\u258c         | 7/126 [00:08<02:35,  1.30s/it]", "", "cluster 7 label: Coin and Currency Values", "", "6%|\u258c         | 7/126 [00:09<02:35,  1.30s/it]", "6%|\u258b         | 8/126 [00:09<02:29,  1.27s/it]", "", "cluster 8 label: Mathematical Factors and Digits", "", "6%|\u258b         | 8/126 [00:11<02:29,  1.27s/it]", "7%|\u258b         | 9/126 [00:11<02:33,  1.32s/it]", "", "cluster 9 label: Calorie Count Questions", "", "7%|\u258b         | 9/126 [00:12<02:33,  1.32s/it]", "8%|\u258a         | 10/126 [00:12<02:38,  1.37s/it]", "", "cluster 10 label: Medication Interaction Questions", "", "8%|\u258a         | 10/126 [00:13<02:38,  1.37s/it]", "9%|\u258a         | 11/126 [00:13<02:30,  1.31s/it]", "", "cluster 11 label: Birthplace and Birthdate Queries", "", "9%|\u258a         | 11/126 [00:15<02:30,  1.31s/it]", "10%|\u2589         | 12/126 [00:15<02:29,  1.31s/it]", "", "cluster 12 label: Celebrity Death Inquiries", "", "10%|\u2589         | 12/126 [00:16<02:29,  1.31s/it]", "10%|\u2588         | 13/126 [00:16<02:34,  1.37s/it]", "", "cluster 13 label: Legal Penalties and Limitations", "", "10%|\u2588         | 13/126 [00:18<02:34,  1.37s/it]", "11%|\u2588         | 14/126 [00:18<02:32,  1.36s/it]", "", "cluster 14 label: Age Restrictions and Queries", "", "11%|\u2588         | 14/126 [00:19<02:32,  1.36s/it]", "12%|\u2588\u258f        | 15/126 [00:19<02:26,  1.32s/it]", "", "cluster 15 label: Size and Dimensions Questions", "", "12%|\u2588\u258f        | 15/126 [00:20<02:26,  1.32s/it]", "13%|\u2588\u258e        | 16/126 [00:20<02:22,  1.30s/it]", "", "cluster 16 label: Speed Comparisons", "", "13%|\u2588\u258e        | 16/126 [00:21<02:22,  1.30s/it]", "13%|\u2588\u258e        | 17/126 [00:21<02:25,  1.33s/it]", "", "cluster 17 label: Famous Alumni Education Background", "", "13%|\u2588\u258e        | 17/126 [00:23<02:25,  1.33s/it]", "14%|\u2588\u258d        | 18/126 [00:23<02:20,  1.31s/it]", "", "cluster 18 label: Weight of Various Subjects", "", "14%|\u2588\u258d        | 18/126 [00:24<02:20,  1.31s/it]", "15%|\u2588\u258c        | 19/126 [00:24<02:13,  1.25s/it]", "", "cluster 19 label: Area and Distance Conversions", "", "15%|\u2588\u258c        | 19/126 [00:25<02:13,  1.25s/it]", "16%|\u2588\u258c        | 20/126 [00:25<02:12,  1.25s/it]", "", "cluster 20 label: Unit Conversion Queries", "", "16%|\u2588\u258c        | 20/126 [00:26<02:12,  1.25s/it]", "17%|\u2588\u258b        | 21/126 [00:26<02:11,  1.25s/it]", "", "cluster 21 label: Education Duration for Professions", "", "17%|\u2588\u258b        | 21/126 [00:28<02:11,  1.25s/it]", "17%|\u2588\u258b        | 22/126 [00:28<02:10,  1.25s/it]", "", "cluster 22 label: Educational Qualifications Required", "", "17%|\u2588\u258b        | 22/126 [00:29<02:10,  1.25s/it]", "18%|\u2588\u258a        | 23/126 [00:29<02:06,  1.23s/it]", "", "cluster 23 label: Weight and Mass Conversions", "", "18%|\u2588\u258a        | 23/126 [00:30<02:06,  1.23s/it]", "19%|\u2588\u2589        | 24/126 [00:30<02:10,  1.28s/it]", "", "cluster 24 label: Volume Conversion Questions", "", "19%|\u2588\u2589        | 24/126 [00:31<02:10,  1.28s/it]", "20%|\u2588\u2589        | 25/126 [00:31<02:07,  1.26s/it]", "", "cluster 25 label: Geography and Teen Issues", "", "20%|\u2588\u2589        | 25/126 [00:33<02:07,  1.26s/it]", "21%|\u2588\u2588        | 26/126 [00:33<02:06,  1.27s/it]", "", "cluster 26 label: Origins of Names", "", "21%|\u2588\u2588        | 26/126 [00:34<02:06,  1.27s/it]", "21%|\u2588\u2588\u258f       | 27/126 [00:34<02:06,  1.27s/it]", "", "cluster 27 label: Measurement Conversions Cooking", "", "21%|\u2588\u2588\u258f       | 27/126 [00:35<02:06,  1.27s/it]", "22%|\u2588\u2588\u258f       | 28/126 [00:35<02:07,  1.30s/it]", "", "cluster 28 label: Volume and Measurement Conversions", "", "22%|\u2588\u2588\u258f       | 28/126 [00:36<02:07,  1.30s/it]", "23%|\u2588\u2588\u258e       | 29/126 [00:36<02:01,  1.25s/it]", "", "cluster 29 label: Statistics on mortality causes", "", "23%|\u2588\u2588\u258e       | 29/126 [00:38<02:01,  1.25s/it]", "24%|\u2588\u2588\u258d       | 30/126 [00:38<02:04,  1.29s/it]", "", "cluster 30 label: Global Population Inquiries", "", "24%|\u2588\u2588\u258d       | 30/126 [00:39<02:04,  1.29s/it]", "25%|\u2588\u2588\u258d       | 31/126 [00:39<02:06,  1.33s/it]", "", "cluster 31 label: Famous People and Children", "", "25%|\u2588\u2588\u258d       | 31/126 [00:41<02:06,  1.33s/it]", "25%|\u2588\u2588\u258c       | 32/126 [00:41<02:05,  1.34s/it]", "", "cluster 32 label: Celebrity Contact Information", "", "25%|\u2588\u2588\u258c       | 32/126 [00:42<02:05,  1.34s/it]", "26%|\u2588\u2588\u258c       | 33/126 [00:42<02:07,  1.37s/it]", "", "cluster 33 label: Celebrity Phone Numbers", "", "26%|\u2588\u2588\u258c       | 33/126 [00:43<02:07,  1.37s/it]", "27%|\u2588\u2588\u258b       | 34/126 [00:43<02:04,  1.35s/it]", "", "cluster 34 label: State Representation and Population", "", "27%|\u2588\u2588\u258b       | 34/126 [00:44<02:04,  1.35s/it]", "28%|\u2588\u2588\u258a       | 35/126 [00:45<01:57,  1.29s/it]", "", "cluster 35 label: Currency inquiries by country", "", "28%|\u2588\u2588\u258a       | 35/126 [00:46<01:57,  1.29s/it]", "29%|\u2588\u2588\u258a       | 36/126 [00:46<01:55,  1.28s/it]", "", "cluster 36 label: Music storage questions", "", "29%|\u2588\u2588\u258a       | 36/126 [00:47<01:55,  1.28s/it]", "29%|\u2588\u2588\u2589       | 37/126 [00:47<01:53,  1.27s/it]", "", "cluster 37 label: Famous Characters and Ideas", "", "29%|\u2588\u2588\u2589       | 37/126 [00:48<01:53,  1.27s/it]", "30%|\u2588\u2588\u2588       | 38/126 [00:48<01:54,  1.30s/it]", "", "cluster 38 label: Information and Queries", "", "30%|\u2588\u2588\u2588       | 38/126 [00:50<01:54,  1.30s/it]", "31%|\u2588\u2588\u2588       | 39/126 [00:50<01:51,  1.28s/it]", "", "cluster 39 label: Game Cheats and Codes", "", "31%|\u2588\u2588\u2588       | 39/126 [00:51<01:51,  1.28s/it]", "32%|\u2588\u2588\u2588\u258f      | 40/126 [00:51<01:48,  1.26s/it]", "", "cluster 40 label: Poptropica Gameplay Tips", "", "32%|\u2588\u2588\u2588\u258f      | 40/126 [00:52<01:48,  1.26s/it]", "33%|\u2588\u2588\u2588\u258e      | 41/126 [00:52<01:49,  1.28s/it]", "", "cluster 41 label: Game Tips and Tricks", "", "33%|\u2588\u2588\u2588\u258e      | 41/126 [00:54<01:49,  1.28s/it]", "33%|\u2588\u2588\u2588\u258e      | 42/126 [00:54<01:49,  1.31s/it]", "", "cluster 42 label: Where to buy items", "", "33%|\u2588\u2588\u2588\u258e      | 42/126 [00:55<01:49,  1.31s/it]", "34%|\u2588\u2588\u2588\u258d      | 43/126 [00:55<01:46,  1.28s/it]", "", "cluster 43 label: Symbol and Ticker Queries", "", "34%|\u2588\u2588\u2588\u258d      | 43/126 [00:56<01:46,  1.28s/it]", "35%|\u2588\u2588\u2588\u258d      | 44/126 [00:56<01:46,  1.30s/it]", "", "cluster 44 label: Atomic Structure Questions", "", "35%|\u2588\u2588\u2588\u258d      | 44/126 [00:57<01:46,  1.30s/it]", "36%|\u2588\u2588\u2588\u258c      | 45/126 [00:57<01:41,  1.26s/it]", "", "cluster 45 label: Cooking and Ingredients", "", "36%|\u2588\u2588\u2588\u258c      | 45/126 [00:58<01:41,  1.26s/it]", "37%|\u2588\u2588\u2588\u258b      | 46/126 [00:58<01:38,  1.23s/it]", "", "cluster 46 label: Song Inquiries and Themes", "", "37%|\u2588\u2588\u2588\u258b      | 46/126 [01:00<01:38,  1.23s/it]", "37%|\u2588\u2588\u2588\u258b      | 47/126 [01:00<01:36,  1.22s/it]", "", "cluster 47 label: Geographical Inquiries and Trivia", "", "37%|\u2588\u2588\u2588\u258b      | 47/126 [01:01<01:36,  1.22s/it]", "38%|\u2588\u2588\u2588\u258a      | 48/126 [01:01<01:38,  1.26s/it]", "", "cluster 48 label: Color-related Inquiries", "", "38%|\u2588\u2588\u2588\u258a      | 48/126 [01:02<01:38,  1.26s/it]", "39%|\u2588\u2588\u2588\u2589      | 49/126 [01:02<01:41,  1.31s/it]", "", "cluster 49 label: Worlds Biggest Records", "", "39%|\u2588\u2588\u2588\u2589      | 49/126 [01:04<01:41,  1.31s/it]", "40%|\u2588\u2588\u2588\u2589      | 50/126 [01:04<01:42,  1.35s/it]", "", "cluster 50 label: Measurement and Normal Values", "", "40%|\u2588\u2588\u2588\u2589      | 50/126 [01:05<01:42,  1.35s/it]", "40%|\u2588\u2588\u2588\u2588      | 51/126 [01:05<01:38,  1.31s/it]", "", "cluster 51 label: Release Date Inquiries", "", "40%|\u2588\u2588\u2588\u2588      | 51/126 [01:06<01:38,  1.31s/it]", "41%|\u2588\u2588\u2588\u2588\u258f     | 52/126 [01:06<01:36,  1.30s/it]", "", "cluster 52 label: Celebrity Personal Favorites", "", "41%|\u2588\u2588\u2588\u2588\u258f     | 52/126 [01:08<01:36,  1.30s/it]", "42%|\u2588\u2588\u2588\u2588\u258f     | 53/126 [01:08<01:45,  1.45s/it]", "", "cluster 53 label: Coral Reef Information", "", "42%|\u2588\u2588\u2588\u2588\u258f     | 53/126 [01:09<01:45,  1.45s/it]", "43%|\u2588\u2588\u2588\u2588\u258e     | 54/126 [01:09<01:38,  1.37s/it]", "", "cluster 54 label: Awards and Achievements", "", "43%|\u2588\u2588\u2588\u2588\u258e     | 54/126 [01:11<01:38,  1.37s/it]", "44%|\u2588\u2588\u2588\u2588\u258e     | 55/126 [01:11<01:35,  1.35s/it]", "", "cluster 55 label: Celebrity Residences and Locations", "", "44%|\u2588\u2588\u2588\u2588\u258e     | 55/126 [01:12<01:35,  1.35s/it]", "44%|\u2588\u2588\u2588\u2588\u258d     | 56/126 [01:12<01:31,  1.31s/it]", "", "cluster 56 label: Sibling Relationships Inquiries", "", "44%|\u2588\u2588\u2588\u2588\u258d     | 56/126 [01:13<01:31,  1.31s/it]", "45%|\u2588\u2588\u2588\u2588\u258c     | 57/126 [01:13<01:29,  1.30s/it]", "", "cluster 57 label: Family and Parental Inquiries", "", "45%|\u2588\u2588\u2588\u2588\u258c     | 57/126 [01:14<01:29,  1.30s/it]", "46%|\u2588\u2588\u2588\u2588\u258c     | 58/126 [01:14<01:28,  1.31s/it]", "", "cluster 58 label: Celebrity Careers and Works", "", "46%|\u2588\u2588\u2588\u2588\u258c     | 58/126 [01:16<01:28,  1.31s/it]", "47%|\u2588\u2588\u2588\u2588\u258b     | 59/126 [01:16<01:32,  1.38s/it]", "", "cluster 59 label: Famous Personalities and Culture", "", "47%|\u2588\u2588\u2588\u2588\u258b     | 59/126 [01:17<01:32,  1.38s/it]", "48%|\u2588\u2588\u2588\u2588\u258a     | 60/126 [01:17<01:27,  1.33s/it]", "", "cluster 60 label: Celebrity Relationship Questions", "", "48%|\u2588\u2588\u2588\u2588\u258a     | 60/126 [01:19<01:27,  1.33s/it]", "48%|\u2588\u2588\u2588\u2588\u258a     | 61/126 [01:19<01:33,  1.43s/it]", "", "cluster 61 label: Important Dates and Deadlines", "", "48%|\u2588\u2588\u2588\u2588\u258a     | 61/126 [01:20<01:33,  1.43s/it]", "49%|\u2588\u2588\u2588\u2588\u2589     | 62/126 [01:20<01:28,  1.38s/it]", "", "cluster 62 label: Languages and their regions", "", "49%|\u2588\u2588\u2588\u2588\u2589     | 62/126 [01:21<01:28,  1.38s/it]", "50%|\u2588\u2588\u2588\u2588\u2588     | 63/126 [01:21<01:23,  1.33s/it]", "", "cluster 63 label: Real Names and Identities", "", "50%|\u2588\u2588\u2588\u2588\u2588     | 63/126 [01:22<01:23,  1.33s/it]", "51%|\u2588\u2588\u2588\u2588\u2588     | 64/126 [01:22<01:18,  1.27s/it]", "", "cluster 64 label: Celebrity Names and Questions", "", "51%|\u2588\u2588\u2588\u2588\u2588     | 64/126 [01:24<01:18,  1.27s/it]", "52%|\u2588\u2588\u2588\u2588\u2588\u258f    | 65/126 [01:24<01:15,  1.24s/it]", "", "cluster 65 label: Highest Mountains and Elevations", "", "52%|\u2588\u2588\u2588\u2588\u2588\u258f    | 65/126 [01:25<01:15,  1.24s/it]", "52%|\u2588\u2588\u2588\u2588\u2588\u258f    | 66/126 [01:25<01:18,  1.30s/it]", "", "cluster 66 label: Animal Names and Habitats", "", "52%|\u2588\u2588\u2588\u2588\u2588\u258f    | 66/126 [01:26<01:18,  1.30s/it]", "53%|\u2588\u2588\u2588\u2588\u2588\u258e    | 67/126 [01:26<01:16,  1.29s/it]", "", "cluster 67 label: Animal Predators and Habitat", "", "53%|\u2588\u2588\u2588\u2588\u2588\u258e    | 67/126 [01:27<01:16,  1.29s/it]", "54%|\u2588\u2588\u2588\u2588\u2588\u258d    | 68/126 [01:27<01:11,  1.24s/it]", "", "cluster 68 label: Phylum of Various Worms", "", "54%|\u2588\u2588\u2588\u2588\u2588\u258d    | 68/126 [01:29<01:11,  1.24s/it]", "55%|\u2588\u2588\u2588\u2588\u2588\u258d    | 69/126 [01:29<01:13,  1.29s/it]", "", "cluster 69 label: Scientific Names of Plants", "", "55%|\u2588\u2588\u2588\u2588\u2588\u258d    | 69/126 [01:30<01:13,  1.29s/it]", "56%|\u2588\u2588\u2588\u2588\u2588\u258c    | 70/126 [01:30<01:12,  1.30s/it]", "", "cluster 70 label: Origins of Cultural Elements", "", "56%|\u2588\u2588\u2588\u2588\u2588\u258c    | 70/126 [01:31<01:12,  1.30s/it]", "56%|\u2588\u2588\u2588\u2588\u2588\u258b    | 71/126 [01:31<01:09,  1.27s/it]", "", "cluster 71 label: Winter Survival Adaptations", "", "56%|\u2588\u2588\u2588\u2588\u2588\u258b    | 71/126 [01:33<01:09,  1.27s/it]", "57%|\u2588\u2588\u2588\u2588\u2588\u258b    | 72/126 [01:33<01:06,  1.23s/it]", "", "cluster 72 label: Poisonous Animals and Plants", "", "57%|\u2588\u2588\u2588\u2588\u2588\u258b    | 72/126 [01:34<01:06,  1.23s/it]", "58%|\u2588\u2588\u2588\u2588\u2588\u258a    | 73/126 [01:34<01:04,  1.22s/it]", "", "cluster 73 label: Animal Diets and Preferences", "", "58%|\u2588\u2588\u2588\u2588\u2588\u258a    | 73/126 [01:35<01:04,  1.22s/it]", "59%|\u2588\u2588\u2588\u2588\u2588\u258a    | 74/126 [01:35<01:09,  1.34s/it]", "", "cluster 74 label: Italian Culture and Traditions", "", "59%|\u2588\u2588\u2588\u2588\u2588\u258a    | 74/126 [01:37<01:09,  1.34s/it]", "60%|\u2588\u2588\u2588\u2588\u2588\u2589    | 75/126 [01:37<01:08,  1.34s/it]", "", "cluster 75 label: Environmental Effects and Phenomena", "", "60%|\u2588\u2588\u2588\u2588\u2588\u2589    | 75/126 [01:38<01:08,  1.34s/it]", "60%|\u2588\u2588\u2588\u2588\u2588\u2588    | 76/126 [01:38<01:06,  1.32s/it]", "", "cluster 76 label: Sports and Environment Questions", "", "60%|\u2588\u2588\u2588\u2588\u2588\u2588    | 76/126 [01:39<01:06,  1.32s/it]", "61%|\u2588\u2588\u2588\u2588\u2588\u2588    | 77/126 [01:39<01:03,  1.30s/it]", "", "cluster 77 label: Roles and Responsibilities", "", "61%|\u2588\u2588\u2588\u2588\u2588\u2588    | 77/126 [01:40<01:03,  1.30s/it]", "62%|\u2588\u2588\u2588\u2588\u2588\u2588\u258f   | 78/126 [01:40<01:01,  1.27s/it]", "", "cluster 78 label: Geography and Climate Queries", "", "62%|\u2588\u2588\u2588\u2588\u2588\u2588\u258f   | 78/126 [01:42<01:01,  1.27s/it]", "63%|\u2588\u2588\u2588\u2588\u2588\u2588\u258e   | 79/126 [01:42<00:59,  1.27s/it]", "", "cluster 79 label: Sports and Teams Inquiry", "", "63%|\u2588\u2588\u2588\u2588\u2588\u2588\u258e   | 79/126 [01:43<00:59,  1.27s/it]", "63%|\u2588\u2588\u2588\u2588\u2588\u2588\u258e   | 80/126 [01:43<00:57,  1.25s/it]", "", "cluster 80 label: Government and Agriculture Issues", "", "63%|\u2588\u2588\u2588\u2588\u2588\u2588\u258e   | 80/126 [01:44<00:57,  1.25s/it]", "64%|\u2588\u2588\u2588\u2588\u2588\u2588\u258d   | 81/126 [01:44<00:54,  1.22s/it]", "", "cluster 81 label: Sentence Construction Queries", "", "64%|\u2588\u2588\u2588\u2588\u2588\u2588\u258d   | 81/126 [01:45<00:54,  1.22s/it]", "65%|\u2588\u2588\u2588\u2588\u2588\u2588\u258c   | 82/126 [01:45<00:51,  1.18s/it]", "", "cluster 82 label: Nautical and Writing Concepts", "", "65%|\u2588\u2588\u2588\u2588\u2588\u2588\u258c   | 82/126 [01:46<00:51,  1.18s/it]", "66%|\u2588\u2588\u2588\u2588\u2588\u2588\u258c   | 83/126 [01:46<00:49,  1.16s/it]", "", "cluster 83 label: Measurement and Calculation Queries", "", "66%|\u2588\u2588\u2588\u2588\u2588\u2588\u258c   | 83/126 [01:47<00:49,  1.16s/it]", "67%|\u2588\u2588\u2588\u2588\u2588\u2588\u258b   | 84/126 [01:47<00:49,  1.18s/it]", "", "cluster 84 label: Earth and Atmospheric Science", "", "67%|\u2588\u2588\u2588\u2588\u2588\u2588\u258b   | 84/126 [01:49<00:49,  1.18s/it]", "67%|\u2588\u2588\u2588\u2588\u2588\u2588\u258b   | 85/126 [01:49<00:48,  1.18s/it]", "", "cluster 85 label: Leaders and Leadership Questions", "", "67%|\u2588\u2588\u2588\u2588\u2588\u2588\u258b   | 85/126 [01:50<00:48,  1.18s/it]", "68%|\u2588\u2588\u2588\u2588\u2588\u2588\u258a   | 86/126 [01:50<00:46,  1.16s/it]", "", "cluster 86 label: Temperature Measurement Queries", "", "68%|\u2588\u2588\u2588\u2588\u2588\u2588\u258a   | 86/126 [01:51<00:46,  1.16s/it]", "69%|\u2588\u2588\u2588\u2588\u2588\u2588\u2589   | 87/126 [01:51<00:46,  1.19s/it]", "", "cluster 87 label: Planetary Facts and Mysteries", "", "69%|\u2588\u2588\u2588\u2588\u2588\u2588\u2589   | 87/126 [01:53<00:46,  1.19s/it]", "70%|\u2588\u2588\u2588\u2588\u2588\u2588\u2589   | 88/126 [01:53<00:48,  1.29s/it]", "", "cluster 88 label: Temperature and Energy Effects", "", "70%|\u2588\u2588\u2588\u2588\u2588\u2588\u2589   | 88/126 [01:54<00:48,  1.29s/it]", "71%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588   | 89/126 [01:54<00:47,  1.28s/it]", "", "cluster 89 label: Religions and their Followers", "", "71%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588   | 89/126 [01:55<00:47,  1.28s/it]", "71%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f  | 90/126 [01:55<00:47,  1.33s/it]", "", "cluster 90 label: Religious Beliefs and Practices", "", "71%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f  | 90/126 [01:57<00:47,  1.33s/it]", "72%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f  | 91/126 [01:57<00:47,  1.35s/it]", "", "cluster 91 label: Plant Biology and Light", "", "72%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f  | 91/126 [01:58<00:47,  1.35s/it]", "73%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e  | 92/126 [01:58<00:44,  1.32s/it]", "", "cluster 92 label: Signs and Symptoms Inquiry", "", "73%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e  | 92/126 [01:59<00:44,  1.32s/it]", "74%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d  | 93/126 [01:59<00:43,  1.33s/it]", "", "cluster 93 label: Famous Historical Figures", "", "74%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d  | 93/126 [02:01<00:43,  1.33s/it]", "75%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d  | 94/126 [02:01<00:44,  1.39s/it]", "", "cluster 94 label: Examples and Definitions", "", "75%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d  | 94/126 [02:02<00:44,  1.39s/it]", "75%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c  | 95/126 [02:02<00:42,  1.37s/it]", "", "cluster 95 label: Environmental Challenges and Solutions", "", "75%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c  | 95/126 [02:03<00:42,  1.37s/it]", "76%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c  | 96/126 [02:03<00:41,  1.37s/it]", "", "cluster 96 label: Rivers and Geography Questions", "", "76%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c  | 96/126 [02:05<00:41,  1.37s/it]", "77%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b  | 97/126 [02:05<00:38,  1.32s/it]", "", "cluster 97 label: Politics and History Questions", "", "77%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b  | 97/126 [02:06<00:38,  1.32s/it]", "78%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a  | 98/126 [02:06<00:38,  1.38s/it]", "", "cluster 98 label: Historical Questions", "", "78%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a  | 98/126 [02:08<00:38,  1.38s/it]", "79%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a  | 99/126 [02:08<00:36,  1.36s/it]", "", "cluster 99 label: Historical Questions and Events", "", "79%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a  | 99/126 [02:09<00:36,  1.36s/it]", "79%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589  | 100/126 [02:09<00:34,  1.33s/it]", "", "cluster 100 label: Economic Concepts and Management", "", "79%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589  | 100/126 [02:10<00:34,  1.33s/it]", "80%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  | 101/126 [02:10<00:34,  1.36s/it]", "", "cluster 101 label: Knowledge and Understanding Concepts", "", "80%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  | 101/126 [02:12<00:34,  1.36s/it]", "81%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  | 102/126 [02:12<00:32,  1.36s/it]", "", "cluster 102 label: Pregnancy and Reproductive Health", "", "81%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  | 102/126 [02:13<00:32,  1.36s/it]", "82%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f | 103/126 [02:13<00:30,  1.32s/it]", "", "cluster 103 label: Cultural Attire and Beliefs", "", "82%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f | 103/126 [02:16<00:30,  1.32s/it]", "83%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e | 104/126 [02:16<00:39,  1.79s/it]", "", "cluster 104 label: Culinary Traditions and Imports", "", "83%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e | 104/126 [02:17<00:39,  1.79s/it]", "83%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e | 105/126 [02:17<00:35,  1.71s/it]", "", "cluster 105 label: Scientific Discoveries and Discoverers", "", "83%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e | 105/126 [02:19<00:35,  1.71s/it]", "84%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 106/126 [02:19<00:36,  1.80s/it]", "", "cluster 106 label: Geographical and Cultural Queries", "", "84%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 106/126 [02:20<00:36,  1.80s/it]", "85%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 107/126 [02:20<00:30,  1.60s/it]", "", "cluster 107 label: Colonial Economic Resources", "", "85%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 107/126 [02:22<00:30,  1.60s/it]", "86%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 108/126 [02:22<00:26,  1.49s/it]", "", "cluster 108 label: Invention Years and Origins", "", "86%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 108/126 [02:23<00:26,  1.49s/it]", "87%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b | 109/126 [02:23<00:24,  1.44s/it]", "", "cluster 109 label: Inventions and Inventors", "", "87%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b | 109/126 [02:24<00:24,  1.44s/it]", "87%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b | 110/126 [02:24<00:21,  1.36s/it]", "", "cluster 110 label: Capital Cities Inquiry", "", "87%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b | 110/126 [02:26<00:21,  1.36s/it]", "88%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 111/126 [02:26<00:20,  1.39s/it]", "", "cluster 111 label: Geographical Location Questions", "", "88%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 111/126 [02:27<00:20,  1.39s/it]", "89%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589 | 112/126 [02:27<00:19,  1.38s/it]", "", "cluster 112 label: Biological and Medical Queries", "", "89%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589 | 112/126 [02:28<00:19,  1.38s/it]", "90%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589 | 113/126 [02:28<00:17,  1.34s/it]", "", "cluster 113 label: Biological Materials and Processes", "", "90%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589 | 113/126 [02:30<00:17,  1.34s/it]", "90%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 114/126 [02:30<00:16,  1.36s/it]", "", "cluster 114 label: Photosynthesis and Energy Concepts", "", "90%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 114/126 [02:34<00:16,  1.36s/it]", "91%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f| 115/126 [02:34<00:26,  2.37s/it]", "", "cluster 115 label: Waves and Mediums", "", "91%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f| 115/126 [02:36<00:26,  2.37s/it]", "92%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f| 116/126 [02:36<00:21,  2.12s/it]", "", "cluster 116 label: Environmental and Health Concerns", "", "92%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f| 116/126 [02:37<00:21,  2.12s/it]", "93%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e| 117/126 [02:37<00:16,  1.85s/it]", "", "cluster 117 label: Materials and Their Uses", "", "93%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e| 117/126 [02:38<00:16,  1.85s/it]", "94%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e| 118/126 [02:38<00:13,  1.66s/it]", "", "cluster 118 label: Scientific Concepts and Processes", "", "94%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e| 118/126 [02:40<00:13,  1.66s/it]", "94%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d| 119/126 [02:40<00:10,  1.56s/it]", "", "cluster 119 label: Properties and Composition of Matter", "", "94%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d| 119/126 [02:41<00:10,  1.56s/it]", "95%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c| 120/126 [02:41<00:08,  1.43s/it]", "", "cluster 120 label: Chemical Compounds and Properties", "", "95%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c| 120/126 [02:42<00:08,  1.43s/it]", "96%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c| 121/126 [02:42<00:07,  1.43s/it]", "", "cluster 121 label: Biological Concepts and Structures", "", "96%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c| 121/126 [02:43<00:07,  1.43s/it]", "97%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b| 122/126 [02:43<00:05,  1.39s/it]", "", "cluster 122 label: Health Issues and Inflammation", "", "97%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b| 122/126 [02:45<00:05,  1.39s/it]", "98%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a| 123/126 [02:45<00:04,  1.45s/it]", "", "cluster 123 label: Medical Terminology and Pathogens", "", "98%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a| 123/126 [02:46<00:04,  1.45s/it]", "98%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a| 124/126 [02:46<00:02,  1.45s/it]", "", "cluster 124 label: Biological Functions and Processes", "", "98%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a| 124/126 [02:48<00:02,  1.45s/it]", "99%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589| 125/126 [02:48<00:01,  1.40s/it]", "", "cluster 125 label: Mind Body Functionality", "", "99%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589| 125/126 [02:49<00:01,  1.40s/it]", "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 126/126 [02:49<00:00,  1.38s/it]", "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 126/126 [02:49<00:00,  1.35s/it]", "labels: 126", "done with cluster-001-labels-001"], "times": ["2025-01-16 23:40:06.607825", "2025-01-16 23:40:06.640837", "2025-01-16 23:40:06.641060", "2025-01-16 23:40:07.964525", "2025-01-16 23:40:08.155842", "2025-01-16 23:40:08.156102", "2025-01-16 23:40:08.257220", "2025-01-16 23:40:08.273495", "2025-01-16 23:40:08.273764", "2025-01-16 23:40:08.273882", "2025-01-16 23:40:08.911636", "2025-01-16 23:40:08.912098", "2025-01-16 23:40:08.912310", "2025-01-16 23:40:08.912477", "2025-01-16 23:40:08.922459", "2025-01-16 23:40:10.441117", "2025-01-16 23:40:10.442723", "2025-01-16 23:40:10.443503", "2025-01-16 23:40:10.444068", "2025-01-16 23:40:10.452407", "2025-01-16 23:40:11.683292", 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latentscope/jobs/cad25526-e993-4f6a-bba4-647e776f340c.json

@@ -0,0 +1 @@
+{"id": "cad25526-e993-4f6a-bba4-647e776f340c", "dataset": "google_wellformed_query", "job_name": "umap", "command": "ls-umap \"google_wellformed_query\" \"embedding-001\" 25 0.1 --seed=42", "status": "completed", "last_update": "2025-01-16 23:38:47.640165", "progress": ["Loading environment variables from: /Users/enjalot/code/latent-scope/.env", "RUNNING: umap-001", "loading embeddings", "Loading environment variables from: /Users/enjalot/code/latent-scope/.env", "reducing 768 embeddings to 2 dimensions", "/Users/enjalot/code/latent-scope/testenv/lib/python3.12/site-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.", "warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")", "UMAP(angular_rp_forest=True, metric='cosine', n_jobs=1, n_neighbors=25, random_state=42, verbose=True)", "Thu Jan 16 23:38:24 2025 Construct fuzzy simplicial set", "Thu Jan 16 23:38:24 2025 Finding Nearest Neighbors", "Thu Jan 16 23:38:24 2025 Building RP forest with 12 trees", "Thu Jan 16 23:38:26 2025 NN descent for 14 iterations", "1  /  14", "2  /  14", "3  /  14", "4  /  14", "5  /  14", "6  /  14", "Stopping threshold met -- exiting after 6 iterations", "Thu Jan 16 23:38:36 2025 Finished Nearest Neighbor Search", "Thu Jan 16 23:38:37 2025 Construct embedding", "", "Epochs completed:   0%|            0/200 [00:00]\tcompleted  0  /  200 epochs", "", "Epochs completed:   0%|            1/200 [00:00]", "Epochs completed:   3%| \u258e          6/200 [00:00]", "Epochs completed:   4%| \u258d          9/200 [00:00]", "Epochs completed:   6%| \u258c          12/200 [00:00]", "Epochs completed:   8%| \u258a          15/200 [00:00]", "Epochs completed:   9%| \u2589          18/200 [00:00]\tcompleted  20  /  200 epochs", "", "Epochs completed:  10%| \u2588          21/200 [00:01]", "Epochs completed:  12%| \u2588\u258f         24/200 [00:01]", "Epochs completed:  14%| \u2588\u258e         27/200 [00:01]", "Epochs completed:  15%| \u2588\u258c         30/200 [00:01]", "Epochs completed:  16%| \u2588\u258b         33/200 [00:01]", "Epochs completed:  18%| \u2588\u258a         36/200 [00:01]", "Epochs completed:  20%| \u2588\u2589         39/200 [00:01]\tcompleted  40  /  200 epochs", "", "Epochs completed:  21%| \u2588\u2588         42/200 [00:01]", "Epochs completed:  22%| \u2588\u2588\u258e        45/200 [00:02]", "Epochs completed:  24%| \u2588\u2588\u258d        48/200 [00:02]", "Epochs completed:  26%| \u2588\u2588\u258c        51/200 [00:02]", "Epochs completed:  27%| \u2588\u2588\u258b        54/200 [00:02]", "Epochs completed:  28%| \u2588\u2588\u258a        57/200 [00:02]", "Epochs completed:  30%| \u2588\u2588\u2588        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completed:  66%| \u2588\u2588\u2588\u2588\u2588\u2588\u258c    132/200 [00:05]", "Epochs completed:  68%| \u2588\u2588\u2588\u2588\u2588\u2588\u258a    135/200 [00:05]", "Epochs completed:  69%| \u2588\u2588\u2588\u2588\u2588\u2588\u2589    138/200 [00:05]\tcompleted  140  /  200 epochs", "", "Epochs completed:  70%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588    141/200 [00:06]", "Epochs completed:  72%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f   144/200 [00:06]", "Epochs completed:  74%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e   147/200 [00:06]", "Epochs completed:  75%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c   150/200 [00:06]", "Epochs completed:  76%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b   153/200 [00:06]", "Epochs completed:  78%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a   156/200 [00:06]", "Epochs completed:  80%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589   159/200 [00:06]\tcompleted  160  /  200 epochs", "", "Epochs completed:  81%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588   162/200 [00:06]", "Epochs completed:  82%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e  165/200 [00:07]", "Epochs completed:  84%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d  168/200 [00:07]", "Epochs completed:  86%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c  171/200 [00:07]", "Epochs completed:  87%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b  174/200 [00:07]", "Epochs completed:  88%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a  177/200 [00:07]", "Epochs completed:  90%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  180/200 [00:07]\tcompleted  180  /  200 epochs", "", "Epochs completed:  92%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f 183/200 [00:07]", "Epochs completed:  93%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e 186/200 [00:07]", "Epochs completed:  94%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d 189/200 [00:07]", "Epochs completed:  96%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c 192/200 [00:08]", "Epochs completed:  98%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a 195/200 [00:08]", "Epochs completed:  99%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589 198/200 [00:08]", "Epochs completed: 100%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 200/200 [00:08]", "Thu Jan 16 23:38:47 2025 Finished embedding", "writing normalized umap umap-001", "wrote /Users/enjalot/latent-scope-demo/google_wellformed_query/umaps/umap-001.parquet", "POINT SIZE 30 for 17500 points", "done with umap-001"], "times": ["2025-01-16 23:38:21.338933", "2025-01-16 23:38:21.339524", "2025-01-16 23:38:24.481328", "2025-01-16 23:38:24.481917", "2025-01-16 23:38:24.538761", "2025-01-16 23:38:24.551858", "2025-01-16 23:38:24.552141", "2025-01-16 23:38:24.552721", "2025-01-16 23:38:24.569294", "2025-01-16 23:38:24.576542", "2025-01-16 23:38:24.579595", "2025-01-16 23:38:26.432128", "2025-01-16 23:38:30.875167", "2025-01-16 23:38:32.783575", "2025-01-16 23:38:34.921911", "2025-01-16 23:38:35.650615", "2025-01-16 23:38:35.853895", "2025-01-16 23:38:35.976208", "2025-01-16 23:38:36.060506", "2025-01-16 23:38:36.095479", "2025-01-16 23:38:37.300005", "2025-01-16 23:38:38.789268", "2025-01-16 23:38:39.185779", "2025-01-16 23:38:39.186106", "2025-01-16 23:38:39.307956", "2025-01-16 23:38:39.417621", "2025-01-16 23:38:39.534625", "2025-01-16 23:38:39.657197", "2025-01-16 23:38:39.780047", "2025-01-16 23:38:39.896408", "2025-01-16 23:38:39.896738", "2025-01-16 23:38:40.027565", "2025-01-16 23:38:40.150468", "2025-01-16 23:38:40.273661", "2025-01-16 23:38:40.391880", "2025-01-16 23:38:40.507630", "2025-01-16 23:38:40.622382", "2025-01-16 23:38:40.709669", "2025-01-16 23:38:40.750752", "2025-01-16 23:38:40.876926", "2025-01-16 23:38:40.994483", "2025-01-16 23:38:41.117598", "2025-01-16 23:38:41.235164", "2025-01-16 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latentscope/jobs/dd53c742-53a9-4e6b-9ffa-781563caed4f.json

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+{"id": "dd53c742-53a9-4e6b-9ffa-781563caed4f", "dataset": "google_wellformed_query", "job_name": "upload-dataset", "command": "ls-upload-dataset \"/Users/enjalot/latent-scope-demo/google_wellformed_query\" \"ls-google_wellformed_query\" --main-parquet=\"scopes/scopes-001-input.parquet\" --private=false", "status": "running", "last_update": "2025-01-16 23:44:20.372061", "progress": ["ARGS Namespace(directory='/Users/enjalot/latent-scope-demo/google_wellformed_query', dataset_name='ls-google_wellformed_query', main_parquet='scopes/scopes-001-input.parquet', private='false', token=None)", "get key /Users/enjalot/code/latent-scope", "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.", "Token is valid (permission: write).", "Your token has been saved to /volumes/T9/huggingface/token", "Login successful", "USERNAME enjalot", "Creating repository: ls-google_wellformed_query", "Repository created/verified: https://huggingface.co/datasets/enjalot/ls-google_wellformed_query", "DIRECTORY /Users/enjalot/latent-scope-demo/google_wellformed_query"], "times": ["2025-01-16 23:44:19.982927", "2025-01-16 23:44:19.983170", "2025-01-16 23:44:19.983331", "2025-01-16 23:44:20.103028", "2025-01-16 23:44:20.104776", "2025-01-16 23:44:20.104894", "2025-01-16 23:44:20.141082", "2025-01-16 23:44:20.141280", "2025-01-16 23:44:20.371864", "2025-01-16 23:44:20.372059"]}

latentscope/meta.json

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+  "columns": [
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+  "potential_embeddings": [],
+  "ls_version": "0.5.1"
+}

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latentscope/scopes/scopes-001-input.parquet

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latentscope/umaps/umap-001.json

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