Datasets:

enjalot
/

ls-dataisplural

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+ {"id": "1e1ff745-20a4-4246-87b7-58bac74eb6a9", "dataset": "dataisplural", "job_name": "embed", "command": "ls-embed \"dataisplural\" \"text\" \"\ud83e\udd17-nomic-ai___nomic-embed-text-v1.5\" --prefix=\"\" --batch_size=100 --max_seq_length=512", "status": "completed", "last_update": "2025-01-16 23:34:04.262866", "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 0x12759f1a0>", "loading nomic-ai/nomic-embed-text-v1.5", "<All keys matched successfully>", "Checking for empty inputs", "embedding 1965 sentences in 19 batches", "", "0%| | 0/19 [00:00<?, ?it/s]", "5%|\u258c | 1/19 [00:01<00:20, 1.16s/it]", "11%|\u2588 | 2/19 [00:02<00:18, 1.06s/it]", "16%|\u2588\u258c | 3/19 [00:03<00:17, 1.06s/it]", "21%|\u2588\u2588 | 4/19 [00:04<00:15, 1.06s/it]", "26%|\u2588\u2588\u258b | 5/19 [00:05<00:13, 1.00it/s]", "32%|\u2588\u2588\u2588\u258f | 6/19 [00:06<00:12, 1.04it/s]", "37%|\u2588\u2588\u2588\u258b | 7/19 [00:07<00:12, 1.00s/it]", "42%|\u2588\u2588\u2588\u2588\u258f | 8/19 [00:08<00:12, 1.10s/it]", "47%|\u2588\u2588\u2588\u2588\u258b | 9/19 [00:09<00:11, 1.12s/it]", "53%|\u2588\u2588\u2588\u2588\u2588\u258e | 10/19 [00:10<00:10, 1.20s/it]", "58%|\u2588\u2588\u2588\u2588\u2588\u258a | 11/19 [00:12<00:09, 1.24s/it]", "63%|\u2588\u2588\u2588\u2588\u2588\u2588\u258e | 12/19 [00:13<00:08, 1.22s/it]", "68%|\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 13/19 [00:14<00:07, 1.21s/it]", "74%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e | 14/19 [00:16<00:06, 1.28s/it]", "79%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589 | 15/19 [00:17<00:05, 1.28s/it]", "84%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 16/19 [00:18<00:03, 1.33s/it]", "89%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589 | 17/19 [00:20<00:02, 1.35s/it]", "95%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d| 18/19 [00:21<00:01, 1.46s/it]", "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 19/19 [00:23<00:00, 1.50s/it]", "20it [00:25, 1.49s/it]", "20it [00:25, 1.25s/it]", "done with embedding-001"], "times": ["2025-01-16 23:33:31.595731", "2025-01-16 23:33:31.619474", "2025-01-16 23:33:31.619752", "2025-01-16 23:33:32.267762", "2025-01-16 23:33:32.268084", "2025-01-16 23:33:38.231461", "2025-01-16 23:33:39.237028", "2025-01-16 23:33:39.237784", "2025-01-16 23:33:39.238116", "2025-01-16 23:33:40.398356", "2025-01-16 23:33:41.387399", "2025-01-16 23:33:42.455021", "2025-01-16 23:33:43.508223", "2025-01-16 23:33:44.393187", "2025-01-16 23:33:45.287014", "2025-01-16 23:33:46.374123", "2025-01-16 23:33:47.687306", "2025-01-16 23:33:48.848498", "2025-01-16 23:33:50.232138", "2025-01-16 23:33:51.555339", "2025-01-16 23:33:52.739439", "2025-01-16 23:33:53.909134", "2025-01-16 23:33:55.354470", "2025-01-16 23:33:56.642538", "2025-01-16 23:33:58.090237", "2025-01-16 23:33:59.499425", "2025-01-16 23:34:01.196201", "2025-01-16 23:34:02.791548", "2025-01-16 23:34:04.261197", "2025-01-16 23:34:04.261493", "2025-01-16 23:34:04.261641", "2025-01-16 23:34:04.262864"], "run_id": "embedding-001"}

latentscope/jobs/31a151ab-cc80-48bd-aa7f-fdd61b57c6c9.json ADDED Viewed

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+ {"id": "31a151ab-cc80-48bd-aa7f-fdd61b57c6c9", "dataset": "dataisplural", "job_name": "cluster", "command": "ls-cluster \"dataisplural\" \"umap-001\" 15 5 0.005", "status": "completed", "last_update": "2025-01-16 23:35:07.159825", "progress": ["Loading environment variables from: /Users/enjalot/code/latent-scope/.env", "RUNNING: cluster-001", "n_clusters: 30", "noise points assigned to clusters: 513", "cluster raw_cluster", "0 7 7", "1 26 26", "2 3 3", "3 25 -1", "4 18 18", "wrote /Users/enjalot/latent-scope-demo/dataisplural/clusters/cluster-001.parquet", "POINT SIZE 30 for 1965 points", "", "0%| | 0/30 [00:00<?, ?it/s]", "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30/30 [00:00<00:00, 5160.74it/s]", "done with cluster-001"], "times": ["2025-01-16 23:35:05.640040", "2025-01-16 23:35:05.640627", "2025-01-16 23:35:06.774819", "2025-01-16 23:35:06.775053", "2025-01-16 23:35:06.777362", "2025-01-16 23:35:06.777458", "2025-01-16 23:35:06.777540", "2025-01-16 23:35:06.777615", "2025-01-16 23:35:06.777812", "2025-01-16 23:35:06.777888", "2025-01-16 23:35:06.778276", "2025-01-16 23:35:06.883110", "2025-01-16 23:35:07.151607", "2025-01-16 23:35:07.157542", "2025-01-16 23:35:07.157796", "2025-01-16 23:35:07.159822"], "run_id": "cluster-001"}

latentscope/jobs/3975f802-895a-4c48-b6f7-490b219584fe.json ADDED Viewed

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+ {"id": "3975f802-895a-4c48-b6f7-490b219584fe", "dataset": "dataisplural", "job_name": "umap", "command": "ls-umap \"dataisplural\" \"embedding-001\" 25 0.1 --seed=42", "status": "completed", "last_update": "2025-01-16 23:34:39.183460", "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 23:34:31 2025 Construct fuzzy simplicial set", "Thu Jan 16 23:34:33 2025 Finding Nearest Neighbors", "Thu Jan 16 23:34:35 2025 Finished Nearest Neighbor Search", "Thu Jan 16 23:34:36 2025 Construct embedding", "", "Epochs completed: 0%| 0/500 [00:00]\tcompleted 0 / 500 epochs", "", "Epochs completed: 0%| 1/500 [00:00]", "Epochs completed: 5%| \u258c 27/500 [00:00]", "Epochs completed: 10%| \u2588 50/500 [00:00]\tcompleted 50 / 500 epochs", "", "Epochs completed: 15%| \u2588\u258d 74/500 [00:00]", "Epochs completed: 19%| \u2588\u2589 97/500 [00:00]\tcompleted 100 / 500 epochs", "", "Epochs completed: 24%| \u2588\u2588\u258d 121/500 [00:00]", "Epochs completed: 29%| \u2588\u2588\u2589 145/500 [00:00]\tcompleted 150 / 500 epochs", "", "Epochs completed: 34%| \u2588\u2588\u2588\u258e 168/500 [00:01]", "Epochs completed: 38%| \u2588\u2588\u2588\u258a 192/500 [00:01]\tcompleted 200 / 500 epochs", "", "Epochs completed: 43%| \u2588\u2588\u2588\u2588\u258e 215/500 [00:01]", "Epochs completed: 48%| \u2588\u2588\u2588\u2588\u258a 238/500 [00:01]\tcompleted 250 / 500 epochs", "", "Epochs completed: 52%| \u2588\u2588\u2588\u2588\u2588\u258f 262/500 [00:01]", "Epochs completed: 57%| \u2588\u2588\u2588\u2588\u2588\u258b 285/500 [00:01]\tcompleted 300 / 500 epochs", "", "Epochs completed: 62%| \u2588\u2588\u2588\u2588\u2588\u2588\u258f 308/500 [00:01]", "Epochs completed: 66%| \u2588\u2588\u2588\u2588\u2588\u2588\u258c 331/500 [00:01]\tcompleted 350 / 500 epochs", "", "Epochs completed: 71%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588 354/500 [00:01]", "Epochs completed: 75%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c 377/500 [00:01]", "Epochs completed: 80%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 400/500 [00:02]\tcompleted 400 / 500 epochs", "", "Epochs completed: 85%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d 424/500 [00:02]", "Epochs completed: 89%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589 447/500 [00:02]\tcompleted 450 / 500 epochs", "", "Epochs completed: 94%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d 470/500 [00:02]", "Epochs completed: 99%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a 493/500 [00:02]", "Epochs completed: 100%| \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 500/500 [00:02]", "Thu Jan 16 23:34:38 2025 Finished embedding", "writing normalized umap umap-001", "wrote /Users/enjalot/latent-scope-demo/dataisplural/umaps/umap-001.parquet", "POINT SIZE 30 for 1965 points", "done with umap-001"], "times": ["2025-01-16 23:34:28.386789", "2025-01-16 23:34:28.387457", "2025-01-16 23:34:31.495410", "2025-01-16 23:34:31.496452", "2025-01-16 23:34:31.496715", "2025-01-16 23:34:31.497219", "2025-01-16 23:34:31.497649", "2025-01-16 23:34:31.498023", "2025-01-16 23:34:31.510124", "2025-01-16 23:34:33.995438", "2025-01-16 23:34:35.130029", "2025-01-16 23:34:36.179440", "2025-01-16 23:34:36.374970", "2025-01-16 23:34:36.665433", "2025-01-16 23:34:36.665750", "2025-01-16 23:34:36.767977", "2025-01-16 23:34:36.869013", "2025-01-16 23:34:36.873484", "2025-01-16 23:34:36.972851", "2025-01-16 23:34:37.074436", "2025-01-16 23:34:37.091373", "2025-01-16 23:34:37.178593", "2025-01-16 23:34:37.282771", "2025-01-16 23:34:37.308907", "2025-01-16 23:34:37.383367", "2025-01-16 23:34:37.487747", "2025-01-16 23:34:37.526649", "2025-01-16 23:34:37.589148", "2025-01-16 23:34:37.690612", "2025-01-16 23:34:37.747284", "2025-01-16 23:34:37.794464", "2025-01-16 23:34:37.894617", "2025-01-16 23:34:37.964866", "2025-01-16 23:34:37.995602", "2025-01-16 23:34:38.096349", "2025-01-16 23:34:38.186120", "2025-01-16 23:34:38.199241", "2025-01-16 23:34:38.299583", "2025-01-16 23:34:38.399763", "2025-01-16 23:34:38.404151", "2025-01-16 23:34:38.503737", "2025-01-16 23:34:38.604533", "2025-01-16 23:34:38.621858", "2025-01-16 23:34:38.717440", "2025-01-16 23:34:38.817775", "2025-01-16 23:34:38.848453", "2025-01-16 23:34:38.848715", "2025-01-16 23:34:38.849473", "2025-01-16 23:34:38.858486", "2025-01-16 23:34:38.875969", "2025-01-16 23:34:39.016237", "2025-01-16 23:34:39.183451"], "run_id": "umap-001"}

latentscope/jobs/4a1cc1c3-9cc2-4beb-a35b-ac432a473b01.json ADDED Viewed

	@@ -0,0 +1 @@

+ {"id": "4a1cc1c3-9cc2-4beb-a35b-ac432a473b01", "dataset": "dataisplural", "job_name": "upload-dataset", "command": "ls-upload-dataset \"/Users/enjalot/latent-scope-demo/dataisplural\" \"ls-dataisplural\" --main-parquet=\"scopes/scopes-001-input.parquet\" --private=false", "status": "running", "last_update": "2025-01-16 23:43:50.510813", "progress": ["ARGS Namespace(directory='/Users/enjalot/latent-scope-demo/dataisplural', dataset_name='ls-dataisplural', 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-dataisplural", "Repository created/verified: https://huggingface.co/datasets/enjalot/ls-dataisplural", "DIRECTORY /Users/enjalot/latent-scope-demo/dataisplural"], "times": ["2025-01-16 23:43:49.979569", "2025-01-16 23:43:49.979924", "2025-01-16 23:43:49.980051", "2025-01-16 23:43:50.072056", "2025-01-16 23:43:50.073886", "2025-01-16 23:43:50.074015", "2025-01-16 23:43:50.123312", "2025-01-16 23:43:50.123628", "2025-01-16 23:43:50.510339", "2025-01-16 23:43:50.510806"]}

latentscope/jobs/666f98fa-af9f-4042-9558-a55ac2218d61.json ADDED Viewed

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+ {"id": "666f98fa-af9f-4042-9558-a55ac2218d61", "dataset": "dataisplural", "job_name": "cluster", "command": "ls-cluster \"dataisplural\" \"umap-001\" 5 5 0.005", "status": "completed", "last_update": "2025-01-16 23:35:21.031436", "progress": ["Loading environment variables from: /Users/enjalot/code/latent-scope/.env", "RUNNING: cluster-001", "n_clusters: 80", "noise points assigned to clusters: 675", "cluster raw_cluster", "0 20 20", "1 69 -1", "2 8 8", "3 57 -1", "4 48 -1", "wrote /Users/enjalot/latent-scope-demo/dataisplural/clusters/cluster-001.parquet", "POINT SIZE 30 for 1965 points", "", "0%| | 0/80 [00:00<?, ?it/s]", "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 80/80 [00:00<00:00, 7784.17it/s]", "done with cluster-001"], "times": ["2025-01-16 23:35:19.629299", "2025-01-16 23:35:19.629815", "2025-01-16 23:35:20.580724", "2025-01-16 23:35:20.580989", "2025-01-16 23:35:20.584017", "2025-01-16 23:35:20.584242", "2025-01-16 23:35:20.584489", "2025-01-16 23:35:20.584706", "2025-01-16 23:35:20.584818", "2025-01-16 23:35:20.584929", "2025-01-16 23:35:20.585042", "2025-01-16 23:35:20.676683", "2025-01-16 23:35:21.018899", "2025-01-16 23:35:21.029275", "2025-01-16 23:35:21.029566", "2025-01-16 23:35:21.031430"], "run_id": "cluster-001"}

latentscope/jobs/7f4a8bae-4e23-4225-bf16-15748a93c688.json ADDED Viewed

	@@ -0,0 +1 @@

+ {"id": "7f4a8bae-4e23-4225-bf16-15748a93c688", "dataset": "dataisplural", "job_name": "label", "command": "ls-label \"dataisplural\" \"text\" \"cluster-001\" \"openai-gpt-4o-mini\" 10 \"\" --max_tokens_per_sample=512 --max_tokens_total=8192", "status": "completed", "last_update": "2025-01-16 23:38:08.879008", "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%| | 0/80 [00:00<?, ?it/s]", "Preparing extracts: 78%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 62/80 [00:00<00:00, 611.59it/s]", "Preparing extracts: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 80/80 [00:00<00:00, 610.24it/s]", "", "0%| | 0/80 [00:00<?, ?it/s]", "", "cluster 0 label: Biological Databases and Analysis", "", "0%| | 0/80 [00:00<?, ?it/s]", "1%|\u258f | 1/80 [00:00<01:12, 1.09it/s]", "", "cluster 1 label: Legal Data Collections", "", "1%|\u258f | 1/80 [00:02<01:12, 1.09it/s]", "2%|\u258e | 2/80 [00:02<01:26, 1.10s/it]", "", "cluster 2 label: Animal Data Collections", "", "2%|\u258e | 2/80 [00:03<01:26, 1.10s/it]", "4%|\u258d | 3/80 [00:03<01:36, 1.25s/it]", "", "cluster 3 label: Biodiversity Databases and Datasets", "", "4%|\u258d | 3/80 [00:04<01:36, 1.25s/it]", "5%|\u258c | 4/80 [00:04<01:36, 1.27s/it]", "", "cluster 4 label: Fish and Wildlife Data", "", "5%|\u258c | 4/80 [00:06<01:36, 1.27s/it]", "6%|\u258b | 5/80 [00:06<01:35, 1.27s/it]", "", "cluster 5 label: Migration and Detention Data", "", "6%|\u258b | 5/80 [00:07<01:35, 1.27s/it]", "8%|\u258a | 6/80 [00:07<01:35, 1.29s/it]", "", "cluster 6 label: Criminal Justice Databases", "", "8%|\u258a | 6/80 [00:08<01:35, 1.29s/it]", "9%|\u2589 | 7/80 [00:08<01:30, 1.24s/it]", "", "cluster 7 label: Incarceration Data and Research", "", "9%|\u2589 | 7/80 [00:09<01:30, 1.24s/it]", "10%|\u2588 | 8/80 [00:09<01:32, 1.29s/it]", "", "cluster 8 label: Police Accountability Data", "", "10%|\u2588 | 8/80 [00:11<01:32, 1.29s/it]", "11%|\u2588\u258f | 9/80 [00:11<01:31, 1.29s/it]", "", "cluster 9 label: Data Analysis and Disclosure", "", "11%|\u2588\u258f | 9/80 [00:12<01:31, 1.29s/it]", "12%|\u2588\u258e | 10/80 [00:12<01:33, 1.33s/it]", "", "cluster 10 label: Violence and Crime Databases", "", "12%|\u2588\u258e | 10/80 [00:13<01:33, 1.33s/it]", "14%|\u2588\u258d | 11/80 [00:13<01:30, 1.32s/it]", "", "cluster 11 label: Protest and Violence Data", "", "14%|\u2588\u258d | 11/80 [00:15<01:30, 1.32s/it]", "15%|\u2588\u258c | 12/80 [00:15<01:28, 1.30s/it]", "", "cluster 12 label: Conflict Data and Analysis", "", "15%|\u2588\u258c | 12/80 [00:16<01:28, 1.30s/it]", "16%|\u2588\u258b | 13/80 [00:16<01:27, 1.31s/it]", "", "cluster 13 label: Diverse Research Datasets", "", "16%|\u2588\u258b | 13/80 [00:17<01:27, 1.31s/it]", "18%|\u2588\u258a | 14/80 [00:17<01:27, 1.32s/it]", "", "cluster 14 label: COVID-19 Policy Tracking Datasets", "", "18%|\u2588\u258a | 14/80 [00:19<01:27, 1.32s/it]", "19%|\u2588\u2589 | 15/80 [00:19<01:26, 1.33s/it]", "", "cluster 15 label: Mortality Data and Analysis", "", "19%|\u2588\u2589 | 15/80 [00:20<01:26, 1.33s/it]", "20%|\u2588\u2588 | 16/80 [00:20<01:26, 1.35s/it]", "", "cluster 16 label: Public Health Datasets", "", "20%|\u2588\u2588 | 16/80 [00:21<01:26, 1.35s/it]", "21%|\u2588\u2588\u258f | 17/80 [00:22<01:24, 1.34s/it]", "", "cluster 17 label: Historical Data Projects", "", "21%|\u2588\u2588\u258f | 17/80 [00:26<01:24, 1.34s/it]", "22%|\u2588\u2588\u258e | 18/80 [00:26<02:14, 2.17s/it]", "", "cluster 18 label: Data on Aid and Rights", "", "22%|\u2588\u2588\u258e | 18/80 [00:27<02:14, 2.17s/it]", "24%|\u2588\u2588\u258d | 19/80 [00:27<01:54, 1.88s/it]", "", "cluster 19 label: Migration and Policy Data", "", "24%|\u2588\u2588\u258d | 19/80 [00:28<01:54, 1.88s/it]", "25%|\u2588\u2588\u258c | 20/80 [00:28<01:41, 1.70s/it]", "", "cluster 20 label: Forest and Land Data", "", "25%|\u2588\u2588\u258c | 20/80 [00:29<01:41, 1.70s/it]", "26%|\u2588\u2588\u258b | 21/80 [00:29<01:33, 1.58s/it]", "", "cluster 21 label: Climate Data and Emissions", "", "26%|\u2588\u2588\u258b | 21/80 [00:31<01:33, 1.58s/it]", "28%|\u2588\u2588\u258a | 22/80 [00:31<01:27, 1.51s/it]", "", "cluster 22 label: Energy Data Resources", "", "28%|\u2588\u2588\u258a | 22/80 [00:32<01:27, 1.51s/it]", "29%|\u2588\u2588\u2589 | 23/80 [00:32<01:18, 1.38s/it]", "", "cluster 23 label: Legislative Data and Transparency", "", "29%|\u2588\u2588\u2589 | 23/80 [00:33<01:18, 1.38s/it]", "30%|\u2588\u2588\u2588 | 24/80 [00:33<01:17, 1.38s/it]", "", "cluster 24 label: Wildfire Data and Monitoring", "", "30%|\u2588\u2588\u2588 | 24/80 [00:34<01:17, 1.38s/it]", "31%|\u2588\u2588\u2588\u258f | 25/80 [00:34<01:13, 1.33s/it]", "", "cluster 25 label: Water Resources Data", "", "31%|\u2588\u2588\u2588\u258f | 25/80 [00:36<01:13, 1.33s/it]", "32%|\u2588\u2588\u2588\u258e | 26/80 [00:36<01:11, 1.33s/it]", "", "cluster 26 label: Environmental Data Collection", "", "32%|\u2588\u2588\u2588\u258e | 26/80 [00:37<01:11, 1.33s/it]", "34%|\u2588\u2588\u2588\u258d | 27/80 [00:37<01:09, 1.31s/it]", "", "cluster 27 label: Government Transparency Datasets", "", "34%|\u2588\u2588\u2588\u258d | 27/80 [00:38<01:09, 1.31s/it]", "35%|\u2588\u2588\u2588\u258c | 28/80 [00:38<01:08, 1.32s/it]", "", "cluster 28 label: Climate and Weather Datasets", "", "35%|\u2588\u2588\u2588\u258c | 28/80 [00:40<01:08, 1.32s/it]", "36%|\u2588\u2588\u2588\u258b | 29/80 [00:40<01:10, 1.38s/it]", "", "cluster 29 label: Disaster and Flood Data", "", "36%|\u2588\u2588\u2588\u258b | 29/80 [00:41<01:10, 1.38s/it]", "38%|\u2588\u2588\u2588\u258a | 30/80 [00:41<01:06, 1.34s/it]", "", "cluster 30 label: Political Dataset Collections", "", "38%|\u2588\u2588\u2588\u258a | 30/80 [00:43<01:06, 1.34s/it]", "39%|\u2588\u2588\u2588\u2589 | 31/80 [00:43<01:06, 1.36s/it]", "", "cluster 31 label: Political Data Datasets", "", "39%|\u2588\u2588\u2588\u2589 | 31/80 [00:44<01:06, 1.36s/it]", "40%|\u2588\u2588\u2588\u2588 | 32/80 [00:44<01:03, 1.33s/it]", "", "cluster 32 label: Geospatial Data and Monitoring", "", "40%|\u2588\u2588\u2588\u2588 | 32/80 [00:45<01:03, 1.33s/it]", "41%|\u2588\u2588\u2588\u2588\u258f | 33/80 [00:45<01:02, 1.32s/it]", "", "cluster 33 label: Space Exploration Datasets", "", "41%|\u2588\u2588\u2588\u2588\u258f | 33/80 [00:46<01:02, 1.32s/it]", "42%|\u2588\u2588\u2588\u2588\u258e | 34/80 [00:46<01:01, 1.34s/it]", "", "cluster 34 label: Geospatial and Environmental Data", "", "42%|\u2588\u2588\u2588\u2588\u258e | 34/80 [00:48<01:01, 1.34s/it]", "44%|\u2588\u2588\u2588\u2588\u258d | 35/80 [00:48<01:00, 1.34s/it]", "", "cluster 35 label: Consumer Safety Reports", "", "44%|\u2588\u2588\u2588\u2588\u258d | 35/80 [00:50<01:00, 1.34s/it]", "45%|\u2588\u2588\u2588\u2588\u258c | 36/80 [00:50<01:04, 1.47s/it]", "", "cluster 36 label: Agricultural Data and Analysis", "", "45%|\u2588\u2588\u2588\u2588\u258c | 36/80 [01:02<01:04, 1.47s/it]", "46%|\u2588\u2588\u2588\u2588\u258b | 37/80 [01:02<03:27, 4.82s/it]", "", "cluster 37 label: Geospatial Datasets and Analysis", "", "46%|\u2588\u2588\u2588\u2588\u258b | 37/80 [01:03<03:27, 4.82s/it]", "48%|\u2588\u2588\u2588\u2588\u258a | 38/80 [01:03<02:38, 3.77s/it]", "", "cluster 38 label: Aviation Safety Data", "", "48%|\u2588\u2588\u2588\u2588\u258a | 38/80 [01:05<02:38, 3.77s/it]", "49%|\u2588\u2588\u2588\u2588\u2589 | 39/80 [01:05<02:04, 3.03s/it]", "", "cluster 39 label: Aviation Data and Regulations", "", "49%|\u2588\u2588\u2588\u2588\u2589 | 39/80 [01:06<02:04, 3.03s/it]", "50%|\u2588\u2588\u2588\u2588\u2588 | 40/80 [01:06<01:39, 2.49s/it]", "", "cluster 40 label: Media Franchise APIs", "", "50%|\u2588\u2588\u2588\u2588\u2588 | 40/80 [01:07<01:39, 2.49s/it]", "51%|\u2588\u2588\u2588\u2588\u2588\u258f | 41/80 [01:07<01:23, 2.13s/it]", "", "cluster 41 label: Datasets on Social Issues", "", "51%|\u2588\u2588\u2588\u2588\u2588\u258f | 41/80 [01:09<01:23, 2.13s/it]", "52%|\u2588\u2588\u2588\u2588\u2588\u258e | 42/80 [01:09<01:11, 1.87s/it]", "", "cluster 42 label: Sports Data Collections", "", "52%|\u2588\u2588\u2588\u2588\u2588\u258e | 42/80 [01:10<01:11, 1.87s/it]", "54%|\u2588\u2588\u2588\u2588\u2588\u258d | 43/80 [01:10<01:01, 1.65s/it]", "", "cluster 43 label: Geolocation and Dataset Projects", "", "54%|\u2588\u2588\u2588\u2588\u2588\u258d | 43/80 [01:11<01:01, 1.65s/it]", "55%|\u2588\u2588\u2588\u2588\u2588\u258c | 44/80 [01:11<00:57, 1.58s/it]", "", "cluster 44 label: Diverse Research Databases", "", "55%|\u2588\u2588\u2588\u2588\u2588\u258c | 44/80 [01:13<00:57, 1.58s/it]", "56%|\u2588\u2588\u2588\u2588\u2588\u258b | 45/80 [01:13<01:01, 1.75s/it]", "", "cluster 45 label: Healthcare Data and Transparency", "", "56%|\u2588\u2588\u2588\u2588\u2588\u258b | 45/80 [01:15<01:01, 1.75s/it]", "57%|\u2588\u2588\u2588\u2588\u2588\u258a | 46/80 [01:15<00:54, 1.60s/it]", "", "cluster 46 label: Digital Historical Data", "", "57%|\u2588\u2588\u2588\u2588\u2588\u258a | 46/80 [01:16<00:54, 1.60s/it]", "59%|\u2588\u2588\u2588\u2588\u2588\u2589 | 47/80 [01:16<00:48, 1.48s/it]", "", "cluster 47 label: Historical Data Datasets", "", "59%|\u2588\u2588\u2588\u2588\u2588\u2589 | 47/80 [01:17<00:48, 1.48s/it]", "60%|\u2588\u2588\u2588\u2588\u2588\u2588 | 48/80 [01:17<00:44, 1.40s/it]", "", "cluster 48 label: New York City Housing Data", "", "60%|\u2588\u2588\u2588\u2588\u2588\u2588 | 48/80 [01:18<00:44, 1.40s/it]", "61%|\u2588\u2588\u2588\u2588\u2588\u2588\u258f | 49/80 [01:18<00:43, 1.40s/it]", "", "cluster 49 label: Language Data and Research", "", "61%|\u2588\u2588\u2588\u2588\u2588\u2588\u258f | 49/80 [01:20<00:43, 1.40s/it]", "62%|\u2588\u2588\u2588\u2588\u2588\u2588\u258e | 50/80 [01:20<00:41, 1.38s/it]", "", "cluster 50 label: Urban Transit Data", "", "62%|\u2588\u2588\u2588\u2588\u2588\u2588\u258e | 50/80 [01:23<00:41, 1.38s/it]", "64%|\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 51/80 [01:23<00:57, 1.99s/it]", "", "cluster 51 label: Surveillance and Mapping Data", "", "64%|\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 51/80 [01:24<00:57, 1.99s/it]", "65%|\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 52/80 [01:24<00:50, 1.79s/it]", "", "cluster 52 label: Urban Infrastructure Datasets", "", "65%|\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 52/80 [01:26<00:50, 1.79s/it]", "66%|\u2588\u2588\u2588\u2588\u2588\u2588\u258b | 53/80 [01:26<00:43, 1.60s/it]", "", "cluster 53 label: Housing Market Data", "", "66%|\u2588\u2588\u2588\u2588\u2588\u2588\u258b | 53/80 [01:27<00:43, 1.60s/it]", "68%|\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 54/80 [01:27<00:39, 1.52s/it]", "", "cluster 54 label: Housing Price Data Analysis", "", "68%|\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 54/80 [01:28<00:39, 1.52s/it]", "69%|\u2588\u2588\u2588\u2588\u2588\u2588\u2589 | 55/80 [01:28<00:37, 1.49s/it]", "", "cluster 55 label: Open Data Initiatives", "", "69%|\u2588\u2588\u2588\u2588\u2588\u2588\u2589 | 55/80 [01:30<00:37, 1.49s/it]", "70%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 56/80 [01:30<00:34, 1.45s/it]", "", "cluster 56 label: Data Platforms on Corporations", "", "70%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 56/80 [01:31<00:34, 1.45s/it]", "71%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f | 57/80 [01:31<00:32, 1.41s/it]", "", "cluster 57 label: Historical News Datasets", "", "71%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f | 57/80 [01:32<00:32, 1.41s/it]", "72%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e | 58/80 [01:32<00:29, 1.36s/it]", "", "cluster 58 label: Political Data and Analysis", "", "72%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e | 58/80 [01:34<00:29, 1.36s/it]", "74%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 59/80 [01:34<00:28, 1.38s/it]", "", "cluster 59 label: Economic Statistics Reports", "", "74%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 59/80 [01:35<00:28, 1.38s/it]", "75%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 60/80 [01:35<00:26, 1.30s/it]", "", "cluster 60 label: International Economic Databases", "", "75%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 60/80 [01:36<00:26, 1.30s/it]", "76%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b | 61/80 [01:36<00:25, 1.33s/it]", "", "cluster 61 label: Government Financial Datasets", "", "76%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b | 61/80 [01:37<00:25, 1.33s/it]", "78%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 62/80 [01:37<00:23, 1.30s/it]", "", "cluster 62 label: Government Financial Data", "", "78%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 62/80 [01:39<00:23, 1.30s/it]", "79%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589 | 63/80 [01:39<00:21, 1.26s/it]", "", "cluster 63 label: Economic and Demographic Studies", "", "79%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589 | 63/80 [01:40<00:21, 1.26s/it]", "80%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 64/80 [01:40<00:19, 1.22s/it]", "", "cluster 64 label: Diverse Research Datasets", "", "80%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 64/80 [01:41<00:19, 1.22s/it]", "81%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f | 65/80 [01:41<00:18, 1.26s/it]", "", "cluster 65 label: Labor and Employment Surveys", "", "81%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f | 65/80 [01:42<00:18, 1.26s/it]", "82%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e | 66/80 [01:42<00:17, 1.26s/it]", "", "cluster 66 label: Occupational and Workforce Data", "", "82%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e | 66/80 [01:44<00:17, 1.26s/it]", "84%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 67/80 [01:44<00:16, 1.30s/it]", "", "cluster 67 label: Film Data and Analysis", "", "84%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 67/80 [01:45<00:16, 1.30s/it]", "85%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 68/80 [01:45<00:15, 1.31s/it]", "", "cluster 68 label: Education Data and Statistics", "", "85%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 68/80 [01:46<00:15, 1.31s/it]", "86%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b | 69/80 [01:46<00:13, 1.25s/it]", "", "cluster 69 label: Education Data and Analysis", "", "86%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b | 69/80 [01:47<00:13, 1.25s/it]", "88%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 70/80 [01:47<00:12, 1.25s/it]", "", "cluster 70 label: Research Data and Datasets", "", "88%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 70/80 [01:49<00:12, 1.25s/it]", "89%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589 | 71/80 [01:49<00:11, 1.26s/it]", "", "cluster 71 label: Datasets and Corpora", "", "89%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589 | 71/80 [01:50<00:11, 1.26s/it]", "90%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 72/80 [01:50<00:10, 1.34s/it]", "", "cluster 72 label: Data-Driven Puzzles Analysis", "", "90%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 72/80 [01:51<00:10, 1.34s/it]", "91%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f| 73/80 [01:51<00:09, 1.31s/it]", "", "cluster 73 label: Sports Data Compilation", "", "91%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f| 73/80 [01:53<00:09, 1.31s/it]", "92%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e| 74/80 [01:53<00:07, 1.33s/it]", "", "cluster 74 label: Cybersecurity Datasets and Vulnerabilities", "", "92%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e| 74/80 [01:54<00:07, 1.33s/it]", "94%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d| 75/80 [01:54<00:06, 1.33s/it]", "", "cluster 75 label: Open Research Datasets", "", "94%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d| 75/80 [01:56<00:06, 1.33s/it]", "95%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c| 76/80 [01:56<00:05, 1.34s/it]", "", "cluster 76 label: Data Collections and Analyses", "", "95%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c| 76/80 [01:57<00:05, 1.34s/it]", "96%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b| 77/80 [01:57<00:03, 1.33s/it]", "", "cluster 77 label: Diverse Data Collections", "", "96%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b| 77/80 [01:58<00:03, 1.33s/it]", "98%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a| 78/80 [01:58<00:02, 1.30s/it]", "", "cluster 78 label: Music and Performance Databases", "", "98%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a| 78/80 [02:00<00:02, 1.30s/it]", "99%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589| 79/80 [02:00<00:01, 1.37s/it]", "", "cluster 79 label: Open Data Art Projects", "", "99%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589| 79/80 [02:01<00:01, 1.37s/it]", "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 80/80 [02:01<00:00, 1.41s/it]", "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 80/80 [02:01<00:00, 1.52s/it]", "labels: 80", "done with cluster-001-labels-001"], "times": ["2025-01-16 23:36:04.382981", "2025-01-16 23:36:04.417002", "2025-01-16 23:36:04.417294", "2025-01-16 23:36:06.218086", "2025-01-16 23:36:07.120043", "2025-01-16 23:36:07.120389", "2025-01-16 23:36:07.221906", "2025-01-16 23:36:07.251610", "2025-01-16 23:36:07.251979", "2025-01-16 23:36:07.252173", "2025-01-16 23:36:08.169690", "2025-01-16 23:36:08.169927", "2025-01-16 23:36:08.170059", "2025-01-16 23:36:08.170174", "2025-01-16 23:36:08.172595", "2025-01-16 23:36:09.400625", "2025-01-16 23:36:09.400876", "2025-01-16 23:36:09.401016", "2025-01-16 23:36:09.401123", "2025-01-16 23:36:09.402373", "2025-01-16 23:36:10.832175", 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latentscope/jobs/8aeb7d86-bb5c-4f77-9e67-a3b293bf6b32.json ADDED Viewed

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+ {"id": "8aeb7d86-bb5c-4f77-9e67-a3b293bf6b32", "dataset": "dataisplural", "job_name": "sae", "command": "ls-sae \"dataisplural\" \"embedding-001\" \"enjalot/sae-nomic-text-v1.5-FineWeb-edu-100BT\" 64_32", "status": "completed", "last_update": "2025-01-16 23:34:22.671251", "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: 50%|\u2588\u2588\u2588\u2588\u2588 | 1/2 [00:00<00:00, 6.23it/s]", "Fetching 2 files: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2/2 [00:04<00:00, 2.45s/it]", "Fetching 2 files: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2/2 [00:04<00:00, 2.11s/it]", "Dropping extra args {'signed': False}", "Encoding embeddings with SAE", "", "Encoding batches: 0%| | 0/16 [00:00<?, ?it/s]", "Encoding batches: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 16/16 [00:00<00:00, 369.54it/s]", "encoding completed", "saving to disk", "calculating summary statistics", "ALL ACTS SHAPE (1965, 64)", "ALL INDS SHAPE (1965, 64)", "MATRIX (1965, 24576)", "MAX ACTIVATIONS (24576,)", "MAX ACTIVATIONS [0.03984097 0. 0. ... 0. 0. 0. ]", "Number of dead features: 14761", "done with sae-001"], "times": ["2025-01-16 23:34:15.897895", "2025-01-16 23:34:15.898305", "2025-01-16 23:34:17.573590", "2025-01-16 23:34:17.882882", "2025-01-16 23:34:17.997493", "2025-01-16 23:34:18.158231", "2025-01-16 23:34:22.212038", "2025-01-16 23:34:22.212480", "2025-01-16 23:34:22.212746", "2025-01-16 23:34:22.212990", "2025-01-16 23:34:22.402452", "2025-01-16 23:34:22.403082", "2025-01-16 23:34:22.446322", "2025-01-16 23:34:22.446849", "2025-01-16 23:34:22.646085", "2025-01-16 23:34:22.646360", "2025-01-16 23:34:22.647901", "2025-01-16 23:34:22.648128", "2025-01-16 23:34:22.649294", "2025-01-16 23:34:22.653020", "2025-01-16 23:34:22.655906", "2025-01-16 23:34:22.656533", "2025-01-16 23:34:22.656683", "2025-01-16 23:34:22.671243"], "run_id": "sae-001"}

latentscope/jobs/9739f980-f592-4def-88ab-8b888b40811e.json ADDED Viewed

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+ {"id": "9739f980-f592-4def-88ab-8b888b40811e", "dataset": "dataisplural", "job_name": "scope", "command": "ls-scope \"dataisplural\" \"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 23:38:33.296805", "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 23:38:32.908847", "2025-01-16 23:38:32.909331", "2025-01-16 23:38:32.909502", "2025-01-16 23:38:33.239288", "2025-01-16 23:38:33.268764", "2025-01-16 23:38:33.269058", "2025-01-16 23:38:33.269212", "2025-01-16 23:38:33.269417", "2025-01-16 23:38:33.269532", "2025-01-16 23:38:33.296794"], "run_id": "scopes-001"}

latentscope/jobs/9d76d811-4a6e-49fa-982e-434fd4626d9a.json ADDED Viewed

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1	+ {"id": "9d76d811-4a6e-49fa-982e-434fd4626d9a", "dataset": "dataisplural", "job_name": "rm", "command": "rm -rf /Users/enjalot/latent-scope-demo/dataisplural/clusters/cluster-001*", "status": "completed", "last_update": "2025-01-16 23:35:15.409293", "progress": [], "times": []}

latentscope/jobs/feb01c0b-c3e4-46ef-85a0-86f13cdb9a20.json ADDED Viewed

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+ {"id": "feb01c0b-c3e4-46ef-85a0-86f13cdb9a20", "dataset": "dataisplural", "job_name": "ingest", "command": "ls-ingest \"dataisplural\" --path=\"/Users/enjalot/latent-scope-demo/dataisplural/dataisplural.csv\"", "status": "completed", "last_update": "2025-01-16 23:33:14.330648", "progress": ["Loading environment variables from: /Users/enjalot/code/latent-scope/.env", "RUNNING: dataisplural", "File type detected: csv", "reading /Users/enjalot/latent-scope-demo/dataisplural/dataisplural.csv", "Loading environment variables from: /Users/enjalot/code/latent-scope/.env", "DATA DIR /Users/enjalot/latent-scope-demo", "DIRECTORY /Users/enjalot/latent-scope-demo/dataisplural", "edition ... hattips", "0 2015.10.21 ... https://twitter.com/emilymbadger/status/653982...", "1 2015.10.21 ... NaN", "2 2015.10.21 ... NaN", "3 2015.10.21 ... NaN", "4 2015.10.21 ... https://twitter.com/veltman/status/62897277788...", "", "[5 rows x 6 columns]", "edition ... hattips", "1960 2025.01.08 ... https://www.linkedin.com/pulse/new-local-journ...", "1961 2025.01.08 ... https://www.linkedin.com/posts/aneri-pattani-4...", "1962 2025.01.08 ... NaN", "1963 2025.01.08 ... https://melaniewalsh.org/\\nhttps://thescoop.org/", "1964 2025.01.08 ... https://news.ycombinator.com/item?id=42505454\\...", "", "[5 rows x 6 columns]", "Index(['edition', 'position', 'headline', 'text', 'links', 'hattips'], dtype='object')", "checking column types", "COLUMN edition TYPE string", "COLUMN position TYPE number", "COLUMN headline TYPE string", "COLUMN text TYPE string", "COLUMN links TYPE string", "COLUMN hattips TYPE string", "wrote /Users/enjalot/latent-scope-demo/dataisplural/input.parquet"], "times": ["2025-01-16 23:33:14.281882", "2025-01-16 23:33:14.282556", "2025-01-16 23:33:14.282888", "2025-01-16 23:33:14.283121", "2025-01-16 23:33:14.300165", "2025-01-16 23:33:14.300645", "2025-01-16 23:33:14.300763", "2025-01-16 23:33:14.303502", "2025-01-16 23:33:14.303598", "2025-01-16 23:33:14.303679", "2025-01-16 23:33:14.303755", "2025-01-16 23:33:14.303829", "2025-01-16 23:33:14.303903", "2025-01-16 23:33:14.303985", "2025-01-16 23:33:14.304065", "2025-01-16 23:33:14.305171", "2025-01-16 23:33:14.305249", "2025-01-16 23:33:14.305332", "2025-01-16 23:33:14.305409", "2025-01-16 23:33:14.305484", "2025-01-16 23:33:14.305558", "2025-01-16 23:33:14.305637", "2025-01-16 23:33:14.305794", "2025-01-16 23:33:14.305878", "2025-01-16 23:33:14.305956", "2025-01-16 23:33:14.306042", "2025-01-16 23:33:14.306125", "2025-01-16 23:33:14.306214", "2025-01-16 23:33:14.306863", "2025-01-16 23:33:14.310683", "2025-01-16 23:33:14.312428", "2025-01-16 23:33:14.330637"], "run_id": "dataisplural"}

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