| [ | |
| { | |
| "title": "Data imputation", | |
| "header": [ | |
| { | |
| "value": "Model", | |
| "markdown": false, | |
| "metadata": {} | |
| }, | |
| { | |
| "value": "EM", | |
| "description": "Scenario from [Mei et al. (2021)](https://ieeexplore.ieee.org/document/9458712/) that tests the ability to impute missing entities in a data table.\n\nQuasi-exact match: Fraction of instances that the predicted output matches a correct reference up to light processing.", | |
| "markdown": false, | |
| "lower_is_better": false, | |
| "metadata": { | |
| "metric": "EM", | |
| "run_group": "Data imputation" | |
| } | |
| }, | |
| { | |
| "value": "Denoised inference time (s)", | |
| "description": "Scenario from [Mei et al. (2021)](https://ieeexplore.ieee.org/document/9458712/) that tests the ability to impute missing entities in a data table.\n\nDenoised inference runtime (s): Average time to process a request to the model minus performance contention by using profiled runtimes from multiple trials of SyntheticEfficiencyScenario.", | |
| "markdown": false, | |
| "lower_is_better": true, | |
| "metadata": { | |
| "metric": "Denoised inference time (s)", | |
| "run_group": "Data imputation" | |
| } | |
| }, | |
| { | |
| "value": "# eval", | |
| "description": "Scenario from [Mei et al. (2021)](https://ieeexplore.ieee.org/document/9458712/) that tests the ability to impute missing entities in a data table.\n\n# eval: Number of evaluation instances.", | |
| "markdown": false, | |
| "metadata": { | |
| "metric": "# eval", | |
| "run_group": "Data imputation" | |
| } | |
| }, | |
| { | |
| "value": "# train", | |
| "description": "Scenario from [Mei et al. (2021)](https://ieeexplore.ieee.org/document/9458712/) that tests the ability to impute missing entities in a data table.\n\n# train: Number of training instances (e.g., in-context examples).", | |
| "markdown": false, | |
| "metadata": { | |
| "metric": "# train", | |
| "run_group": "Data imputation" | |
| } | |
| }, | |
| { | |
| "value": "truncated", | |
| "description": "Scenario from [Mei et al. (2021)](https://ieeexplore.ieee.org/document/9458712/) that tests the ability to impute missing entities in a data table.\n\ntruncated: Fraction of instances where the prompt itself was truncated (implies that there were no in-context examples).", | |
| "markdown": false, | |
| "metadata": { | |
| "metric": "truncated", | |
| "run_group": "Data imputation" | |
| } | |
| }, | |
| { | |
| "value": "# prompt tokens", | |
| "description": "Scenario from [Mei et al. (2021)](https://ieeexplore.ieee.org/document/9458712/) that tests the ability to impute missing entities in a data table.\n\n# prompt tokens: Number of tokens in the prompt.", | |
| "markdown": false, | |
| "metadata": { | |
| "metric": "# prompt tokens", | |
| "run_group": "Data imputation" | |
| } | |
| }, | |
| { | |
| "value": "# output tokens", | |
| "description": "Scenario from [Mei et al. (2021)](https://ieeexplore.ieee.org/document/9458712/) that tests the ability to impute missing entities in a data table.\n\n# output tokens: Actual number of output tokens.", | |
| "markdown": false, | |
| "metadata": { | |
| "metric": "# output tokens", | |
| "run_group": "Data imputation" | |
| } | |
| }, | |
| { | |
| "value": "# trials", | |
| "description": "Scenario from [Mei et al. (2021)](https://ieeexplore.ieee.org/document/9458712/) that tests the ability to impute missing entities in a data table.\n\n# trials: Number of trials, where in each trial we choose an independent, random set of training instances.", | |
| "markdown": false, | |
| "metadata": { | |
| "metric": "# trials", | |
| "run_group": "Data imputation" | |
| } | |
| } | |
| ], | |
| "rows": [ | |
| [ | |
| { | |
| "value": "EleutherAI/pythia-2.8b", | |
| "description": "", | |
| "markdown": false | |
| }, | |
| { | |
| "value": 0.33282647584973163, | |
| "description": "min=0.035, mean=0.333, max=0.631, sum=0.666 (2)", | |
| "style": { | |
| "font-weight": "bold" | |
| }, | |
| "markdown": false, | |
| "run_spec_names": [ | |
| "entity_data_imputation:dataset=Buy,model=EleutherAI_pythia-2.8b", | |
| "entity_data_imputation:dataset=Restaurant,model=EleutherAI_pythia-2.8b" | |
| ] | |
| }, | |
| { | |
| "description": "2 matching runs, but no matching metrics", | |
| "markdown": false | |
| }, | |
| { | |
| "value": 75.5, | |
| "description": "min=65, mean=75.5, max=86, sum=151 (2)", | |
| "style": {}, | |
| "markdown": false, | |
| "run_spec_names": [ | |
| "entity_data_imputation:dataset=Buy,model=EleutherAI_pythia-2.8b", | |
| "entity_data_imputation:dataset=Restaurant,model=EleutherAI_pythia-2.8b" | |
| ] | |
| }, | |
| { | |
| "value": 5.0, | |
| "description": "min=5, mean=5, max=5, sum=10 (2)", | |
| "style": {}, | |
| "markdown": false, | |
| "run_spec_names": [ | |
| "entity_data_imputation:dataset=Buy,model=EleutherAI_pythia-2.8b", | |
| "entity_data_imputation:dataset=Restaurant,model=EleutherAI_pythia-2.8b" | |
| ] | |
| }, | |
| { | |
| "value": 0.0, | |
| "description": "min=0, mean=0, max=0, sum=0 (2)", | |
| "style": {}, | |
| "markdown": false, | |
| "run_spec_names": [ | |
| "entity_data_imputation:dataset=Buy,model=EleutherAI_pythia-2.8b", | |
| "entity_data_imputation:dataset=Restaurant,model=EleutherAI_pythia-2.8b" | |
| ] | |
| }, | |
| { | |
| "value": 291.5386404293381, | |
| "description": "min=256.047, mean=291.539, max=327.031, sum=583.077 (2)", | |
| "style": {}, | |
| "markdown": false, | |
| "run_spec_names": [ | |
| "entity_data_imputation:dataset=Buy,model=EleutherAI_pythia-2.8b", | |
| "entity_data_imputation:dataset=Restaurant,model=EleutherAI_pythia-2.8b" | |
| ] | |
| }, | |
| { | |
| "value": 3.2625223613595704, | |
| "description": "min=2.769, mean=3.263, max=3.756, sum=6.525 (2)", | |
| "style": {}, | |
| "markdown": false, | |
| "run_spec_names": [ | |
| "entity_data_imputation:dataset=Buy,model=EleutherAI_pythia-2.8b", | |
| "entity_data_imputation:dataset=Restaurant,model=EleutherAI_pythia-2.8b" | |
| ] | |
| }, | |
| { | |
| "value": 1.0, | |
| "description": "min=1, mean=1, max=1, sum=2 (2)", | |
| "style": {}, | |
| "markdown": false, | |
| "run_spec_names": [ | |
| "entity_data_imputation:dataset=Buy,model=EleutherAI_pythia-2.8b", | |
| "entity_data_imputation:dataset=Restaurant,model=EleutherAI_pythia-2.8b" | |
| ] | |
| } | |
| ] | |
| ], | |
| "links": [ | |
| { | |
| "text": "LaTeX", | |
| "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/entity_data_imputation_entity_data_imputation.tex" | |
| }, | |
| { | |
| "text": "JSON", | |
| "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/entity_data_imputation_entity_data_imputation.json" | |
| } | |
| ], | |
| "name": "entity_data_imputation" | |
| }, | |
| { | |
| "title": "dataset: Buy", | |
| "header": [ | |
| { | |
| "value": "Model", | |
| "markdown": false, | |
| "metadata": {} | |
| }, | |
| { | |
| "value": "EM", | |
| "description": "Scenario from [Mei et al. (2021)](https://ieeexplore.ieee.org/document/9458712/) that tests the ability to impute missing entities in a data table.\n\nQuasi-exact match: Fraction of instances that the predicted output matches a correct reference up to light processing.", | |
| "markdown": false, | |
| "lower_is_better": false, | |
| "metadata": { | |
| "metric": "EM", | |
| "run_group": "Data imputation" | |
| } | |
| }, | |
| { | |
| "value": "Denoised inference time (s)", | |
| "description": "Scenario from [Mei et al. (2021)](https://ieeexplore.ieee.org/document/9458712/) that tests the ability to impute missing entities in a data table.\n\nDenoised inference runtime (s): Average time to process a request to the model minus performance contention by using profiled runtimes from multiple trials of SyntheticEfficiencyScenario.", | |
| "markdown": false, | |
| "lower_is_better": true, | |
| "metadata": { | |
| "metric": "Denoised inference time (s)", | |
| "run_group": "Data imputation" | |
| } | |
| }, | |
| { | |
| "value": "# eval", | |
| "description": "Scenario from [Mei et al. (2021)](https://ieeexplore.ieee.org/document/9458712/) that tests the ability to impute missing entities in a data table.\n\n# eval: Number of evaluation instances.", | |
| "markdown": false, | |
| "metadata": { | |
| "metric": "# eval", | |
| "run_group": "Data imputation" | |
| } | |
| }, | |
| { | |
| "value": "# train", | |
| "description": "Scenario from [Mei et al. (2021)](https://ieeexplore.ieee.org/document/9458712/) that tests the ability to impute missing entities in a data table.\n\n# train: Number of training instances (e.g., in-context examples).", | |
| "markdown": false, | |
| "metadata": { | |
| "metric": "# train", | |
| "run_group": "Data imputation" | |
| } | |
| }, | |
| { | |
| "value": "truncated", | |
| "description": "Scenario from [Mei et al. (2021)](https://ieeexplore.ieee.org/document/9458712/) that tests the ability to impute missing entities in a data table.\n\ntruncated: Fraction of instances where the prompt itself was truncated (implies that there were no in-context examples).", | |
| "markdown": false, | |
| "metadata": { | |
| "metric": "truncated", | |
| "run_group": "Data imputation" | |
| } | |
| }, | |
| { | |
| "value": "# prompt tokens", | |
| "description": "Scenario from [Mei et al. (2021)](https://ieeexplore.ieee.org/document/9458712/) that tests the ability to impute missing entities in a data table.\n\n# prompt tokens: Number of tokens in the prompt.", | |
| "markdown": false, | |
| "metadata": { | |
| "metric": "# prompt tokens", | |
| "run_group": "Data imputation" | |
| } | |
| }, | |
| { | |
| "value": "# output tokens", | |
| "description": "Scenario from [Mei et al. (2021)](https://ieeexplore.ieee.org/document/9458712/) that tests the ability to impute missing entities in a data table.\n\n# output tokens: Actual number of output tokens.", | |
| "markdown": false, | |
| "metadata": { | |
| "metric": "# output tokens", | |
| "run_group": "Data imputation" | |
| } | |
| }, | |
| { | |
| "value": "# trials", | |
| "description": "Scenario from [Mei et al. (2021)](https://ieeexplore.ieee.org/document/9458712/) that tests the ability to impute missing entities in a data table.\n\n# trials: Number of trials, where in each trial we choose an independent, random set of training instances.", | |
| "markdown": false, | |
| "metadata": { | |
| "metric": "# trials", | |
| "run_group": "Data imputation" | |
| } | |
| } | |
| ], | |
| "rows": [ | |
| [ | |
| { | |
| "value": "EleutherAI/pythia-2.8b", | |
| "description": "", | |
| "href": "?group=entity_data_imputation&subgroup=dataset%3A%20Buy&runSpecs=%5B%22entity_data_imputation%3Adataset%3DBuy%2Cmodel%3DEleutherAI_pythia-2.8b%22%5D", | |
| "markdown": false, | |
| "run_spec_names": [ | |
| "entity_data_imputation:dataset=Buy,model=EleutherAI_pythia-2.8b" | |
| ] | |
| }, | |
| { | |
| "value": 0.6307692307692307, | |
| "description": "min=0.631, mean=0.631, max=0.631, sum=0.631 (1)", | |
| "style": { | |
| "font-weight": "bold" | |
| }, | |
| "markdown": false | |
| }, | |
| { | |
| "description": "1 matching runs, but no matching metrics", | |
| "markdown": false | |
| }, | |
| { | |
| "value": 65.0, | |
| "description": "min=65, mean=65, max=65, sum=65 (1)", | |
| "style": {}, | |
| "markdown": false | |
| }, | |
| { | |
| "value": 5.0, | |
| "description": "min=5, mean=5, max=5, sum=5 (1)", | |
| "style": {}, | |
| "markdown": false | |
| }, | |
| { | |
| "value": 0.0, | |
| "description": "min=0, mean=0, max=0, sum=0 (1)", | |
| "style": {}, | |
| "markdown": false | |
| }, | |
| { | |
| "value": 327.03076923076924, | |
| "description": "min=327.031, mean=327.031, max=327.031, sum=327.031 (1)", | |
| "style": {}, | |
| "markdown": false | |
| }, | |
| { | |
| "value": 2.769230769230769, | |
| "description": "min=2.769, mean=2.769, max=2.769, sum=2.769 (1)", | |
| "style": {}, | |
| "markdown": false | |
| }, | |
| { | |
| "value": 1.0, | |
| "description": "min=1, mean=1, max=1, sum=1 (1)", | |
| "style": {}, | |
| "markdown": false | |
| } | |
| ] | |
| ], | |
| "links": [ | |
| { | |
| "text": "LaTeX", | |
| "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/entity_data_imputation_entity_data_imputation_dataset:Buy.tex" | |
| }, | |
| { | |
| "text": "JSON", | |
| "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/entity_data_imputation_entity_data_imputation_dataset:Buy.json" | |
| } | |
| ], | |
| "name": "entity_data_imputation_dataset:Buy" | |
| }, | |
| { | |
| "title": "dataset: Restaurant", | |
| "header": [ | |
| { | |
| "value": "Model", | |
| "markdown": false, | |
| "metadata": {} | |
| }, | |
| { | |
| "value": "EM", | |
| "description": "Scenario from [Mei et al. (2021)](https://ieeexplore.ieee.org/document/9458712/) that tests the ability to impute missing entities in a data table.\n\nQuasi-exact match: Fraction of instances that the predicted output matches a correct reference up to light processing.", | |
| "markdown": false, | |
| "lower_is_better": false, | |
| "metadata": { | |
| "metric": "EM", | |
| "run_group": "Data imputation" | |
| } | |
| }, | |
| { | |
| "value": "Denoised inference time (s)", | |
| "description": "Scenario from [Mei et al. (2021)](https://ieeexplore.ieee.org/document/9458712/) that tests the ability to impute missing entities in a data table.\n\nDenoised inference runtime (s): Average time to process a request to the model minus performance contention by using profiled runtimes from multiple trials of SyntheticEfficiencyScenario.", | |
| "markdown": false, | |
| "lower_is_better": true, | |
| "metadata": { | |
| "metric": "Denoised inference time (s)", | |
| "run_group": "Data imputation" | |
| } | |
| }, | |
| { | |
| "value": "# eval", | |
| "description": "Scenario from [Mei et al. (2021)](https://ieeexplore.ieee.org/document/9458712/) that tests the ability to impute missing entities in a data table.\n\n# eval: Number of evaluation instances.", | |
| "markdown": false, | |
| "metadata": { | |
| "metric": "# eval", | |
| "run_group": "Data imputation" | |
| } | |
| }, | |
| { | |
| "value": "# train", | |
| "description": "Scenario from [Mei et al. (2021)](https://ieeexplore.ieee.org/document/9458712/) that tests the ability to impute missing entities in a data table.\n\n# train: Number of training instances (e.g., in-context examples).", | |
| "markdown": false, | |
| "metadata": { | |
| "metric": "# train", | |
| "run_group": "Data imputation" | |
| } | |
| }, | |
| { | |
| "value": "truncated", | |
| "description": "Scenario from [Mei et al. (2021)](https://ieeexplore.ieee.org/document/9458712/) that tests the ability to impute missing entities in a data table.\n\ntruncated: Fraction of instances where the prompt itself was truncated (implies that there were no in-context examples).", | |
| "markdown": false, | |
| "metadata": { | |
| "metric": "truncated", | |
| "run_group": "Data imputation" | |
| } | |
| }, | |
| { | |
| "value": "# prompt tokens", | |
| "description": "Scenario from [Mei et al. (2021)](https://ieeexplore.ieee.org/document/9458712/) that tests the ability to impute missing entities in a data table.\n\n# prompt tokens: Number of tokens in the prompt.", | |
| "markdown": false, | |
| "metadata": { | |
| "metric": "# prompt tokens", | |
| "run_group": "Data imputation" | |
| } | |
| }, | |
| { | |
| "value": "# output tokens", | |
| "description": "Scenario from [Mei et al. (2021)](https://ieeexplore.ieee.org/document/9458712/) that tests the ability to impute missing entities in a data table.\n\n# output tokens: Actual number of output tokens.", | |
| "markdown": false, | |
| "metadata": { | |
| "metric": "# output tokens", | |
| "run_group": "Data imputation" | |
| } | |
| }, | |
| { | |
| "value": "# trials", | |
| "description": "Scenario from [Mei et al. (2021)](https://ieeexplore.ieee.org/document/9458712/) that tests the ability to impute missing entities in a data table.\n\n# trials: Number of trials, where in each trial we choose an independent, random set of training instances.", | |
| "markdown": false, | |
| "metadata": { | |
| "metric": "# trials", | |
| "run_group": "Data imputation" | |
| } | |
| } | |
| ], | |
| "rows": [ | |
| [ | |
| { | |
| "value": "EleutherAI/pythia-2.8b", | |
| "description": "", | |
| "href": "?group=entity_data_imputation&subgroup=dataset%3A%20Restaurant&runSpecs=%5B%22entity_data_imputation%3Adataset%3DRestaurant%2Cmodel%3DEleutherAI_pythia-2.8b%22%5D", | |
| "markdown": false, | |
| "run_spec_names": [ | |
| "entity_data_imputation:dataset=Restaurant,model=EleutherAI_pythia-2.8b" | |
| ] | |
| }, | |
| { | |
| "value": 0.03488372093023256, | |
| "description": "min=0.035, mean=0.035, max=0.035, sum=0.035 (1)", | |
| "style": { | |
| "font-weight": "bold" | |
| }, | |
| "markdown": false | |
| }, | |
| { | |
| "description": "1 matching runs, but no matching metrics", | |
| "markdown": false | |
| }, | |
| { | |
| "value": 86.0, | |
| "description": "min=86, mean=86, max=86, sum=86 (1)", | |
| "style": {}, | |
| "markdown": false | |
| }, | |
| { | |
| "value": 5.0, | |
| "description": "min=5, mean=5, max=5, sum=5 (1)", | |
| "style": {}, | |
| "markdown": false | |
| }, | |
| { | |
| "value": 0.0, | |
| "description": "min=0, mean=0, max=0, sum=0 (1)", | |
| "style": {}, | |
| "markdown": false | |
| }, | |
| { | |
| "value": 256.04651162790697, | |
| "description": "min=256.047, mean=256.047, max=256.047, sum=256.047 (1)", | |
| "style": {}, | |
| "markdown": false | |
| }, | |
| { | |
| "value": 3.755813953488372, | |
| "description": "min=3.756, mean=3.756, max=3.756, sum=3.756 (1)", | |
| "style": {}, | |
| "markdown": false | |
| }, | |
| { | |
| "value": 1.0, | |
| "description": "min=1, mean=1, max=1, sum=1 (1)", | |
| "style": {}, | |
| "markdown": false | |
| } | |
| ] | |
| ], | |
| "links": [ | |
| { | |
| "text": "LaTeX", | |
| "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/entity_data_imputation_entity_data_imputation_dataset:Restaurant.tex" | |
| }, | |
| { | |
| "text": "JSON", | |
| "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/entity_data_imputation_entity_data_imputation_dataset:Restaurant.json" | |
| } | |
| ], | |
| "name": "entity_data_imputation_dataset:Restaurant" | |
| } | |
| ] |