yuhengtu's picture
Upload folder using huggingface_hub
f0e8b2a verified
[
{
"title": "Accuracy",
"header": [
{
"value": "Model",
"markdown": false,
"metadata": {}
},
{
"value": "Mean win rate",
"description": "How many models this model outperforms on average (over columns).",
"markdown": false,
"lower_is_better": false,
"metadata": {}
},
{
"value": "IMDB - EM",
"description": "The IMDB benchmark for sentiment analysis in movie review [(Maas et al., 2011)](https://aclanthology.org/P11-1015/).\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": "IMDB"
}
}
],
"rows": [
[
{
"value": "EleutherAI/pythia-2.8b",
"description": "",
"markdown": false
},
{
"markdown": false
},
{
"value": 0.466,
"description": "min=0.466, mean=0.466, max=0.466, sum=0.466 (1)",
"style": {
"font-weight": "bold"
},
"markdown": false,
"run_spec_names": [
"imdb:model=EleutherAI_pythia-2.8b,data_augmentation=canonical"
]
}
]
],
"links": [
{
"text": "LaTeX",
"href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/sentiment_analysis_accuracy.tex"
},
{
"text": "JSON",
"href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/sentiment_analysis_accuracy.json"
}
],
"name": "accuracy"
},
{
"title": "Calibration",
"header": [
{
"value": "Model",
"markdown": false,
"metadata": {}
},
{
"value": "Mean win rate",
"description": "How many models this model outperforms on average (over columns).",
"markdown": false,
"lower_is_better": false,
"metadata": {}
},
{
"value": "IMDB - ECE (10-bin)",
"description": "The IMDB benchmark for sentiment analysis in movie review [(Maas et al., 2011)](https://aclanthology.org/P11-1015/).\n\n10-bin expected calibration error: The average difference between the model's confidence and accuracy, averaged across 10 bins where each bin contains an equal number of points (only computed for classification tasks). Warning - not reliable for small datasets (e.g., with < 300 examples) because each bin will have very few examples.",
"markdown": false,
"lower_is_better": true,
"metadata": {
"metric": "ECE (10-bin)",
"run_group": "IMDB"
}
}
],
"rows": [
[
{
"value": "EleutherAI/pythia-2.8b",
"description": "",
"markdown": false
},
{
"markdown": false
},
{
"value": 0.14353804678840293,
"description": "min=0.144, mean=0.144, max=0.144, sum=0.144 (1)",
"style": {
"font-weight": "bold"
},
"markdown": false,
"run_spec_names": [
"imdb:model=EleutherAI_pythia-2.8b,data_augmentation=canonical"
]
}
]
],
"links": [
{
"text": "LaTeX",
"href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/sentiment_analysis_calibration.tex"
},
{
"text": "JSON",
"href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/sentiment_analysis_calibration.json"
}
],
"name": "calibration"
},
{
"title": "Robustness",
"header": [
{
"value": "Model",
"markdown": false,
"metadata": {}
},
{
"value": "Mean win rate",
"description": "How many models this model outperforms on average (over columns).",
"markdown": false,
"lower_is_better": false,
"metadata": {}
},
{
"value": "IMDB - EM (Robustness)",
"description": "The IMDB benchmark for sentiment analysis in movie review [(Maas et al., 2011)](https://aclanthology.org/P11-1015/).\n\nQuasi-exact match: Fraction of instances that the predicted output matches a correct reference up to light processing.\n- Perturbation Robustness: Computes worst case over different robustness perturbations (misspellings, formatting, contrast sets).",
"markdown": false,
"lower_is_better": false,
"metadata": {
"metric": "EM",
"run_group": "IMDB",
"perturbation": "Robustness"
}
}
],
"rows": [
[
{
"value": "EleutherAI/pythia-2.8b",
"description": "",
"markdown": false
},
{
"markdown": false
},
{
"value": 0.438,
"description": "min=0.438, mean=0.438, max=0.438, sum=0.438 (1)",
"style": {
"font-weight": "bold"
},
"markdown": false,
"run_spec_names": [
"imdb:model=EleutherAI_pythia-2.8b,data_augmentation=canonical"
]
}
]
],
"links": [
{
"text": "LaTeX",
"href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/sentiment_analysis_robustness.tex"
},
{
"text": "JSON",
"href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/sentiment_analysis_robustness.json"
}
],
"name": "robustness"
},
{
"title": "Fairness",
"header": [
{
"value": "Model",
"markdown": false,
"metadata": {}
},
{
"value": "Mean win rate",
"description": "How many models this model outperforms on average (over columns).",
"markdown": false,
"lower_is_better": false,
"metadata": {}
},
{
"value": "IMDB - EM (Fairness)",
"description": "The IMDB benchmark for sentiment analysis in movie review [(Maas et al., 2011)](https://aclanthology.org/P11-1015/).\n\nQuasi-exact match: Fraction of instances that the predicted output matches a correct reference up to light processing.\n- Perturbation Fairness: Computes worst case over different fairness perturbations (changing dialect, race of names, gender).",
"markdown": false,
"lower_is_better": false,
"metadata": {
"metric": "EM",
"run_group": "IMDB",
"perturbation": "Fairness"
}
}
],
"rows": [
[
{
"value": "EleutherAI/pythia-2.8b",
"description": "",
"markdown": false
},
{
"markdown": false
},
{
"value": 0.451,
"description": "min=0.451, mean=0.451, max=0.451, sum=0.451 (1)",
"style": {
"font-weight": "bold"
},
"markdown": false,
"run_spec_names": [
"imdb:model=EleutherAI_pythia-2.8b,data_augmentation=canonical"
]
}
]
],
"links": [
{
"text": "LaTeX",
"href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/sentiment_analysis_fairness.tex"
},
{
"text": "JSON",
"href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/sentiment_analysis_fairness.json"
}
],
"name": "fairness"
},
{
"title": "Bias",
"header": [
{
"value": "Model",
"markdown": false,
"metadata": {}
},
{
"value": "Mean win rate",
"description": "How many models this model outperforms on average (over columns).",
"markdown": false,
"lower_is_better": false,
"metadata": {}
},
{
"value": "IMDB - Stereotypes (race)",
"description": "The IMDB benchmark for sentiment analysis in movie review [(Maas et al., 2011)](https://aclanthology.org/P11-1015/).\n\nStereotypical associations (race, profession): Measures uneven association of racial groups (Asian, Hispanic, White) with target professions. This measurement is based on cooccurence statistics between the racially-associated names (word list from [Garg et al., 2018](https://www.pnas.org/doi/10.1073/pnas.1720347115); race associations based on US Census statistics) and the target professions (word list from [Bolukbasi et al., 2016](https://papers.nips.cc/paper/2016/hash/a486cd07e4ac3d270571622f4f316ec5-Abstract.html)).",
"markdown": false,
"lower_is_better": true,
"metadata": {
"metric": "Stereotypes (race)",
"run_group": "IMDB"
}
},
{
"value": "IMDB - Stereotypes (gender)",
"description": "The IMDB benchmark for sentiment analysis in movie review [(Maas et al., 2011)](https://aclanthology.org/P11-1015/).\n\nStereotypical associations (gender, profession): Measures uneven association of gender groups (male, female) with target professions. This measurement is based on cooccurence statistics between the gender terms (word list from [Bolukbasi et al., 2016](https://papers.nips.cc/paper/2016/hash/a486cd07e4ac3d270571622f4f316ec5-Abstract.html)) and the target professions (word list from [Bolukbasi et al., 2016](https://papers.nips.cc/paper/2016/hash/a486cd07e4ac3d270571622f4f316ec5-Abstract.html)).",
"markdown": false,
"lower_is_better": true,
"metadata": {
"metric": "Stereotypes (gender)",
"run_group": "IMDB"
}
},
{
"value": "IMDB - Representation (race)",
"description": "The IMDB benchmark for sentiment analysis in movie review [(Maas et al., 2011)](https://aclanthology.org/P11-1015/).\n\nDemographic representation (race): Measures uneven representation of racial groups (Asian, Hispanic, White). This measurement is based on disparities in the frequency statistics across racially-associated names (word list from [Garg et al., 2018](https://www.pnas.org/doi/10.1073/pnas.1720347115); race associations based on US Census statistics).",
"markdown": false,
"lower_is_better": true,
"metadata": {
"metric": "Representation (race)",
"run_group": "IMDB"
}
},
{
"value": "IMDB - Representation (gender)",
"description": "The IMDB benchmark for sentiment analysis in movie review [(Maas et al., 2011)](https://aclanthology.org/P11-1015/).\n\nDemographic representation (gender): Measures uneven representation of gender groups (male, female). This measurement is based on disparities in the frequency statistics across gender terms (word list from [Bolukbasi et al., 2016](https://papers.nips.cc/paper/2016/hash/a486cd07e4ac3d270571622f4f316ec5-Abstract.html)).",
"markdown": false,
"lower_is_better": true,
"metadata": {
"metric": "Representation (gender)",
"run_group": "IMDB"
}
}
],
"rows": [
[
{
"value": "EleutherAI/pythia-2.8b",
"description": "",
"markdown": false
},
{
"markdown": false
},
{
"description": "1 matching runs, but no matching metrics",
"markdown": false
},
{
"description": "1 matching runs, but no matching metrics",
"markdown": false
},
{
"description": "1 matching runs, but no matching metrics",
"markdown": false
},
{
"description": "1 matching runs, but no matching metrics",
"markdown": false
}
]
],
"links": [
{
"text": "LaTeX",
"href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/sentiment_analysis_bias.tex"
},
{
"text": "JSON",
"href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/sentiment_analysis_bias.json"
}
],
"name": "bias"
},
{
"title": "Toxicity",
"header": [
{
"value": "Model",
"markdown": false,
"metadata": {}
},
{
"value": "Mean win rate",
"description": "How many models this model outperforms on average (over columns).",
"markdown": false,
"lower_is_better": false,
"metadata": {}
},
{
"value": "IMDB - Toxic fraction",
"description": "The IMDB benchmark for sentiment analysis in movie review [(Maas et al., 2011)](https://aclanthology.org/P11-1015/).\n\nToxic fraction: Fraction of model outputs that are toxic (based on the PerspectiveAPI toxicity classifier).",
"markdown": false,
"lower_is_better": true,
"metadata": {
"metric": "Toxic fraction",
"run_group": "IMDB"
}
}
],
"rows": [
[
{
"value": "EleutherAI/pythia-2.8b",
"description": "",
"markdown": false
},
{
"markdown": false
},
{
"description": "1 matching runs, but no matching metrics",
"markdown": false
}
]
],
"links": [
{
"text": "LaTeX",
"href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/sentiment_analysis_toxicity.tex"
},
{
"text": "JSON",
"href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/sentiment_analysis_toxicity.json"
}
],
"name": "toxicity"
},
{
"title": "Efficiency",
"header": [
{
"value": "Model",
"markdown": false,
"metadata": {}
},
{
"value": "Mean win rate",
"description": "How many models this model outperforms on average (over columns).",
"markdown": false,
"lower_is_better": false,
"metadata": {}
},
{
"value": "IMDB - Denoised inference time (s)",
"description": "The IMDB benchmark for sentiment analysis in movie review [(Maas et al., 2011)](https://aclanthology.org/P11-1015/).\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": "IMDB"
}
}
],
"rows": [
[
{
"value": "EleutherAI/pythia-2.8b",
"description": "",
"markdown": false
},
{
"markdown": false
},
{
"description": "1 matching runs, but no matching metrics",
"markdown": false
}
]
],
"links": [
{
"text": "LaTeX",
"href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/sentiment_analysis_efficiency.tex"
},
{
"text": "JSON",
"href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/sentiment_analysis_efficiency.json"
}
],
"name": "efficiency"
},
{
"title": "General information",
"header": [
{
"value": "Model",
"markdown": false,
"metadata": {}
},
{
"value": "Mean win rate",
"description": "How many models this model outperforms on average (over columns).",
"markdown": false,
"lower_is_better": false,
"metadata": {}
},
{
"value": "IMDB - # eval",
"description": "The IMDB benchmark for sentiment analysis in movie review [(Maas et al., 2011)](https://aclanthology.org/P11-1015/).\n\n# eval: Number of evaluation instances.",
"markdown": false,
"metadata": {
"metric": "# eval",
"run_group": "IMDB"
}
},
{
"value": "IMDB - # train",
"description": "The IMDB benchmark for sentiment analysis in movie review [(Maas et al., 2011)](https://aclanthology.org/P11-1015/).\n\n# train: Number of training instances (e.g., in-context examples).",
"markdown": false,
"metadata": {
"metric": "# train",
"run_group": "IMDB"
}
},
{
"value": "IMDB - truncated",
"description": "The IMDB benchmark for sentiment analysis in movie review [(Maas et al., 2011)](https://aclanthology.org/P11-1015/).\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": "IMDB"
}
},
{
"value": "IMDB - # prompt tokens",
"description": "The IMDB benchmark for sentiment analysis in movie review [(Maas et al., 2011)](https://aclanthology.org/P11-1015/).\n\n# prompt tokens: Number of tokens in the prompt.",
"markdown": false,
"metadata": {
"metric": "# prompt tokens",
"run_group": "IMDB"
}
},
{
"value": "IMDB - # output tokens",
"description": "The IMDB benchmark for sentiment analysis in movie review [(Maas et al., 2011)](https://aclanthology.org/P11-1015/).\n\n# output tokens: Actual number of output tokens.",
"markdown": false,
"metadata": {
"metric": "# output tokens",
"run_group": "IMDB"
}
},
{
"value": "IMDB - # trials",
"description": "The IMDB benchmark for sentiment analysis in movie review [(Maas et al., 2011)](https://aclanthology.org/P11-1015/).\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": "IMDB"
}
}
],
"rows": [
[
{
"value": "EleutherAI/pythia-2.8b",
"description": "",
"markdown": false
},
{
"markdown": false
},
{
"value": 1000.0,
"description": "min=1000, mean=1000, max=1000, sum=1000 (1)",
"style": {},
"markdown": false,
"run_spec_names": [
"imdb:model=EleutherAI_pythia-2.8b,data_augmentation=canonical"
]
},
{
"value": 2.911,
"description": "min=2.911, mean=2.911, max=2.911, sum=2.911 (1)",
"style": {},
"markdown": false,
"run_spec_names": [
"imdb:model=EleutherAI_pythia-2.8b,data_augmentation=canonical"
]
},
{
"value": 0.0,
"description": "min=0, mean=0, max=0, sum=0 (1)",
"style": {},
"markdown": false,
"run_spec_names": [
"imdb:model=EleutherAI_pythia-2.8b,data_augmentation=canonical"
]
},
{
"value": 1619.568,
"description": "min=1619.568, mean=1619.568, max=1619.568, sum=1619.568 (1)",
"style": {},
"markdown": false,
"run_spec_names": [
"imdb:model=EleutherAI_pythia-2.8b,data_augmentation=canonical"
]
},
{
"value": 1.169,
"description": "min=1.169, mean=1.169, max=1.169, sum=1.169 (1)",
"style": {},
"markdown": false,
"run_spec_names": [
"imdb:model=EleutherAI_pythia-2.8b,data_augmentation=canonical"
]
},
{
"value": 1.0,
"description": "min=1, mean=1, max=1, sum=1 (1)",
"style": {},
"markdown": false,
"run_spec_names": [
"imdb:model=EleutherAI_pythia-2.8b,data_augmentation=canonical"
]
}
]
],
"links": [
{
"text": "LaTeX",
"href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/sentiment_analysis_general_information.tex"
},
{
"text": "JSON",
"href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/sentiment_analysis_general_information.json"
}
],
"name": "general_information"
}
]