[ { "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": "RAFT - EM", "description": "The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text classification tasks [(Alex et al., 2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html).\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": "RAFT" } } ], "rows": [ [ { "value": "EleutherAI/pythia-2.8b", "description": "", "markdown": false }, { "markdown": false }, { "value": 0.41590909090909095, "description": "min=0.025, mean=0.416, max=0.975, sum=4.575 (11)", "style": { "font-weight": "bold" }, "markdown": false, "run_spec_names": [ "raft:subset=ade_corpus_v2,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=banking_77,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=neurips_impact_statement_risks,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=one_stop_english,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=overruling,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=semiconductor_org_types,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=systematic_review_inclusion,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tai_safety_research,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=terms_of_service,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tweet_eval_hate,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=twitter_complaints,model=EleutherAI_pythia-2.8b,data_augmentation=canonical" ] } ] ], "links": [ { "text": "LaTeX", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/miscellaneous_text_classification_accuracy.tex" }, { "text": "JSON", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/miscellaneous_text_classification_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": "RAFT - ECE (10-bin)", "description": "The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text classification tasks [(Alex et al., 2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html).\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": "RAFT" } } ], "rows": [ [ { "value": "EleutherAI/pythia-2.8b", "description": "", "markdown": false }, { "markdown": false }, { "value": 0.2537619778871928, "description": "min=0.098, mean=0.254, max=0.427, sum=2.791 (11)", "style": { "font-weight": "bold" }, "markdown": false, "run_spec_names": [ "raft:subset=ade_corpus_v2,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=banking_77,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=neurips_impact_statement_risks,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=one_stop_english,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=overruling,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=semiconductor_org_types,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=systematic_review_inclusion,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tai_safety_research,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=terms_of_service,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tweet_eval_hate,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=twitter_complaints,model=EleutherAI_pythia-2.8b,data_augmentation=canonical" ] } ] ], "links": [ { "text": "LaTeX", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/miscellaneous_text_classification_calibration.tex" }, { "text": "JSON", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/miscellaneous_text_classification_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": "RAFT - EM (Robustness)", "description": "The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text classification tasks [(Alex et al., 2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html).\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": "RAFT", "perturbation": "Robustness" } } ], "rows": [ [ { "value": "EleutherAI/pythia-2.8b", "description": "", "markdown": false }, { "markdown": false }, { "value": 0.3568181818181818, "description": "min=0, mean=0.357, max=0.975, sum=3.925 (11)", "style": { "font-weight": "bold" }, "markdown": false, "run_spec_names": [ "raft:subset=ade_corpus_v2,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=banking_77,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=neurips_impact_statement_risks,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=one_stop_english,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=overruling,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=semiconductor_org_types,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=systematic_review_inclusion,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tai_safety_research,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=terms_of_service,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tweet_eval_hate,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=twitter_complaints,model=EleutherAI_pythia-2.8b,data_augmentation=canonical" ] } ] ], "links": [ { "text": "LaTeX", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/miscellaneous_text_classification_robustness.tex" }, { "text": "JSON", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/miscellaneous_text_classification_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": "RAFT - EM (Fairness)", "description": "The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text classification tasks [(Alex et al., 2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html).\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": "RAFT", "perturbation": "Fairness" } } ], "rows": [ [ { "value": "EleutherAI/pythia-2.8b", "description": "", "markdown": false }, { "markdown": false }, { "value": 0.38181818181818183, "description": "min=0, mean=0.382, max=0.975, sum=4.2 (11)", "style": { "font-weight": "bold" }, "markdown": false, "run_spec_names": [ "raft:subset=ade_corpus_v2,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=banking_77,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=neurips_impact_statement_risks,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=one_stop_english,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=overruling,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=semiconductor_org_types,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=systematic_review_inclusion,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tai_safety_research,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=terms_of_service,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tweet_eval_hate,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=twitter_complaints,model=EleutherAI_pythia-2.8b,data_augmentation=canonical" ] } ] ], "links": [ { "text": "LaTeX", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/miscellaneous_text_classification_fairness.tex" }, { "text": "JSON", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/miscellaneous_text_classification_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": "RAFT - Stereotypes (race)", "description": "The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text classification tasks [(Alex et al., 2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html).\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": "RAFT" } }, { "value": "RAFT - Stereotypes (gender)", "description": "The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text classification tasks [(Alex et al., 2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html).\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": "RAFT" } }, { "value": "RAFT - Representation (race)", "description": "The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text classification tasks [(Alex et al., 2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html).\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": "RAFT" } }, { "value": "RAFT - Representation (gender)", "description": "The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text classification tasks [(Alex et al., 2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html).\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": "RAFT" } } ], "rows": [ [ { "value": "EleutherAI/pythia-2.8b", "description": "", "markdown": false }, { "markdown": false }, { "description": "(0)", "style": {}, "markdown": false, "run_spec_names": [ "raft:subset=ade_corpus_v2,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=banking_77,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=neurips_impact_statement_risks,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=one_stop_english,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=overruling,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=semiconductor_org_types,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=systematic_review_inclusion,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tai_safety_research,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=terms_of_service,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tweet_eval_hate,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=twitter_complaints,model=EleutherAI_pythia-2.8b,data_augmentation=canonical" ] }, { "description": "(0)", "style": {}, "markdown": false, "run_spec_names": [ "raft:subset=ade_corpus_v2,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=banking_77,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=neurips_impact_statement_risks,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=one_stop_english,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=overruling,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=semiconductor_org_types,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=systematic_review_inclusion,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tai_safety_research,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=terms_of_service,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tweet_eval_hate,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=twitter_complaints,model=EleutherAI_pythia-2.8b,data_augmentation=canonical" ] }, { "description": "(0)", "style": {}, "markdown": false, "run_spec_names": [ "raft:subset=ade_corpus_v2,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=banking_77,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=neurips_impact_statement_risks,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=one_stop_english,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=overruling,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=semiconductor_org_types,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=systematic_review_inclusion,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tai_safety_research,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=terms_of_service,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tweet_eval_hate,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=twitter_complaints,model=EleutherAI_pythia-2.8b,data_augmentation=canonical" ] }, { "value": 0.0, "description": "min=0, mean=0, max=0, sum=0 (1)", "style": { "font-weight": "bold" }, "markdown": false, "run_spec_names": [ "raft:subset=ade_corpus_v2,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=banking_77,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=neurips_impact_statement_risks,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=one_stop_english,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=overruling,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=semiconductor_org_types,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=systematic_review_inclusion,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tai_safety_research,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=terms_of_service,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tweet_eval_hate,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=twitter_complaints,model=EleutherAI_pythia-2.8b,data_augmentation=canonical" ] } ] ], "links": [ { "text": "LaTeX", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/miscellaneous_text_classification_bias.tex" }, { "text": "JSON", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/miscellaneous_text_classification_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": "RAFT - Toxic fraction", "description": "The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text classification tasks [(Alex et al., 2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html).\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": "RAFT" } } ], "rows": [ [ { "value": "EleutherAI/pythia-2.8b", "description": "", "markdown": false }, { "markdown": false }, { "description": "11 matching runs, but no matching metrics", "markdown": false } ] ], "links": [ { "text": "LaTeX", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/miscellaneous_text_classification_toxicity.tex" }, { "text": "JSON", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/miscellaneous_text_classification_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": "RAFT - Denoised inference time (s)", "description": "The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text classification tasks [(Alex et al., 2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html).\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": "RAFT" } } ], "rows": [ [ { "value": "EleutherAI/pythia-2.8b", "description": "", "markdown": false }, { "markdown": false }, { "description": "11 matching runs, but no matching metrics", "markdown": false } ] ], "links": [ { "text": "LaTeX", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/miscellaneous_text_classification_efficiency.tex" }, { "text": "JSON", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/miscellaneous_text_classification_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": "RAFT - # eval", "description": "The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text classification tasks [(Alex et al., 2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html).\n\n# eval: Number of evaluation instances.", "markdown": false, "metadata": { "metric": "# eval", "run_group": "RAFT" } }, { "value": "RAFT - # train", "description": "The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text classification tasks [(Alex et al., 2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html).\n\n# train: Number of training instances (e.g., in-context examples).", "markdown": false, "metadata": { "metric": "# train", "run_group": "RAFT" } }, { "value": "RAFT - truncated", "description": "The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text classification tasks [(Alex et al., 2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html).\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": "RAFT" } }, { "value": "RAFT - # prompt tokens", "description": "The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text classification tasks [(Alex et al., 2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html).\n\n# prompt tokens: Number of tokens in the prompt.", "markdown": false, "metadata": { "metric": "# prompt tokens", "run_group": "RAFT" } }, { "value": "RAFT - # output tokens", "description": "The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text classification tasks [(Alex et al., 2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html).\n\n# output tokens: Actual number of output tokens.", "markdown": false, "metadata": { "metric": "# output tokens", "run_group": "RAFT" } }, { "value": "RAFT - # trials", "description": "The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text classification tasks [(Alex et al., 2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html).\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": "RAFT" } } ], "rows": [ [ { "value": "EleutherAI/pythia-2.8b", "description": "", "markdown": false }, { "markdown": false }, { "value": 40.0, "description": "min=40, mean=40, max=40, sum=440 (11)", "style": {}, "markdown": false, "run_spec_names": [ "raft:subset=ade_corpus_v2,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=banking_77,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=neurips_impact_statement_risks,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=one_stop_english,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=overruling,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=semiconductor_org_types,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=systematic_review_inclusion,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tai_safety_research,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=terms_of_service,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tweet_eval_hate,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=twitter_complaints,model=EleutherAI_pythia-2.8b,data_augmentation=canonical" ] }, { "value": 4.6045454545454545, "description": "min=0.7, mean=4.605, max=5, sum=50.65 (11)", "style": {}, "markdown": false, "run_spec_names": [ "raft:subset=ade_corpus_v2,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=banking_77,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=neurips_impact_statement_risks,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=one_stop_english,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=overruling,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=semiconductor_org_types,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=systematic_review_inclusion,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tai_safety_research,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=terms_of_service,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tweet_eval_hate,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=twitter_complaints,model=EleutherAI_pythia-2.8b,data_augmentation=canonical" ] }, { "value": 0.0, "description": "min=0, mean=0, max=0, sum=0 (11)", "style": {}, "markdown": false, "run_spec_names": [ "raft:subset=ade_corpus_v2,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=banking_77,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=neurips_impact_statement_risks,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=one_stop_english,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=overruling,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=semiconductor_org_types,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=systematic_review_inclusion,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tai_safety_research,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=terms_of_service,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tweet_eval_hate,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=twitter_complaints,model=EleutherAI_pythia-2.8b,data_augmentation=canonical" ] }, { "value": 869.6909090909089, "description": "min=280.35, mean=869.691, max=1756.575, sum=9566.6 (11)", "style": {}, "markdown": false, "run_spec_names": [ "raft:subset=ade_corpus_v2,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=banking_77,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=neurips_impact_statement_risks,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=one_stop_english,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=overruling,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=semiconductor_org_types,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=systematic_review_inclusion,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tai_safety_research,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=terms_of_service,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tweet_eval_hate,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=twitter_complaints,model=EleutherAI_pythia-2.8b,data_augmentation=canonical" ] }, { "value": 4.661363636363637, "description": "min=1.1, mean=4.661, max=18.65, sum=51.275 (11)", "style": {}, "markdown": false, "run_spec_names": [ "raft:subset=ade_corpus_v2,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=banking_77,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=neurips_impact_statement_risks,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=one_stop_english,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=overruling,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=semiconductor_org_types,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=systematic_review_inclusion,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tai_safety_research,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=terms_of_service,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tweet_eval_hate,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=twitter_complaints,model=EleutherAI_pythia-2.8b,data_augmentation=canonical" ] }, { "value": 1.0, "description": "min=1, mean=1, max=1, sum=11 (11)", "style": {}, "markdown": false, "run_spec_names": [ "raft:subset=ade_corpus_v2,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=banking_77,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=neurips_impact_statement_risks,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=one_stop_english,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=overruling,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=semiconductor_org_types,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=systematic_review_inclusion,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tai_safety_research,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=terms_of_service,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=tweet_eval_hate,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "raft:subset=twitter_complaints,model=EleutherAI_pythia-2.8b,data_augmentation=canonical" ] } ] ], "links": [ { "text": "LaTeX", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/miscellaneous_text_classification_general_information.tex" }, { "text": "JSON", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/miscellaneous_text_classification_general_information.json" } ], "name": "general_information" } ]